THE IMPACT OF HOME WORKING ON EMPLOYEE MOTIVATION. FOCUS ON THE UK CONSTRUCTION INDUSTRY.

Among the various contemporary concepts related to the innovative approaches of business management, the utilisation of business intelligence is considered a necessary norm in the existing business environments (Cohen, 2017). Business intelligence refers to the notion of the collective information and data related to the existing paradigms of the respective business markets, the consumer trends, the related competition data and all relative business information required for optimum functioning of the businesses in the globalised environment of today (Rouhani & Lecic, 2018). This study will be focused towards the assessment of the requirement and significance of business information for the development of optimum project management approaches and functions.

With the advancements of modern technology and the increase in the corresponding utilisation in various domains of business management, the paradigms of the codes of conduct in this respect have been altered drastically (Rouhani & Lecic, 2018). This includes the introduction of numerous contemporary approaches for the management of the various business functions. Moreover, the globalised integration of the informatics and data related to the development of the associated paradigms through the means of the internet has further changed the relative approaches of information management required for the optimum functioning of the various operations reflecting towards viable and profitable business functions (Elbashir et al., 2008; Trieu, 2017).

Business intelligence in the existing paradigms of today’s modern world includes not only the information associated with the organisational statistics and information- rather, the business intelligence in the modern world of today entails the various unconventional data collection and processing approaches (Cohen, 2017). This includes features such as big data analysis and the use of the internet of things for developing patterns and trends for optimised business functionalities. Moreover, the introduction of various contemporary fields of operations- such as game theory analysis and decentralised economies- have resulted in further complicated systems of managing the various business functions (Williams & Williams, 2010). However, the development of optimum approaches resulting in the acquirement and processing of the information intrinsically related to the business operations tends to reflect towards the development of optimum approaches for the rendering of effective and profitable business functions (Williams & Williams, 2010; Turban et al., 2013).

Business intelligence is currently being applied in numerous contexts of business management. This includes the domains of sales management, inventory management, warehouse management, systems management, operations management, supply chain management, customer relationships management and production management, including numerous other related business management domains (Rouhani & Lecic, 2018). The major challenge in the paradigms of the current bombarded information systems is to assess the necessary information to develop patterns and protocols for using the respective information for the augmentation of the corresponding business functions and operations. However, there are numerous existing businesses and projects which tend to operate on the conventional approaches of business management- uninformed of the various advantages and benefits of the effective utilisation of business intelligence for the optimisation of the respective operative paradigms (Williams & Williams, 2010; Trieu, 2017; Rouhani & Lecic, 2018). Hence, this study will be pertained towards the notion of the impact of business intelligence on the project management paradigms.

This study will aim to “assess the impacts of business intelligence on project management”. In order to effectively attain the relative aim of this study, the following objectives have been defined:

· To evaluate the various factors associated with the business intelligence and its optimum utilisation

· To appraise the approaches of business intelligence in the augmentation of the management of business projects

· To assess the influence of business intelligence on the development of optimum functioning of project management

· What are the impacts of business intelligence on management of projects?

· What are the various approaches used for utilisation of business intelligence in management of projects?

· What are the factors influencing effective utilisation of business intelligence for management of projects?

As this study is associated with the analysis of the impacts of business intelligence on the management of projects, the primary focus of this study will be pertained towards the evaluation of the respective impacts. Although the study will include the various aspects related to the assessment of the impacts, the study is limited through the various constraints related to the study limitations of student academic projects. Moreover, the inclusion of the limited variables for the study analysis also present the relative limitations of this research study.

Furthermore, this study is limited by the availability of the various literature being included in this study. Being a student academic researcher, the study is also constrained by the availability of related literature on the freely available websites and data bases- in addition to the limited databases provided by the university. In addition, the selection of the research methodology also reflects towards the various limitations related to this particular research study. Overall, the study limitations are copious, which will also be highlighted in the final section of this study.

This study has been divided into five chapter- the foremost of which is the introduction to the overall topic under research. This chapter has introduced the relative background as well as the aim and objectives to be achieved through the completion of this study. Moreover, the second chapter is related to the critical review of past literature associated with this study. This will encompass the related conceptual frameworks as well as the included terminologies and concepts required for the completion of this study.

The third chapter of this dissertation will include the relative research methodology being selected for this study. This will discuss the related research methodologies in addition to the enumeration of the exclusive study design being used for this particular study. Against the mentioned methodology for this study, the fourth chapter will enumerate the various results acquired through the effective implementation of the stated research approach. In addition, the findings of the study will be discussed against the associated secondary literature in order to augment and emphasise the various findings acquired through the implementation of the research methodology for this study. Finally, the fifth chapter of this study will conclude the various findings of this study- in addition to the highlighting of the relative recommendations against the conclusive findings of the completed study.

In recent years, the traditional domestic business intelligence field has made the project deep target and lean management modularisation as an important means to create high-quality enterprises and implement project management, and combined with the implementation of various large-scale business intelligence projects, more prominent lean management (Turban et al., 2013). Leading role in cost control and optimisation of deep target management in the field of business intelligence. The implementation of lean management in business intelligence enterprises can rationally use enterprise resources, promote profits and disadvantages, and maximise economic benefits (Chen et al., 2012; Turban et al., 2013).

In today's society, the network economy characterised by e-commerce is gradually maturing, and the emergence and widespread use of the network revolutionises our way of life (Collier, 2012). E-commerce has made the players on the arena back to the same starting line – both traditional and emerging e-commerce companies have the same opportunities in the era of network economy. Emerging .com companies are launching B2B or B2C transactions over the Internet, establishing platforms for direct sales and service between suppliers or between suppliers and customers, competing for market share from traditional companies; traditional companies are not willing to show weakness and have changed strategies while maintaining the traditional channels, it opened up the second battlefield of e-commerce (Minelli et al., 2012; Ramakrishnan et al., 2012; Kimball et al., 2015). 

 The data comes from the feedback of each application system within the respective project or organisation where the BI systems are being integrated, and data mining can discover new knowledge from the existing data warehouse and automatically correct the content in the knowledge base at any time, so the knowledge base in BI is a dynamic structure (Collier, 2012). But business intelligence also has shortcomings. The goal of business intelligence is the same as decision Support System (DSS), in order to improve the efficiency and accuracy of corporate decision-making (Minelli et al., 2012). However, BI provides valuable and decision-making information and knowledge through data analysis and knowledge discovery tools. Users must use this knowledge and knowledge to make judgments based on this knowledge and experience, make decisions, and have little ability to make intelligent decisions (Collier, 2012; Minelli et al., 2012). It does not provide functions such as solution generation, program coordination, and program evaluation, as in a specialised decision support system, and it does not have the ability to make group decisions (Minelli et al., 2012). 

An obvious feature of the enterprise management model is the need to undertake the project by means of bidding. First, the enterprise must undertake the project, and then organise the construction by project, which has typical project management characteristics, that is to say the enterprise is based on project management (IşıK et al., 2013). Combined with business intelligence, it can extend the information application to the enterprise's refined management, development strategy, resource planning and decision support; apply business intelligence, based on the information subsystem of the construction enterprise, can be the transactional data of the construction enterprise Transforming into analytical data, revealing and excavating the information and knowledge contained in the data, and playing a role in project management (Khan & Quadri, 2012; IşıK et al., 2013). The main functions of business intelligence technology in project management of construction enterprises include two aspects (IşıK et al., 2013):

1 integrating data, solving information island problems, and effectively promoting information sharing in project management;

2 extracting, processing and organically combining massive structured data and non-structure Data, access and create information and knowledge that is conducive to decision making in project management.

It includes product sales strategy, sales volume analysis, factor analysis that affects product sales, and product improvement forecasting. The sales model is established through system sales information, and the overall sales model and regional and departmental sales models are divided (Khan & Quadri, 2012). Analyse sales volume and sales strategy for sales models that produce different results, analyse and evaluate the factors affecting sales, and implement product launch and removal plans for corresponding product sales plans according to different sales environments to increase the amount of marketing. Through historical data analysis, it is also possible to establish a predictive model that increases sales (Debortoli et al., 2014). 

It is a typical example of a business intelligence data mining application that can monitor its performance goals in real time and continuously, and the business intelligence system can monitor its deviation from the planned goals (Debortoli et al., 2014). When the deviation is too large, the system will notify the corporate responsibility supervisor of the deviation status by various communication methods, such as e-mail, at the first time, thereby reducing the enterprise risk and improving the company's revenue. Specific applications include credit card analysis, fraud monitoring in industries such as banking and insurance (Debortoli et al., 2014). 

Regardless of goal management or exception management, the power behind the support comes from the facts. E, the system that maintains business operations, accumulates countless facts and knowledge in daily transactions (Chiang et al., 2012; Wixom et al., 2014). The business intelligence system combines corporate goals, exceptions, and facts to enable managers to further analyse causes or trends, query and detect relevant information. In the age of lack of information, management relies more on personal experience and intuition to manage and make decisions. In the era of knowledge economy, enterprises must implement fact management, relying on fantasy and feelings, and using business intelligence to make scientific decisions based on understanding the daily business situation of enterprises (Wixom et al., 2014). 

Customer is a key factor in the survival of an enterprise. Customer relationship management (CRM) is an important task for enterprises. Through the customer relationship management subsystem of business intelligence, companies can analyse customer spending habits and consumption trends, improve customer satisfaction, and then take corresponding countermeasures to enhance customer retention, cultivate loyal customers, and maintain good customer relationships (Debortoli et al., 2014; Larson & Chang, 2016). 

New products promote the development of enterprises, but the development and promotion of new products must be based on a certain market (Larson & Chang, 2016). Good corporate history information can be used to predict market demand trends; data warehouse technology in business intelligence systems to solve which products need to be updated, whether it needs to carry out some kind of advertising campaign; what kind of users are targeted for advertising campaigns; Most likely, the potential customers of the company, the direct advertising strategy for such customers can outweigh the untargeted gain; predict the future earnings of the project and so on (Debortoli et al., 2014). 

The risk is dangerous or damage to the project, it is a possible event, it may not happen, it may happen, and the project risk is a potential problem in the process of the event that may hinder the success of the project or may exist during the implementation of the project (Wixom et al., 2014). Team's risk itself is a possible event, and only when the conditions that trigger the risk or the conditions for the risk of conversion exist, will the risk actually arise. What is called project risk management is to control the conditions of conversion and risk triggering through various methods such as transfer, avoidance and mitigation. Risk management has certain practical significance for the scope of the project and the cost of the project scope and the actual progress of the project as well as the plan estimation (Chiang et al., 2012; Wixom et al., 2014).

The prevention of risk management is very beneficial to understanding the nature of the project, so that the employees of the company can better understand the disadvantages of the project. And the advantages, do a good job of project risk prevention, and other knowledge of the integration project management is also beneficial (Wixom et al., 2014). In addition, risk management is also a form of insurance for the project, which can effectively reduce the adverse events of the project, and risk management is also the project implementation process. In an investment, there will be a lot of costs involved, but in any case, the cost management of the project will not exceed the project revenue (Debortoli et al., 2014; Wixom et al., 2014). IT project risk management is similar to general project risk management. The main goal is to reduce the probability of potential risks, so that the project can achieve the highest possible profit while completing quality on time and quality.

While business intelligence (BI) can have a profound impact on leadership decisions, in an increasingly complex environment, enterprise development, mergers and acquisitions, and business model changes mean enterprise IT infrastructure It may also become more complex, and this complexity makes enterprises more attractive to an architecture that is scalable and flexible, because users need a new level of information integrity, integration, execution, and availability (Chiang et al., 2012; Alhyasat & Al-Dalahmeh, 2013). If the pre-implementation is not adequate, and the expectations of BI for all industries are too high, it is likely to lead to huge problems, pressures and more potential risks in the implementation of a BI project. How to minimise the risk of the BI project itself is undoubtedly a problem that every CIO must consider in the implementation of BI projects (Alhyasat & Al-Dalahmeh, 2013). To ensure maximum impact from BI, the management of BI projects needs to take into account all aspects of the business, from strategy to implementation, project management and delivery. A well-designed and executed BI strategy must be aligned with the company's business goals, and technology, data, processes, organisational structure, customer focus, management requirements, internal support, etc. should be considered together (Alhyasat & Al-Dalahmeh, 2013; Moro et al., 2015). 

The risk of inaccurate definition of BI user groups and requirements is undoubted, and the user is the centre of any BI project. But because BI systems often have different user groups, it is not an easy task to know who the end user is (Moro et al., 2015). From the ordinary employees who use daily reports to the high-end users who are engaged in data analysis on a full-time basis, each group of people has different requirements for the use of BI products. Some BI system implementers have spent a lot of time completing a certain requirement, but found that users have no interest in the content displayed by the BI system, which is not what this user needs to see most (Moro et al., 2015; Debortoli et al., 2014). There are also many companies that do not have clear goals. Even BI is only used to make unnecessary comparisons with users in the same industry, rather than based on the development needs of enterprises (Alhyasat & Al-Dalahmeh, 2013). This makes the BI project more difficult and increases the risk. The most common mistake made by technicians is to treat users as software design experts (Alhyasat & Al-Dalahmeh, 2013; Wixom et al., 2014). 

BI projects are often a wide-ranging work, from data collection, clean-up, storage, to data calculation, analysis, presentation, to information release. And monitoring and so on will be involved. Although from the perspective of the big environment, various technical means are relatively mature, for a large amount of data, with modern technology (Demirkan & Delen, 2013). In some enterprises that are preparing to build a new BI system, business leaders often generate some overly optimistic ideas. They often set goals very high, and the scope of implementation is too broad. The system above the line will immediately solve all management decision problems. This will not only affect the budget and duration of the project, but often expand the scope too much and often fail to effectively plan and utilise people and resources (Demirkan & Delen, 2013; George et al., 2015). 

The risk that the senior leadership does not pay enough attention to BI is the analysis and summary of the basic data. Logically speaking, employees are responsible for providing data, which is then analysed by the BI system. The final result is seen by the leadership (Wixom et al., 2014). However, in many practical work, this is not the case. Some senior leaders basically do not look at the reports of the BI system, or they have the following employees manually sort out the reports and show them to the leaders. In this process of reorganisation, employees who are not responsible will correct some errors, but the manual changes may be only the final result, not the basic data. This means that the data in the BI system is always wrong (Wixom et al., 2014; Moro et al., 2015). This is obviously not the result that managers want. In addition, BI projects require the company's spending from top to bottom. This requires leaders to value BI projects and give BI administrators greater power. Only in this way can we send staff from other departments to support the operation of the BI system (George et al., 2015). It is a pity that many failed projects or projects with unsatisfactory results are caused by insufficient power of the B project manager and the inability to uniformly allocate the resources of the enterprise (Wixom et al., 2014). 

In the implementation strategy and actual implementation of BI, there is a big disconnect between the current systems. The existing BI system answers many corporate questions, but the answers to these questions are usually limited to a specific environment and department (Wixom et al., 2014; George et al., 2015). On the contrary, many questions that are specific to the entire enterprise or market level are not well answered. Another limitation is that the data warehouse system focuses on the analysis of historical data, rather than directly operating the entire enterprise information value chain. Gartner defines BI networks as: a flattened network that can be interconnected for knowledge users, through which users of knowledge can share and exchange data, metadata, data/process models, analysis results, and decision information (Wixom et al., 2014). Through this network, knowledge users can make more accurate decisions faster and better. Gartner had predicted that by 2008, BI network products will become mainstream BI system products, and its network products will include collaborative work components, workflow components and enterprise application integration (EAI) software (Wixom et al., 2014; Alpar & Schulz, 2016). These software components are of great value in nature, and at the same time increase the coverage and intrinsic value of BI network products. However, current developments asserts that the technological advancements have rendered even more enhanced changes (Alpar & Schulz, 2016).

 The solution is more open, scalable, and customisable, providing a customised interface while ensuring core technology. In response to the unique needs of different enterprises, BI system makes the system personalised when it provides the same technology as the data warehouse technology in the business intelligence system, that is, adding its own code and solution based on the original solution, enhancing Customised interfaces and extended features; Provides enterprises with customised tools based on business intelligence platforms, giving the system greater flexibility and scope of use (Demirkan & Delen, 2013; Wixom et al., 2014; George et al., 2015; Sharda et al., 2016). 

From individual business intelligence to embedded business intelligence is a major trend in business intelligence applications, namely embedding business intelligence components in the enterprise's existing application systems, such as finance, manpower, sales, etc., so that the transaction processing system in the general sense has the characteristics of business intelligence (Wixom et al., 2014). Considering a component of the BI system rather than the entire BI system is not a simple matter, such as applying OLAP technology to an application system, a relatively complete business intelligence development process, such as enterprise problem analysis, solution design, and prototype system development (Wixom et al., 2014; George et al., 2015). Processes such as system applications are indispensable. Transition from traditional functions to enhanced functions. Enhanced business intelligence capabilities are business intelligence capabilities that were implemented in comparison to earlier SQL tools (Alpar & Schulz, 2016). In addition to the traditional BI system functions, most of the current BI systems have implemented the functions of the data analysis layer. Data mining and enterprise modelling are applications that BI systems should strengthen to better improve system performance (Thamir & Poulis, 2015). 

The concept of data warehouse is proposed by the book “Building Data Warehouse” published by WH Gongn in 1992 (Thamir & Poulis, 2015). The introduction of data warehouse is based on the rapid development of relational database, parallel processing and distributed technology. In order to solve the problem that information technology has a large amount of data in the development process, on the other hand, useful information is very poor solution (Wixom et al., 2014; George et al., 2015). According to the definition given by W.H. lnmon, the data warehouse is a subject-oriented, integrated, relatively stable, time-varying data set to support management decisions. The data warehouse is developed on the basis of the original relational data, but it is different from the organisational form of the database system (Alpar & Schulz, 2016). 

The data composition of a general data warehouse includes: Early detail level data, current level of detail data, lightly integrated level data, highly integrated level data. After the source data is integrated, it first enters the current level of detail, and further synthesis according to specific needs, so as to enter a light comprehensive level or even a highly integrated level, the aging data will enter the early level of detail, which shows that the data warehouse is different (Thamir & Poulis, 2015; George et al., 2015). The level of integration is generally referred to as "granularity." The larger the granularity, the lower the level of detail and the higher the level of integration. The granularity of the data warehouse is a major design issue because it affects the amount of data stored in the data warehouse and the types of queries that the data warehouse can answer (Demirkan & Delen, 2013; Alpar & Schulz, 2016). Data warehouses and databases are not the same concept. The data warehouse is a comprehensive solution, and the database is just a ready-made product. The data warehouse needs a very powerful database engine to drive. Unlike relational databases, data warehousing does not have a rigorous mathematical theoretical foundation, and it is more engineering (Thamir & Poulis, 2015). Due to the engineering nature of the data warehouse, it can be divided into four aspects: data extraction, data storage and management, data performance and technical consultation of data warehouse design according to its working process. In the field of data warehouse research, the data warehouse is a system (Alpar & Schulz, 2016).

The data warehouse is enterprise-level. It contains all the topics related to the enterprise. The data mart is departmental or process-level. It is oriented to single or several topics and is split and reorganised (Demirkan & Delen, 2013; George et al., 2015). After the simple version of the data warehouse, it takes a certain type of data separately for screening, reprinting and exploiting, generating reports, and so on. In general, a data mart is built on top of a data warehouse, meaning that its data comes from a data warehouse. But there are also data marts that are directly obtained from the data preparation area. Such data marts are called independent data marts (Thamir & Poulis, 2015). The advantages of independent data marts are: the ability to quickly build a library structure that meets the decision-making needs of a department, reducing development time and development costs, but this development approach also increases data inconsistencies and incomplete possibilities. The limitations of independent data marts are even more pronounced when decision analysis is needed across departments (Wongthongtham & Abu-Salih, 2015). In order to combine the overall advantages of enterprise data warehouse with the advantages of rapid development and rapid effectiveness of independent data marts, we need to take certain measures to overcome its limitations and realise data sharing among data marts (Thamir & Poulis, 2015). 

The determination of the theme directly affects the availability and value of the data warehouse. Generally, the analysis of the subject is determined by establishing an enterprise data model (Sharda et al., 2016). It is necessary to repeatedly communicate with relevant business analysts to establish an analysis topic. Hence, the following attributes have been defined which are necessary to be maintained and present for the accomplishment of optimum BI systems:

Among all the features of the data warehouse, integration is the most important feature. Due to the independent construction of the application data warehouse technology in the business intelligence system, the correlation and consistency with other systems are not considered in the design, so when the data enters the data warehouse, some method is used to eliminate the application problem (Thamir & Poulis, 2015; Alpar & Schulz, 2016). Many inconsistencies in the integration. Common consistency processes are: consistency of coding, consistency of units of measure, consistency of description or definition, consistency of format or type size, and so on. Data integration and transformation (ETL) is the most difficult, complex, and time consuming in data warehouse implementation (Wongthongtham & Abu-Salih, 2015). The data source of the data warehouse is mainly the application system of the enterprise. Before the data integration and conversion, the recording system must be defined. That is, it must be determined which systems or which data in the system meet the requirements. For this, it is necessary to understand all the systems. And the data in the system (Dinter et al., 2015; Alpar & Schulz, 2016). Since the construction of each system is a transaction processing service, the data organisation mode and the data representation form are different from the data organisation and expression forms used for analysis, and the forms are various (Dinter et al., 2015). In order to process the data, the rules must be determined according to the data form, and the rules are Comprehensive and accurate direct impact on the data quality of the data warehouse (Alpar & Schulz, 2016). 

In an operational system, data records are generally required to be added, deleted, and changed one by one. In the data warehouse, data operations in the operating system are not the same. In the data warehouse environment, data updates in the general sense are not performed (Dinter et al., 2015). Usually it is loaded and accessed once, that is to say, the data is loaded into the data warehouse once after being integrated. For data maintenance, new records are generally used. Although an update can be used in some cases, this is rarely possible (Wixom et al., 2014). This does not mean that the data in the data warehouse is always the same. In fact, the data in the data warehouse needs to be cleaned up, but the clean-up here is not simply to delete the data from the data warehouse, but to transfer the early data of the data warehouse to a cheaper storage device, or to retain the early details of the data. Another important aspect of cleaning up the data warehouse is the handling of sleep data and dirty data (Thamir & Poulis, 2015). Hibernate data refers to data that exists in the data warehouse and that is not currently in use and that will not be used in the future. 

Dirty data refers to those that are erroneous. Although it is said that we want to ensure the quality of the data, both types of data will inevitably exist in the data warehouse (Dinter et al., 2015; Alpar & Schulz, 2016). Identifying and processing these two types of data is one aspect of improving the performance of the data warehouse, although it is not so easy to handle. At the beginning of the data warehouse construction, this problem may not be particularly obvious, but with the operation of the data warehouse, this will become an important part of data warehouse management (Sun et al., 2015). The data maintenance strategy in the data warehouse is an important part of the data warehouse implementation. The rationality of the maintenance strategy directly affects the function and performance of the entire data warehouse (Wixom et al., 2014; Alpar & Schulz, 2016).

The operational database contains data of the "current value", the accuracy of which is valid at the time of access, and the data of the current value can be updated. The data in the data warehouse is just a series of complex snapshots generated at a certain moment. That is to say, the data warehouse stores the corresponding historical data storage technology in the business intelligence system (Alpar & Schulz, 2016). The data in the data warehouse is always time-dependent. An important part of the process of building a data warehouse model based on the enterprise model is to remove the pure operational data and add time elements (Sharda et al., 2016). The key code structure of a data warehouse always contains a certain time element. Data record triggering in the data warehouse is generally a "time-on-time" type, such as starting a data processing activity at a certain time (Alpar & Schulz, 2016). 

Granularity refers to the level of refinement or comprehensiveness of data stored in the data unit of the data warehouse. The higher the level of refinement, the smaller the granularity level, such as inventory-level data; on the contrary, the lower the degree of refinement, the larger the granularity level, such as monthly summary data or application summary data (Wixom et al., 2014). Obviously, granularity is closely related to the performance and functionality of the data warehouse. It deeply affects the amount of data stored in the data warehouse, and affects the type of query that the data warehouse can answer (Sharda et al., 2016). 

Classification mode and regression mode are the most common modes used. In addition, the above modes can be further divided into descriptive mode and predictive mode (Thamir & Poulis, 2015). Descriptive mode the results of the data before the establishment of the mode are known and can be directly used to detect the accuracy of the mode. The generation of the mode is carried out under supervision, so it is also called supervised knowledge, such as classification mode, Regression mode, time series mode, etc.; in the establishment of these modes, the predictive mode uses a part of the data as a sample, and uses another part of the data to test and correct the mode (Olszak & Ziemba, 2012; Wixom et al., 2014). Before the mode is established, the result is unknown, and the mode is generated without any supervision. Discussed below are some of the modes of data warehousing currently being used in businesses (Wixom et al., 2014).

Virtual storage mode is the data organisation form of virtual data warehouse. It does not have a dedicated data warehouse to store data. All data is stored in the source database. Only when it needs to be analysed or inquired depends on certain rules (George et al., 2015). Extract the corresponding data from the source database to temporarily compose the required data view for user analysis or query. This method is less expensive, soft and flexible, but requires high source data, and the release must conform to the data warehouse's format and organisational requirements (Moro et al., 2015; Larson & Chang, 2016). 

The storage method based on the relational table is based on the storage method of the relational table. The data is still stored in the relational model database, which causes the dimension table and the fact table to be separated between the tables, and the virtual multidimensional database is constructed according to the management of the metadata (Wixom et al., 2014). This organisation can be divided into a star model and a snowflake model. The star model is shown in Figure, in the middle is the fact table, which is the centre of the query by dimension, surrounded by the information of the dimension related to it, each dimension table has its own attributes, the dimension table and the fact table are associated by the dimension keyword (Alhyasat & Al-Dalahmeh, 2013). Regional dimension table time dimension table student fact table school category dimension table star model snowflake model is an extension of the star model, which adds a layer in the dimension table, reflecting the difference in dimensions The division of granularity makes the level of this data warehouse clearer and the logical relationship more clear, but at the same time increases the complexity of the system and reduces the versatility (Demirkan & Delen, 2013; George et al., 2015).

Classification mode is a classification function (classifier) ​​that can map data items in a data set to a given class (Wixom et al., 2014). The classification mode is often expressed as a classification tree. Searching from the root of the tree according to the value of the data, going up along the branch where the data is satisfied, and going to the leaves to determine the category (Moro et al., 2015).

The function definition of the regression mode is similar to the classification mode. The difference is that the prediction values ​​of the classification mode are discrete, and the prediction values ​​of the regression mode are continuous (George et al., 2015). If the characteristics of an animal are given, the classification model can be used to determine whether the animal is a mammal or a bird; given the education and work experience of a person, the regression model can be used to determine the extent to which the person’s annual salary is (George et al., 2015).

The time series mode predicts future values ​​based on trends in data over time. Here we have to take into account the special nature of time, like some periodic time definitions such as the week, month, season, year, etc., special days such as the impact of holidays, the calculation of the date itself, and some places that require special consideration (Thamir & Poulis, 2015). Such as the correlation before and after the time (how much influence the past has on the future). Only when fully considered there are also some areas that require special considerations such as the correlation before and after time (how much influence the past has on the future) (Wongthongtham & Abu-Salih, 2015).

The clustering mode divides the data into different groups. The difference between the groups is as large as possible, and the difference within the group is as small as possible (Wongthongtham & Abu-Salih, 2015). Unlike the classification mode, clustering is not known until it is divided into groups and groups, and it is not known which group/number of data items to define. In general, people with business knowledge should be able to understand the meaning of these groups (Wongthongtham & Abu-Salih, 2015). If the resulting model is incomprehensible or unavailable, the model may be meaningless and needs to go back to the previous stage to reorganise the data (Sun et al., 2015). 

The huge amount of data and the shortcomings of traditional data management methods have caused the phenomenon of “data congestion” (data jail) in most enterprises, which is not conducive to the management of enterprises and to the effective use of information. Therefore, how to solve the data congestion, and at the same time enable these data to fully play a role has become a hot issue in the business development of enterprises (Thamir & Poulis, 2015). The establishment of the concept of a new enterprise of “data two assets”. At present, most large and medium-sized enterprises are information-rich organisations, and the performance of an information-rich organisation depends not only on factors such as products, services or locations, but more importantly on knowledge. And knowledge from data-information is a not-simple process (Wixom et al., 2014). The essence of business intelligence is to transform data into knowledge, and to focus on knowledge discovery and mining, so that the data assets of enterprises can bring obvious economic benefits, reduce the influence of uncertain factors, and enable enterprises to obtain new competitive advantages (Sharda et al., 2016). 

E-commerce based on the business model of internet: The development and application of technologies such as e-mail, electronic data exchange, electronic payment system, and e-marketing provide a market and living environment for business intelligence systems (George et al., 2015; Moro et al., 2015). Furthermore, the development of business intelligence also benefits from the development of related technologies, such as high-capacity storage technology on hardware, parallel processor technology, software mining tools, data warehouse environment management tools, etc., enabling enterprises to invest at lower cost. Business intelligence, which in turn leads to a higher return on investment (Demirkan & Delen, 2013). Coupled with Internet technology, data access and sharing between distributors, suppliers, business partners, retailers and manufacturing companies is possible, so the research and application of business intelligence has been paid more and more attention (George et al., 2015).

There are five main reasons why the transaction processing environment is not suitable for decision support system (DSS) applications (Dinter et al., 2015; Alpar & Schulz, 2016):

(1) The performance characteristics of transaction processing and analysis processing are different: In the transaction processing environment, the user's behaviour is characterised by high frequency of data access operations. The time required for each operation is short, so the system can allow multiple users to use system resources in a time-sharing manner while maintaining a short response time; in an analytics processing environment, the user's behaviour pattern is completely different (Dinter et al., 2015). A DSS application may need to run for several hours, which consumes a lot of system resources. It is obviously not appropriate to put two applications with such different processing performance in the same environment (Dinter et al., 2015). 

(2) Data integration issues DSS needs to use integrated data: Comprehensive and correct data is the premise of effective data warehousing technology for effective analysis and decision making in business intelligence systems (Alpar & Schulz, 2016). The more complete the relevant data is collected, the more reliable the results will be. The true state of data in most of today's enterprises is decentralised rather than integrated. There are many reasons for this distraction, mainly transaction processing application dispersion, "spider web" problems, data inconsistencies, external data, and unstructured data (Alpar & Schulz, 2016). 

(3) Data dynamic integration problem: The biggest disadvantage of static integration is that if the data changes in the data source after data integration, these changes will not be reflected to the decision maker, resulting in the use of outdated data by the decision maker (Sharda et al., 2016). The integration data must be refreshed in a certain cycle, which we call dynamic integration. Obviously, transaction processing systems do not have the ability to dynamically integrate. 

(4) Historical data problem: Transaction processing generally only needs current data. In the database, short-term data is generally stored, and the retention period of different data is also different. Even if some historical data is preserved, it is not fully utilised. But for decision analysis, historical data is quite important, and many analytical methods must rely on a large amount of historical data (Dinter et al., 2015; Alpar & Schulz, 2016). Without detailed analysis of historical data, it is difficult to grasp the development trend of the enterprise. DSS has higher requirements on the breadth of data and time, and the transaction processing environment is difficult to meet these requirements (Alpar & Schulz, 2016). 

(5) Comprehensive problem of data: A large amount of detailed data is accumulated in the transaction processing system. In general, DSS does not analyse these detailed data. Before the analysis, it is often necessary to synthesise the detailed data to different degrees (Alpar & Schulz, 2016). Transaction processing systems do not have this comprehensive capability. According to the standardisation theory, this integration is often limited by a data redundancy. To improve the efficiency and effectiveness of analysis and decision making, analytical processing and its data must be separated from operational processing and its data (Dinter et al., 2015). Analytical data must be extracted from the transaction processing environment, reorganised according to the needs of DSS processing, and a separate analysis processing environment is established. The data warehouse is a kind of data storage that appears to build this new analytical processing environment (George et al., 2015). 

With the establishment of the online information platform of securities companies and the development of online transactions, business intelligence has also had corresponding solutions (Moro et al., 2015). Using Web log analysis and mining technology, the company can not only better track existing customers, but also discover interesting behaviour/transaction patterns that cannot be found in many offline trading system research and analysis processes, and even affect current market potential factors. There is a certain degree of independence between Web-based business intelligence and business intelligence based on internal business systems, but they are inextricably linked (Sharda et al., 2016). If you can combine the two, it will make the enterprise's intelligent platform a new level and more powerful. In the following sections, the author will focus on this content. In short, with the deepening and intelligent transformation of the competition, business intelligence can effectively help securities companies to integrate various resources and conduct creative analysis and mining according to user needs, thus precipitating the company's many years of operation (Alpar & Schulz, 2016). A large amount of data is transformed into knowledge and further becomes a driving force for business growth. Business intelligence has broad application prospects in the securities industry. It can provide securities companies with more comprehensive, accurate and secure data; more convenient data query application methods; more forward-looking data analysis and mining methods (Alpar & Schulz, 2016). This will bring a full range of information control capabilities, efficient resource utilisation, rapid response and decision making. 

From the perspective of system architecture, business intelligence systems generally consist of data warehouse (or data field), data analysis, data mining, online analysis, data backup and recovery (Thamir & Poulis, 2015). The business intelligence system extracts useful data from the data collected by different data sources, cleans the data to ensure the correctness of the data, converts the data, reconstructs it and stores it in the data warehouse or data field (the data becomes information at this time), then find the right query and analysis tools, data mining tools and OLAP tools to process the information (when the information becomes the knowledge of the decision-making), and finally present the knowledge to the user and turn into decision-making (Thamir & Poulis, 2015; Alpar & Schulz, 2016).

Decision support tools includes everything from basic query and reporting tools to advanced online analytical processing to information mining tools. All tools support GUI client interfaces, most of which can be used on the Web interface. Most of these tools can handle database information, and some can handle complex and unstructured information on file systems, multimedia, mail, or web servers (Sharda et al., 2016). The access tools include application interfaces and middleware that enable customer tools to access and process business information in databases and file systems. Database middleware allows customers to transparently access various heterogeneous database servers in the background. Web server middleware allows Web clients to connect to the database (Thamir & Poulis, 2015). The data management part generally uses three layers of information storage. The highest level is the data warehouse. The data warehouse integrates the business information of the whole enterprise. The middle level is the department data warehouse, which stores the commercial data warehouse of a commercial unit, user group or department (Alpar & Schulz, 2016). Application information of technology in a business intelligence system, these data fields can be directly established on the basis of the enterprise business operating system; the lowest level of the structure stores the tailored information based on user and application requirements (Wongthongtham & Abu-Salih, 2015). 

Furthermore, data warehouse model and construction tools are used to capture data from the operating system and external data source systems, process and convert, and finally load the data into a global or departmental data warehouse (Sharda et al., 2016). In addition, the metadata management manages metadata related to the entire business intelligence system, including technical metadata used by developers and administrators, and business metadata that supports business users (Alpar & Schulz, 2016). 

Data mining technology is mainly used to find the hidden rules or relationships between data from a large amount of data. It is usually automatically recognised by the machine and does not require more manual intervention (Sharda et al., 2016). Using data mining technology, it can provide intelligent and automated assistance for users' decision analysis. It can be applied in many fields such as retail, finance and insurance, and medical industry (Moro et al., 2015). Moreover, cluster model, when the data to be analysed lacks description information, or cannot be organised into any classification mode, the cluster model can be used. The clustering model divides user data into groups that are different from each other according to a measure of similarity. The data in the group is similar, and the data between the groups is quite different (George et al., 2015; Sharda et al., 2016). The core of the clustering model is to convert some obvious methods of measuring proximity to quantitative testing methods (Wixom et al., 2014; George et al., 2015).

With the help of business intelligence and the use of massive amounts of data accumulated in the medium and long term, four applications can be realised: 

(1) Analysis of customer classification and characteristics: According to the customer's large number of consumption records and customer's archives data, the customers are classified, and the consumption power, consumption habits, consumption cycle, demand tendency and credibility of each type of customers are analysed (Kimball et al., 2015). Class customers bring the most profit to the company, which kind of customers only bring the least profit to the company and demand the most return, and then give different services and offers to different types of customers (Ramakrishnan et al., 2012; Kimball et al., 2015).

 (2) Analysis of marketing strategy: The use of business intelligence to realise the simulation of the marketing strategy on the model, the simulation results will indicate whether the marketing strategy is appropriate, and the company can adjust and optimise its marketing strategy to maximise its Success (Kimball et al., 2015). 

(3) Analysis of operating costs and income: Cost accounting for various types of economic activities, comparing the balance of income between business income and various expenses, analysing the curve of economic activities, and obtaining corresponding improvement measures and measures to reduce (Chen et al., 2012; Kimball et al., 2015)

(4) Analysis and prevention of fraudulent behaviours: Using on-line analysis and data mining techniques, summarising the rules of fraudulent fees and arrears behaviours, establishing a set of rules for fraudulent behaviour and arrears behaviour on the basis of data warehouse, warning various frauds Fees, arrears, and reduce corporate losses (Kimball et al., 2015). 

Data mining is the process of mining knowledge from a large amount of data stored in a database, data warehouse or other information base. Data mining technology stems from the direct demand of business, so it has a wide range of use values ​​in various commercial fields, mainly in the following aspects (Sun et al., 2015; Sharda et al., 2016):

(l) Customer analysis with the "customer-centric" business philosophy: In-depth knowledge, analysis of customers, understanding of customers and guiding customers' needs has become an important issue in business operations. By analysing the accumulated transaction data, customers can be classified according to various customer indicators (such as natural attributes, income contribution, transaction amount, value, etc.), and then the behaviour patterns of different types of customers can be determined, so as to adopt corresponding marketing measures to promote the maximisation of corporate profits (Sharda et al., 2016).

(2) Optimisation of operational planning and enterprise resources: Cost savings are the key to corporate profitability. By analysing historical financial data, inventory data and transaction data, you can find the key points of enterprise resource consumption and the input-output ratio of main activities, so as to provide decision-making basis for optimal allocation of enterprise resources, such as reducing inventory, improving inventory turnover, and improving Capital usage rate, etc. (Sun et al., 2015)

(3) Anomaly detection: In many commercial fields, anomalous events have significant commercial value, such as customer churn, bank letter chapter business intelligence system core technology card fraud, mobile phone arrears in telecommunications, and signs of special diseases in medicine, etc. These anomalous events can be quickly and accurately identified through singularity analysis in data mining (Sun et al., 2015). 

(4) Management of enterprise analysis models Enterprise members use data mining to tune and customise models. This will gradually accumulate model libraries that meet the needs of the enterprise and become an important part of the enterprise knowledge base. At present, data mining technology is very practical in commercial applications (Thamir & Poulis, 2015). Specific examples of common business applications are: Customer segmentation, customer retention, target marketing, customer development, fraud detection, shopping basket analysis, credit scoring, credit risk assessment, portfolio management, market analysis, security management, customer profitability analysis, resource management, profit analysis, cross-selling , incremental sales, customer service automation, activity analysis, sales forecasting, revenue forecasting, demand forecasting, claims analysis, performance analysis, store location, inventory control, process quality control, failure analysis, prescription analysis, and more (Wixom et al., 2014). 

Furthermore, the most significant value of the risk management of the business intelligence project is the forecasting trend and the decision-making. Therefore, the basic data preparation work in the early stage of the BI project must be done relatively well (Debortoli et al., 2014). Informative data is the cornerstone of success for BI projects: The more basic, more detailed, and more accurate the underlying data, the more accurate BI's predictions for the enterprise. Correspondingly, the survey of users of business analysis companies found that the risks posed by the quality of the underlying data and data integration are also the biggest problems that plague the B1 project (Alhyasat & Al-Dalahmeh, 2013; Moro et al., 2015). Therefore, data quality is a very important factor in the BI field. The quality of the data reflects the value of the data. The higher the quality of the data, the greater the value of the data. Enterprises must use data to analyse and make decisions, and must build high-quality data (Debortoli et al., 2014). Without accurate data as the basis, data analysis and data mining are not reliable, and corporate decisions will be significantly affected and biased. After some BI projects were launched, the person in charge found that the results of the system calculations were quite different from the actual calculation results. After reviewing it, it was found that the original data was inaccurate (Alhyasat & Al-Dalahmeh, 2013). Only reorganise the underlying data. The main function of the BI system is to perform statistics and analysis on the existing basic data in order to provide data support for management decision-making. If the underlying data is inaccurate, it can make BI projects difficult. Therefore, the project administrator needs to take appropriate measures to ensure the accuracy of the underlying data. Many users already have multiple applications before building a BI system (Chiang et al., 2012; Debortoli et al., 2014).

 The biggest difference between data warehouse and traditional database storage is that the database is used for enterprise daily transaction processing, while the data warehouse is used for business analysis processing (Alpar & Schulz, 2016). The purpose of the data warehouse is to analyse the business information to assist the enterprise decision-making without affecting the daily operation processing, and to provide an application basis for the decision support system. Therefore, data warehouses and databases are two types of data management systems that are used for different purposes of business operations. The data storage technology is the core of the data warehouse technology (Thamir & Poulis, 2015). The data integrated in the data warehouse usually organises the data in a star schema, that is, a one-dimensional table structure of the fact table. The fact table is also called the primary table, including quantitative or actual data for business activities. This data can be measured numerically and consists of multiple rows and columns; the dimension table, also known as the auxiliary table, is generally small and is descriptive data reflecting a certain dimension of the business. The fact table and the dimension table are connected by relationship (Thamir & Poulis, 2015; Alpar & Schulz, 2016).

Research methodology tends to form an integral part of academic research studies. This includes the selection of the most appropriate approach for rendering the various research activities in order to achieve the overall objectives of the respective research study under consideration (Alvesson & Sköldberg, 2017; Creswell & Creswell, 2017). In this respect, this chapter will include the discussion of the various research methodologies generally used in academic research studies. In addition, the exclusive study design which is used for the fulfilment of the research objectives will also be entailed in this chapter. Furthermore, the associated ethical considerations related to the academic research studies will also be highlighted in this chapter.

The standardised approaches of research are generally associated with the differences in the collection and the analysis of the data for the completion of the research studies. The researcher's methodological orientation determines the research methods he/she may take when studying a problem (Cohen et al., 2013). If the method involves the activity of collecting data, the methodology explains the rationality of conducting these activities. Methodological orientation should be the connection point between epistemology and method, or the basis of design research programs (Cohen et al., 2013). 

At present, the research on educational research methods and methodology itself has not attracted the attention of education researchers. In recent years, research the author gave a certain level of research on a new term in the field of educational research methods. At the same time, people have also discussed some important research methods in the study of education, quantitative research (Creswell & Creswell, 2017). However, after reviewing these research results, most of the research is concentrated on the theoretical level, and some opinions are expressed. Based on the author's pure speculative thinking, there are often different opinions and different situations. Therefore, so far, quantitative research has not yet formed a comprehensive, objective and correct understanding (Cohen et al., 2013).

The investigation and comparative analysis of the application status reveals objectively the existing status and role of quantitative research in educational research. The research uses literature analysis, content analysis, historical law, comparative research method, graphic method and table (Creswell & Clark, 2007). They are sufficient to summarise the basic characteristics and mode of operation of qualitative research. The various variants of qualitative research will be attributed to possible changes in the six elements. In the following, these elements will be discussed in the form of the six major issues involved in the research process (Alvesson & Sköldberg, 2017). 

Although the elements are given in order, it does not mean that the researcher must follow this order rigidly. Since qualitative research is an evolving dynamic process, the elements often permeate each other and cycle (Creswell et al., 2003). It is generally believed that the theoretical basis of quantitative research is positivism and realism; the theoretical basis of qualitative research is naturalism, constructivism or other post-positivist philosophical schools. This kind of argument can be established in some cases, but it is not a certain rule. In fact, the philosophical and methodological practices that were thought to be opposite each other in the past can actually be merged (Cohen et al., 2013). For example, it is not difficult to find a researcher who holds both positivist beliefs and qualitative research methods. 

However, there are also qualitative researchers who are willing to use deduction to prove a predetermined theory. Induction and deduction can also illustrate the differences between other methodologies. When using inductive methods, the research object is the centre of research (Alvesson & Sköldberg, 2017). The research object not only plays an important role in the construction of the theory, but also the object of the research object is protected, and the authority of the interpretation of the problem is also highly respected. In contrast, the deductive rule is easy to focus on the researcher, and the researcher is the most authoritative from beginning to end. The above differences show that the attitude of the researcher's voice and authority is an important indicator to distinguish different types of research (Cohen et al., 2013).

Therefore, exploring qualitative research methods outside the circle will help to improve the traditional qualitative research methods and make them eventually develop in the direction of localisation. In the discussion of qualitative research methods in the West. How to define qualitative research is a hot issue. This article explores a possible definition framework to outline the basic image of a qualitative research approach (Creswell et al., 2003; Creswell & Creswell, 2017). The discussion will focus on the six basic elements of qualitative research and introduce the Western genres and practices in these areas for reference by domestic educational researchers.  The discussion of qualitative research methods often involves the definition of qualitative research. Since qualitative research involves multiple methods and involves the collection of multiple empirical data, it is difficult to give a straightforward definition (Creswell & Clark, 2007). 

The method usually represents a set of methods for collecting data; while the inquiry reflects the position that should be recognised in terms of cognition and methodology. Sherman went on to point out that qualitative researchers who emphasise inquiry are likely to form a consensus. Similarly, qualitative researchers who focus on methods are also likely to reach consensus (Cohen et al., 2013). And if qualitative research is used as the method and the end of inquiry, these two aspects are mutually exclusive. It is difficult to form a harmonious and unified image. In order to visually summarise qualitative research methods, this paper will present a graphical definition framework. The framework can describe the whole process of qualitative research. It also illustrates the difference between inquiry and method (Alvesson & Sköldberg, 2017). 

This study will be based upon the conduction of primary as well as secondary research. In this respect, the primary research is associated with the development of research surveys through the intrinsically designed questionnaire reflecting towards the various aim and objectives of this particular study. In this regard, the selected population for the development of the primary research study, the population of the study consisted of a total of 75 participants selected for the survey. However, due to the limitations of time as well as other constrictions, only 24 of the respondents completely reverted with the questionnaire surveys. The findings of the results of the survey will be mentioned in the following chapter.

For the development of the primary research findings, the participants were distributed an online survey questionnaire through the use of Google docs form. This form enables the acquisition of the answers given by the participants which is easily accessible through the online form. The participants were intimated regarding the surveys both personally as well as through their respective online contacts. The participants were also given the consent form in order to respect their personal interests and consents regarding the participation in this study survey.

Moreover, in order to augment the various findings of the study and the authentication of the respective findings, the use of secondary research data has also been utilised. This will be mainly included in the discussion of the research findings of this study. The integration of the secondary data with the primary research findings will enable enhance comprehension of the various study results. In addition, this will also enable clarity in terms of the discussion of the various findings of the study under the relate contexts of the associated and authenticated findings of other peer-reviewed journal articles related to the subject under consideration. The selection of the various research study associated with this study was maintained through the search on various databases including “Google, Bing, Yahoo, Arxiv, TandFonline, Wiley, Researchgate and academia.” The search of the relevant materials for the study was maintained through the use of exclusive this included the use of keywords related to the objectives of this study.

For the analysis of the various findings of the study for the primary study of this thesis, the use of graphical tabulation was maintained. This was carried out on the excel sheets through applying appropriate formulae and analytical inputs related to the survey. This resulted in the development of graphical representations of the data analysed through the questionnaire survey. As the questions included in the survey were generally close-ended, the statistical tabulation of the results was rendered quite easily. Moreover, for the augmentation of the research findings against the related secondary research data, the use of interpretative research philosophy has been employed. This is associated with the development of inferential analysis reflecting towards the evaluation of the research surveys. In addition, the use of this approach enables the optimum integration of the associated conceptual frameworks with the findings of the study.

The foremost ethical consideration associated with this study is related to the consent of the participants of the primary study surveys. In this respect, the participants of the survey were provided with the ethics consent form (Bryman, 2015). This contained the relevant consent of the participants for providing their respective inputs for the study survey. Furthermore, regarding the use of secondary data from the past literature, the various sources being used in this study have been accurately cited and referred to in order to conform with the associated legal and ethical considerations.

This chapter will enumerate the various findings of the study acquired through the primary research surveys. In addition, the respective findings of the study will be assessed and appraised through the integration and support from the related secondary research studies in order to attain authentication of the related research surveys. The various findings of the study have been graphically represented against the respective literature to support the relevant findings of the study.

The following section will include the various survey findings developed through the statistical tabulation of the various answers gleaned through the questionnaire surveys. This will be discussed through the integration of the relevant literature associated with the study findings.

The foremost query asked of the participants was regarding the perception related to the role of business intelligence in the management of the projects. In this respect, majority of the participants answered that they agree to the notion. Hence, it is established that business intelligence tends to play an integral role in the management of the projects. For a single project, it is generally relatively independent of other projects, with only a few minor interfaces to other projects. For business intelligence projects, each sub-project is closely linked, and the failure of any one project will affect other sub-projects, which may lead to the failure of the entire project. The more sub-projects, the more complex the management interface, so handling the management interface between sub-projects is the key to the success of business intelligence projects (Elbashir et al., 2008; Williams & Williams, 2010).

The next question asked of the participants was regarding the use of business intelligence for the development of competitive edge for the respective projects under consideration. In this regard, the majority of the participants agree to the notion that the business intelligence tends to assert influence towards the development of competitive advantage in the respective business projects. In today's highly competitive business environment, business operations involve large amounts of data. Successful companies can use this data to increase their understanding of the business, so that they can make timely judgments and take sensible actions in the management and development of the business (Turban et al., 2010). The success of a business depends on its ability to effectively leverage information across various business systems, data marts, or data warehouses. Proper use of this data can improve business processes, enhance customer service, drive sales, and increase profitability. The way to do this and solve many other critical business problems is to build an infrastructure that delivers valuable information to information users, enabling users to make informed decisions in a timely manner (Turban et al., 2010). Many successful companies are realising the value of business intelligence through a complete information supply chain that connects information producers and information consumers. Comprehensive enterprise business intelligence enables organisations in the organisation to use the right tools to get enterprise information, according to their needs (Chen et al., 2012).

The next query asked of the candidates of this study is associated with the notion of the utilisation of the business intelligence in the development of enhanced strategies for the respective business projects. In this respect, the majority of the participants answered that they agree to the notion- asserting the notion of the significance of business intelligence in the development of optimised strategies for the entailed functions of the business projects. From the internal environment, there are information communication between various sub-projects of business intelligence and between the project and the relevant functional departments of the company; from the external environment, there is information exchange between the project team and the user industry standard, and the provincial-level company level Information exchange, and user-level communication with local companies, especially provincial and municipal companies, including communication between multiple departments; and communication with other third parties, such as the original manufacturer, providing other system interface development Business and integrators (Sabherwal & Becerra-Fernandez, 2011; Turban et al., 2013).

Regarding the notion of the business intelligence aiding in the development of cost-effectiveness of the entailed operations of the respective business projects, the majority participants answered in affirmative to the notion. This asserts that the business intelligence tends to participate in the development of cost-effectiveness for the various business functions and operations. For decades, information technology personnel have consistently provided "pre-made" reports and analysis to information users. While many organisations authorise users to access corporate data more directly, most users still rely on information technology personnel (Sabherwal & Becerra-Fernandez, 2011). Successful enterprise business intelligence dramatically changed this dependency, creating an environment where information technology staff can effectively provide users with information and the right business intelligence tools to “arm” users while reducing themselves. The burden of heaping reports and comprehensive technical support (Golfarelli et al., 2004; Sabherwal & Becerra-Fernandez, 2011). 

With respect to the aspect of the relative costs of business intelligence to be effectively utilised for the management of business projects, the majority of the participants answered in neutral. However, among the candidates who answered in negative or positive, the majority is of the opinion that business intelligence tends to cost a lot of money for the respective business projects. The overall economic benefits generated by the company. These indicators are usually some calculation formulas, where the variables are the key basic data of the enterprise, indicating the actual operation and management level of the enterprise. These data can usually be obtained from the financial and statistical reports of the enterprise; the constant parameters indicate that the enterprise applies the BI system. The degree of performance improvement in production, marketing, service, product and R&D is the achievable traction target of the application of BI system (Watson, 2009).

Regarding the aspect of business intelligence being effectively used for the management of business projects and the associated acquisition of profitable outcomes, the majority participants affirm that the returns on investments related to the effective utilisation of business intelligence for the management of projects is quite high. Internal Rate of Return (IRR) is an interest rate that makes the net present value of all future cash flows equal to zero (Golfarelli et al., 2004).  IRR is widely used in various investment evaluations and plays an important role in the decision-making process of deciding whether to invest. IRR is the real rate of return of an investment, expressed as a percentage. It is the discount rate of the break-even, interest rate cash outflow. Hence, through the calculation, the relative advantages of the implementation of the business intelligence approaches can be effectively gauged (Golfarelli et al., 2004).

With respect to the effects of business intelligence in the development of enhanced efficiency of the business projects, majority of the participants assert that the effective implementation of business intelligence tends to reflect towards overall enhanced and efficient business project operations. Today, business intelligence is often understood as a tool that transforms existing data in the enterprise into knowledge and helps companies make informed business decisions (Watson, 2009). The data discussed here includes orders from the enterprise business system, inventory, transaction accounts, customer and supplier data from the industry and competitors' data, and various data from other external environments in which the business is located. Business intelligence can assist business decision-making, either at the operational level or at the tactical and strategic levels. The theoretical basis of the analysis of enterprise business intelligence effectiveness in the current financial industry, telecommunications industry, retail industry, and service industry are widely used in various types of business intelligence tools, and have experienced data (Ranjan, 2008). The power of analysis, reporting, and mining has been greatly assisted in business analysis, customer selection, performance management, and operational early warning. Some emerging companies, such as online bookbinding and reservation services, even form business competitiveness as the core to form competitive capabilities, organise internal operational processes, and challenge industry leaders with analytical competitors (Watson, 2009).

Regarding the aspect of knowledge management in projects for the enhancement of the operability of the employees and team workers of the respective business project, majority of the participants are of the opinion that business intelligence tends to play a significant role in the development of such constructs. These are the concrete manifestations of fine management. All of them are directed to the internal management of enterprises. The biggest feature of internal management is the certainty. This certainty is reflected in two aspects. First, all the daily management of the enterprise. Operations are events that have occurred, such as outbound storage, receipt and payment, and so on. Second, all of the company's plans can be accurately calculated according to pre-made algorithms. This certainty is the premise of standardisation and process management, and the standardisation and process management must bring about efficient management operations. When companies have refined such finely managed content, they have the foundation of agile operations (Ranjan, 2008; Cohen, 2017). 

With respect to the perspective of the necessity of business intelligence for the development of profitable business projects, the majority of the candidates of this study are of the opinion that there is a positive correlation between the business intelligence and the development of profitable business ventures. The information industry has continued to develop, information networks have become widespread, and information technology has become a prominent feature of world development, and has gradually evolved into a full-scale social transformation. In project management, there are more and more research and practice of information technology. As a labour-intensive enterprise, the construction enterprise has a series of characteristics of diversity, complexity and dispersion. It is a comprehensive system with strong comprehensiveness and wide applicability (Rouhani & Lecic, 2018). Engineering requires comprehensive control over the full lifecycle of project management and effective planning, organisation, management and monitoring. Therefore, the application of information technology in project management is increasingly important. Effectively use reasonable business intelligence models and database models to abstract, organise, analyse, and summarise various types of data of companies, and provide decision-makers with a window to grasp the pulse of the enterprise in an image, comprehensive, timely, and accurate manner. For project management of enterprises, it is easy to be affected by natural factors and human factors. The business intelligence system requires high timeliness and accuracy of data (Rouhani & Lecic, 2018). 

Regarding the subjectivity of the business intelligence requiring extensive manpower to manage and effectively utilise, the majority of the participants are of the opinion that the management of business intelligence does not require extensive manpower to effectively manage. This asserts that the relative ROI related to the effective utilisation of business management for the enhancement of the business project outcomes is justified in terms of the human resources being dedicated to the notion. Through the BI system, isolated, decentralised enterprise data is related to each other in historical order and can be stored in an efficient, easy-to-extract structure. As a result, companies can perform rapid analysis on different perspectives and transform data into analytical data. Enterprises can perform any in-depth analysis, mining and organisation of data according to their own analytical methods (Trieu, 2017). This data warehouse is organised in a fast, flexible and versatile manner, making data access extremely easy and accessible to users in a web-based manner. Key words affecting the effectiveness of business intelligence the key to implementing business intelligence projects is the implementation of the system. Several key factors affecting the implementation success include: organisational support, project management, and technical support. 

The most difficult part of implementing a business intelligence project is not technology, but management and control of resistance within the enterprise. Whether the management supports it or not, whether the business unit is involved in the process is the guarantee for the successful implementation of business intelligence. The success of a project requires the support of decision makers and management (Ranjan, 2008; Cohen, 2017). The degree of understanding and awareness of business managers directly affects the development of business intelligence projects. The successful implementation of BI requires the support of company leaders in the following three aspects. First, set clear goals for business intelligence. Second, as a promoter of business intelligence, provide business intelligence projects with all the conditions they need to achieve their goals. Finally, make sure that companies recognise the importance of such a project. When there is a problem with the project, he needs to motivate the staff to solve the problem (Trieu, 2017).

The implementation of business intelligence is not just about the IT department, but about the entire enterprise. The IT department mainly completes the technical realisation of the functions; and the analysis of the needs of the enterprise, and mining some information, must involve all the staff. In the implementation of business intelligence projects, all employees of the company must first realise that business intelligence is a contact mechanism aimed at exploring internal and external business data, and understand that this new mechanism will work for their own departments and their own work (Sabherwal & Becerra-Fernandez, 2011; Turban et al., 2010).

A project planning is the blueprint for the project team. In the project planning phase, the success criteria of business intelligence must first be defined. With successful standards, there will be successful project implementation. The quality of the software system is closely related to the development process. Now the research on the hot software maturity model (CMM) is process-based. In the business intelligence project, there are also part of the system implementation, and the control of the project process cannot be ignored. Its contents include scheduling progress and schedule analysis, cost estimation, budget and expense analysis, project situation analysis, project file management and report writing, task assignment and implementation control (Chen et al., 2012; Rouhani & Lecic, 2018). 

BI of these uncertainties require managers to be agile, which requires ERP to help companies look at the status quo and simulate the future. The maturity of enterprise ERP applications has led to efforts to maximise E's results. However, all these efforts must be based on the complete data foundation established by ERP (Rouhani & Lecic, 2018). Therefore, in the course of this effort, it is also the process of perfecting ERP and establishing a solid data foundation. Of course, the most effective use of ERP will inevitably require the expansion of ERP tools. At this time, the introduction of business intelligence may be the most appropriate time. The reason is that BI makes up for the shortcomings of ERP, because the data foundation established by ERP is only the operational data of enterprise management. All decision analysis for the management level can only rely on analysing data, introducing business intelligence based on data warehouse, applying data warehouse technology to transform management operation data generated by ERP into management analysis data in data warehouse, and then analysing these data (Ranjan, 2008; Trieu, 2017). The data mining of data enables a variety of management analysis models to be utilised, which is definitely the best way to play ERP utility. 

BI is architected on top of ERP, and decision support is a re-expansion based on BI. Installing a data warehouse in an ERP environment is a fairly economical recommendation. From the perspective of infrastructure, BI database and ERP have many commonalities, and there is a high possibility of integration between the two parties. The biggest commonality between the two is that they make the business run more efficiently, respond more timely, and integrate easily (Trieu, 2017). Therefore, companies that have implemented ERP need BI to further enhance. After the implementation of ERP, enterprises have established a new business processing model. All the business processes involved in the ERP system are coordinated by integration and break the original division of the department. The information acquisition ability of all links in the company has been improved, the business processing bottleneck inside and outside the enterprise has been broken, and the response speed can be improved accordingly. BI can improve users' knowledge and control accuracy in key areas (Cohen, 2017). 

The net cash inflow of the project includes the increased profit of the enterprise and the depreciation of the fixed assets recovered due to the investment. The shorter the payback period, the better the economic benefits of the project. The faster the funds are recovered, the less risk they will bear in the future. The payback period is static analysis, and the calculation is simple but not accurate enough. Therefore, we must also consider the dynamic analysis of the time value of funds, such as internal rate of return (Golfarelli et al., 2004). There are other evaluation indicators such as average profit margin and net present value. The financial evaluation indicators have their relevance. According to international conventions, the two key indicators of ROI are reflected (Rouhani & Lecic, 2018).

 The informationisation will immediately remind us that enterprises must have a large number of computers, complex network structures, expensive capital investment and thorough business restructuring, and the company will carry out a revolutionary revolution. Looking forward to huge economic benefits. This makes the average enterprise feel hopeless, and SMEs without sufficient funds, technology and personnel face greater difficulties and have fewer chances of success (Rouhani & Lecic, 2018). Doing more work and spending less money has become the traditional standard for evaluating performance, but informatisation construction often costs a lot of money and risks. In order to make information construction successful, we must first understand why we should build an information system and then choose a solution for information system construction. Enterprises will face more and more various management problems, which can be solved with various solutions. Information construction is only one of them. Enterprise information construction must be considered in a comprehensive manner due to time, conditions, capabilities and needs. Blindly purchasing computers, building networks, and forcing the implementation of software management ideas can not only achieve the expected goals, but also seriously affect the business operations and normal management, ultimately leading to less effective investment, and even bring huge risks to enterprises (Cohen, 2017). 

The system adopts a self-editing tool and a dynamic scripting method, which has the characteristics of flexibility, high speed and strong controllability. To create a cube, you need to aggregate the metrics in different dimensions. Save the data for later reading and analysis. Considering that the business intelligence system mainly performs query and analysis operations, in order to improve the response speed, the multidimensional data model of the system adopts a star schema design, which supports defining the data entity from the perspective of the decision maker to meet the needs of the subject-oriented data warehouse design (Golfarelli et al., 2004; Rouhani & Lecic, 2018). In a multidimensional model, data is stored as facts and dimensions. The star schema consists of a fact table and multiple dimension tables linked to the fact table. Located at the centre of the star model is a fact table that stores factual and quantitative data for the business. Located on the star of the star model is a dimension table that stores descriptive data. The fact table provides quantitative data for the user's decision analysis, and the dimension table represents an angle of observation data, which can be regarded as a measurement scale or a side of the cube. Its role is to limit the user's query results, filter the data, and make the data from the indicator (Rouhani & Lecic, 2018). 

With the support of the business intelligence system, the production and operation process of the enterprise has formed a production management system with coordination and cooperation and information sharing in all aspects of the business process from a management structure that is fragmented and the management information is not smooth. The links between the main business processes are closer and more coordinated, and the management information is more transparent, enabling managers at all levels to accurately grasp the progress of production and operation in a timely manner, providing a detailed and reliable basis for decision-making. The management benefits generated before and after the implementation of business intelligence. There are problems before implementation, no overall strategy is effectively decomposed into corresponding product development strategy, marketing strategy, production strategy; departmental strategy is not implemented, strategy execution ability.

The procurement supplier management system does not establish a sound supplier management system; the evaluation of suppliers only has the quality control regulations. It is impossible to establish a supply chain system cycle standard that is developed in parallel with the enterprise. The lack of a complete system procurement cycle, procurement batch and inventory standards and other cycle standards is not conducive to the development of a reasonable procurement plan price system. In order to reduce the difference between the actual price and the planned price, the warehouse accounting is always available. The arbitrary adjustment of the planned price has lost the control standard of cost; the cost of each link cannot be assessed to realise the integration of production planning, completion, quality, cost and other information; the management departments at all levels can master production in a timely and accurate manner. The implementation of the plan is to set up warehouses in each workshop, and manage according to the warehouse management process to realise data sharing of various systems within the enterprise. 

From the perspective of the application of business intelligence, the current development presents the characteristics of industry and specialisation, and each industry has its focus and analysis model. The general business intelligence system is gradually becoming a concept, and the customer actually needs a system suitable for his or her business field. At present, relevant research in this regard is still in its infancy. Although there are commercial software applications in the international market, it is mainly concentrated in the fields of banking, insurance, telecommunications, and manufacturing. Business intelligence technology is a high-end application in information technology. In view of the actual situation of the construction enterprise, this paper successfully established a targeted business intelligence system in the project management of the business projects, solved the information island and is an effective tool for project management personnel decision-making. 

 Innovative benefits refer to the economic advantages of the enterprise's competitive advantages brought about by the ingenious BI in terms of technology and performance. The BI system provides strong data support for enterprise management decision-making. Due to the deep refinement of data, enterprises have sufficient data support in business decision-making, which provides a driving force for enterprises to enhance their own competitiveness. Business intelligence has realised the scientific and standardised management of enterprises. The effective control of project cost has created new products for enterprises. The point of profit growth, the new profit growth point of the company brings better benefits to the employees of the company and brings more employment and investment opportunities to the society. The successful implementation of the business intelligence system once again verified the irreplaceable positive role played by enterprise informatisation in the production and operation of enterprises, and injected new confidence and vitality into the software service industry chain in the whole industry and even the whole society.

The difficulty is due to the need to distinguish between the benefits of BI and the benefits of other modern management methods or other factors. For example, support for decision-making, strengthening of the competitive position of the organisation, improvement of management level and quality of the enterprise cannot be measured by monetary value, while indirect and social benefits are sometimes far greater than direct benefits. Taking enterprises as an example, the economic benefits generated by BI come from the following aspects, so the total economic benefit should be equal to the sum of all aspects of economic benefits. BI increases labour productivity, shortens lead times, increases sales efficiency, improves relationships with customers and suppliers, and increases the credibility of the company. BI will provide high-level leaders of the company with information from internal and external organisations, national economic policies and laws, competitors in the same industry and product markets, in order to make timely scientific decisions, avoid crises, and occupy the market. Seising the opportunity, the increase in benefits brought about by this aspect cannot be measured in the form of money.

The establishment of BI increases the automation degree of enterprises looking for business opportunities, improves the efficiency of enterprise management and decision-makers, and reduces the labour costs of corresponding parts. BI can also provide timely and accurate information that has a significant impact on decision making, thereby improving the scientific and viable decision making, saving investment and avoiding unnecessary expenses.  Along with the continuous development and maturity of the market, many business leaders have found that this kind of management mode lacking information to support decision-making has become more and more difficult to find. It is thought that everything is under control, but the facts are often far apart.

The effectiveness of a business intelligence project in an enterprise can provide empirical and theoretical evidence for the overall development of the entire business intelligence industry. The research work done in this thesis is only a preliminary exploration of the analysis of the effectiveness of business intelligence. In the actual enterprise management theory category, there are more practical business and problems that involve business intelligence management ideas to be solved. The main focus is on securities investment, scientific and technological projects, construction engineering and other fields, and there are few applications in the field of information technology projects. Among the existing research results of project evaluation, some studies focus on some aspects of evaluation indicators, while others conduct research from different evaluation perspectives.

The users of the grid company can access, analyse and explore the data in a simple and convenient way, because the system can make the organisation and presentation of the data closer to the business rules, so the customer can quickly and intuitively understand the operation of the entire enterprise. With a business intelligence system, users can drill down directly on cross-reports and even charts until they find the details they need. By tidying up various system requirements such as functional requirements, system technical requirements, and network environment, select appropriate implementation suppliers and fully communicate with them to determine the system architecture, application platform, and design plan, and ensure that they complete project implementation as planned. The system collects data information from previously dispersed sales, finance, human resources, production, management reports, etc., and uses data mining theory and technology to "intelligently" and "automatically" analyse these raw data to solve the enterprise. In the process of information construction, different business systems form their own information islands, improve data integration and data utilisation, and provide sufficient and accurate statistics and reports in a timely manner, so that all levels of managers can view the overall situation. 

How to improve business operation efficiency, financial management ability, operational decision-making level and competitiveness is the biggest challenge and difficulty faced by power companies. Make full use of all kinds of financial data accumulated in the process of information construction of power grid enterprises (including integrated human resources, engineering systems, marketing systems, etc.), and explore and refine the relationship among these data through business intelligence technology to establish a unified Financial information standard and system and financial data centralised platform, realise centralised management of financial data, and establish a business intelligence analysis system for financial, business analysis and decision-making at the macro level. Build a “value chain-based” enterprise operation model, realise the integration and integration of business processes, information processes, and management processes, realise the “real-first control” management method, drive the financial management and decision-making leap of enterprises, and improve the enterprise and social economy. Benefits, improve the image of the grid enterprise.

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APPENDIX A:

Survey Questionnaires

Age group:

⃝ 18-20 ⃝ 21-30 ⃝ 31-40 ⃝ 41-59 ⃝ Over 60

Gender:

⃝ Male , ⃝ Female

APPENDIX B

NORTHAMPTON BUSINESS SCHOOL ETHICS FORM.

Part A: Project Detail

1. Project title: THE IMPACT OF BUSINESS INTELLIGENCE ON PROJECT MANAGEMENT

2. Programme/Module: MSC. PROJECT MANAGEMENT

3.

· I have read and agree to adhere to the NBS Research Ethics Procedure and the Guidance on Ethics for Researchers

PRINT NAME: VISHNUPRIYA RAVI

STUDENT NUMBER (if applicable): 17421277

Signed:

Date: 04/10/2018

4. Supervisor name (s): LUAI JRAISAT, DEBBIE ALEXENDER

5. Use of human participants: Tick one of the following:

· I am using human participants.

I am using archival data where individuals are identifiable

I am not using human participants or data where individuals are identifiable and therefore do not need to complete the remainder of this form.

6. Participants: Tick the box which most accurately describes your sample:

· Children under 16 years

· 16-18 year olds

· Adults over 65 years old

· Members of the public (general)

· NBS Students (please specify)

· Members of vulnerable groups (frail elderly, recently bereaved, members of support groups – describe here :……………..……...)

· Other. If other, describe your sample here:

7. Issues for concern: Tick below any issue that relates to this research.

· Involves participants undertaking tasks they would not normally undertake

· Involves any activity that might be described as an ‘invasion of privacy’

· Involves deception

· Involves a topic that would be considered ‘sensitive’

· Involves the collection of data that is not anonymised (contains identifying information such as name and address)

· Other. If other, describe here:

8. Methodology: Tick the appropriate box. Full details of what you will do and where it will happen, should be provided in the accompanying Proposal.

· Questionnaires

· Interviews

· Experiments

· Observations

· Archival

· Other. If other, state here:

9. Recruitment Process. Tick the process that best describes how you plan to recruit participants. Full details of how you will recruit and where it will happen, should be provided in the accompanying Proposal.

· Via poster in a public place such as a library or community centre

· ‘Packs’ will be provided to named person in an organisation/group to be distributed on my behalf

· Asking personal contacts to pass my information packs to their contacts

· Will be asking friends/family

· Cold calling

· Other. If other, state here:

10. Recruitment material. Tick all the recruitment material you will be using. They must not be used until seen and approved by your supervisor.

· Recruitment poster

· Recruitment letter to named person in an organisation/group who will be distributing ‘Packs’ on your behalf

· Recruitment letter to potential participants

· Participant Information Sheet

· Consent form

· Other. If other, state here:

11. Risk assessment: Some projects will require risk assessment for participants and/or researchers. In other words, there is a possibility that participants and/or researchers will get hurt collecting data. If so, a risk assessment must be conducted. Tick the appropriate box below concerning your need for risk assessment.

· There is no risk of injury to participants and/or researchers, so no risk assessment will be conducted.

· There is a potential of injury to participants and/or researchers, so risk assessment has been (or will be) conducted.

12. Consent from the host Company:

I consent to the aforementioned named student carrying out research on Company premises or in relation to this Company.

Senior Manager Name: _______________________________________

Senior Manager Signature: ____________________________________

Host Company Name: ________________________________________

13. Compliance with the Ethics Procedures of the Host Company.

I the student have read and complied with the ethics procedures of the host company.

Signed: ___________________________________

I the Senior Manager confirm that the student has read and complied with the ethics procedures of our Company.

Signature: ____________________________________

Please include an A4 sheet to explain what you hope to achieve from the data provided on acceptance of this project.

The research was conducted to ensure the significance or impact of Business Intelligence on the project management. By the help of data collected from the survey competitive advantage of business using BI, timely decision capability of BI, enhanced strategic management

enhance customer service, drive sales, increase profitability of business kind of impacts were analysed. With the help of this impacts helps to improve the business analysis and functionality hence Profitabitablity of projects or business increased. High timeliness and accuracy of data are the requirements of BI. Part B: To be completed by dissertation/research supervisor/mentor.

In the case of a student, this section should be completed by your Dissertation/Research Supervisor.

In the case of a member of staff, this section should be completed by the Head of Research/Research Mentor.

Supervisor/Mentor Comments: Please comment on the extent to which this proposal meets the requirements of NBS Research Ethics Procedure and the Guidance on Ethics for Researchers.

Should this proposal be considered by the Ethics Committee? No

For students, referral to the Ethics Committee is at the supervisor’s discretion.

For staff, all proposals should be considered by the Ethics Committee.

If yes, please copy this form to the Ethics Committee Secretary (Debbie Christopher)

If no, please retain a copy for your records.

Signed (supervisor/mentor): LUAI JRAISAT

Print Name: LUAI JRAIST

Date: 05/10/2018

Appendix C Consant Form

Business Intelligence helps in developing enhanced strategies for business functions disagree neither agree nor disagree agree 4 9 10

Business intelligence renders the business operations cost-effcteive disagree neither agree nor disagree agree 4 6 14

Business intelligence costs a lot of money to the business projects disagree neither agree nor disagree agree 4 12 8

Business intelligence has high ROIs disagree neither agree nor disagree agree 4 13 7

Business intelligence optimises overall efficiency of the various business functions disagree neither agree nor disagree agree 5 5 14

Business intelligence aids in knowledge management of the employees of the business projects Disagree neutral Agree 4 8 12

Business intelligence is necessary in order to maintain profitable business ventures Disagree Neutral Agree 2 4 18

Business intelligence requires extensive manpower to manage effectively for use in projects Disagree Neutral Agree 15 2 7

Business intelligence plays an important role in project management disagree neither agree nor disagree agree 14 6 4

Business intelligence provides ground for dveeloping competitive advantage disagree neither agree nor disagree agree 6 1 17