extfieldsofstudy: Physics Engineering

text:
Design and analysis of new box frame for online motorcycle transportation

With the increasing needs and human’s creativity, accompanied by the development of technological knowledge from various things, one example is the development of online-based transportation. Transportation is a tool in the form of a vehicle that purpose is to move an item from one place to another. In the current era of globalization, there are several companies that develop their business through online transportation, in addition to the shuttle service there is also a delivery or pick up of food/goods quickly that can provide convenience for the community. This paper describes the redesign of the box that motorcyclists could use in online transportation. The design concept is designed more innovative to design models and materials used are also more practical & efficient in terms of time in use both in terms of installation and dismantling of the box, the aim is to answer the problems of online transportation at this time, which are not only in terms of efficiency but also ergonomics. The research was conducted by observing the existing problems, then brainstorming in ergonomic box design and then making 3D modelling using the inventor’s stress analysis method. to achieved a new innovative and ergonomic design for helping online transportation.

Introduction
In this globalization era, technology tends to develop more and more every time both from technological knowledge, tools used, communication networks and others, one of which can be seen from the ease of people doing their activities by using various technological tools such as smartphones, only by using smartphones, everyone can do various things such as shopping, looking for news, or traveling, with this ease the community can do all their activities in a short amount of time. Transportation is a tool used to move an item from one place to another, in this case using a vehicle. Basically transportation can determine the attractiveness of a city and can be used as a characteristic of the city, with the development of technology and human creativity, the use of transportation can be developed into a form of online applications, known as online transportation that can be applied and used on smartphones, but it can also be used as navigation to a place. So, this resulted in the emergence of several online transportation companies or better known as companies whose business activities are based online (e-commerce), which aims to be able to facilitate the public in traveling, shopping or shipping goods that must be done quickly [1].
The development of online transportation in Indonesia began in 2015, that year was a phenomenal time for the development of online transportation. Starting with the development of a mobile application called Go-Jek which was then followed by competitors such as Grab and especially Uber who had first  [2]. If it is reviewed up to now the transportation company which initially only moves from online transportation has begun to spread its businesses with features such as non-cash payment methods that start using the application, which can further facilitate its users in the transaction. Not stopping there, they began to develop their business to the express / fast shipping feature which is very influential in today's e-commerce world. Due to the increasingly widespread distribution of e-commerce, thus causing a significant increase in demand for express delivery services to consumers [3]. To overcome the high demand, the solution obtained is to enlarge the scope of the shipment so that in one route there are several goods with different shipping destinations. In this case, it takes quite a long time compared to delivery by express or fast [4].
In the case of the development of the goods delivery feature contained in the online-based transportation application such as food delivery services or goods delivery services in a fast / lightning, there are several obstacles in the delivery process based on the results of a review that has been done previously to several online motorcycle taxi drivers who work in making deliveries food or goods, there are several complaints felt by the driver. Among them is there is no limit on taking the amount or weight limit of taking the order listed so that it can be difficult for the driver himself, then coupled with unpredictable weather conditions in the process of delivery or pickup of the goods so that it can be a problem for drivers who are taking and delivery of these goods both with a short distance or with the farther distance.
The purpose of making this box is to resolve the problem of the driver, which is not only in terms of efficiency but also in terms of ergonomics. Ergonomics becomes important in design not only because it meets design standards, but the aim is to prevent mistakes in the design process that will cause the design to be incompatible with the user's body. Therefore, ergonomic calculations must be considered and applied appropriately [5].
In the current situation, there are already several choices of boxes that can be used by drivers, but the existing box design feels less practical when mounted on a motorcycle. This is because the placement of the installed box does not have a suitable design, where the placement takes up quite a large amount of space in the seat of the motorcycle, therefore when the driver gets an order other than sending goods, the driver must remove the box and when the order process occurs the driver must re-install the box so that there is a less efficient process of time. These problems will be carried out by designing a multifunctional box that aims to facilitate the driver in sending goods ordered or also facilitate the use of the box in the use of other activities such as when the driver is delivering passengers without having to remove the box that is installed.

Method
Before doing this research, it has conducted several steps such as doing the observation on the current situation of box use, in this regard, it has formulated a problem to analyse box use in the current condition, then designing the right box model for online transportation users. Figure 1 shows a flowchart of the process a new concept of making box: In general, the procedures of this paper are started in developing the design concept from sketching to detail design and follow the new product development concept. The detail procedures are as follow: a) Identification of Problem: Firstly, doing the observation to online drivers for delivery order.
What is the problem and inconvenience in delivery process? b) Identification of User need: after doing the step 1, then conducted the brainstorm to start the ergonomics box design, and flexible. with purpose to facilitate online drivers in delivery order c) Creation of Product Concept: doing the benchmarking with existing products d) Product Specification: doing designing and determining of materials and size which will be used in the new concept of box design e) Product Design: after doing step 4, then continues to 3D Modelling design using Inventor software  At the moment, most of delivery methods experienced by delivery company are used box as shown in Figure 2. However, this practice could lead unstable for the motor cycle driver when increase the speed and make manoeuvre during traffic jam or rush hour. Figure 3 shows another box design that using fabric, it's also had weakness, due to the online drivers will difficult to ride with passenger, if they have a lot of goods to delivery in the same time With the current condition of box design, it cannot give the easiness to online drivers due they have to unload the box when riding with passengers, and it's not flexible. Therefore, the purpose of research is to give the easiness to online drivers in riding with passengers and delivery order without take apart the box

Result and Discussion
This paper proposes a new product design that helps online motorcycle taxis or expedition services to send goods to customers easily and safely without having to take apart pairs in a long time and no need to feel worried during heavy rain. The box design that was designed also made it easy for online transportation to transport passengers without having to remove the box. This box design has a lock to make the frame strong. Here is a comparison analysis table of the new box concept with the previous design product.  Table 1. Prove that the latest box concept design has many functions and advantages compared to the previous box model. After doing a comparison of the box products, the stress analysis is carried out using the inventor software which can show an analysis of the results of the weight value of the goods carried and the speed of the motor that can affect the strength of the frame on the box.
The new box design concept is made more practical and makes it easier for drivers to fold the box when there are passengers so that passengers feel comfortable when riding a motorcycle. Figures 4, 5,  6 show the concept designed to be flexible and easy to process. After designing the box, the maximum stress test was conducted on the Inventor yield strength software, from the data obtained as shown in Figure 7 which shows the value of 275 MPa which is the maximum stress that can be received by aluminum material used in the frame box and the ultimate tensile strength is achieved at 310 MPa. It is shown that the maximum force allowed to be applied in the box design is 275 MPa to avoid the aluminum material on the frame is damaged.    Figure 7 shows the stress that given to part XX, XY, XZ, and figure 8 on the part YY, YZ, and ZZ still accepted by the product made by aluminum. Color that is shown in figure 7 and figure 8 shows that the product has not damaged when received stress on several sides. The blue color on the product is indicated the product in good condition, if the color turns into the red it means the stress that given to the product is over so that it can cause damage to the product. Figure 7 shows the stress that received on the side of the product, figure 8 shows the stress that received by-product on the backward

Conclusion
From the results of the analysis carried out for design, 3D modeling, and stress analysis, using stress analysis inventor yield strength software. it can be concluded that by making designs that can be folded could make it easier for motorists in the assembly and disassembly process of the box, and for the maximum stress analysis performed shows a value of 275 MPa which is the maximum stress that can be accepted by aluminum material used in the frame box and for ultimate tensile strength of 310 MPa to be able to show the maximum force before the aluminum material in the frame is damaged, in this case, the maximum load conditions that can be placed in the box are 50 kg for a speed of 72 km/h. As a result, a new design of the box frame is the potential to be applied for motorcycle online transportation.


s2fieldsofstudy:
Business
Computer Science

text:
Comparative Empirical Study on CAPM and Stock Price Prediction by BP Neural Network in Shanghai’s A-share

Capital Asset Pricing Model (CAPM) is aimed to quantify the relationship between risk and expected rate of return. It has been tested and improved constantly by a number of researchers both abroad and in China. This study achieves a comparative empirical test of CAPM on E-V model, E-S model and GLS model in Shanghai A-share market. However, all of three model cannot adapt to Shanghai A-share market wonderfully. Now, computer science is a new instrument that can be applied to describe the capital market. Therefore, as a comparison of results, we select a single stock and use the simplest neural network, BP neural network with algorithms to optimize the weights and thresholds between the network layers for prediction. The results show that machine learning is a good method for stock prediction compared with only considering a single factor, which should be further explored in our future learning.

INTRODUCTION
The Capital Asset Pricing Model (CAPM) is hailed as one of the cornerstones of modern financial theory. Since its creation, the CAPM model has undergone multiple tests. The results of early empirical tests are consistent with CAPM (Black, Jensen, Scholes, 1972) [1], it is believed that there is a significant positive correlation between the average stock return and the estimated β value. However, in the subsequent test, Campbell, Andrew, and McKinley (1997) examined the data of the US stock market for the 30 years from 1965 to 1994 and came to the conclusion that the CAPM was violated. (Meng Qingshun, 2006) [2]. In recent years, China is also committed to studying the effectiveness of CAPM in China's stock market. Chen Xiaoyue, Sun Aijun (2000) [3] selected 12-month stocks, and used group identification and interval regression tests to deny the effectiveness of CAPM in China's stock market. Jin Yunhui, Lin Liu (2001) [4] selected about 500 stocks for three years to test. finally, the linear relationship between the stock return and the β value is finally denied. Most of the early researches have denied the effectiveness of CAPM in China's securities market, but its existing data are limited, the time span is too short, the stock market is affected by policies, and the research methods are single. Here, the weekly closing price of 400 stocks during 851 weeks is selected to calculate the 850-week return data. The β factor is regressed according to the EV model, ES model, and GLS model, and an empirical test is performed to compare the applicability of three models based on the test results. Comprehensive analysis of the effectiveness of CAPM in China's securities market based on the results of three model tests. Because stock prices are often affected by many factors, and as mentioned above, the CAPM is mainly used to study the relationship between expected returns and risky assets, and as a consequence, the prediction results obtained by considering only a few factors are often relatively large. Machine learning has enormous advantage in processing financial data. It can accurately analyze the changes of a large number of stock data or financial data during a period, and quickly draw corresponding conclusions, which significantly improves the operating efficiency of the financial market. In stock market, it can use the relevant characteristics of stock price index to achieve stock price trend prediction [5].

Empirical Testing Method
In this paper, we use data in 851 weeks, from June 21, 2002 to March 29, 2019. The average repurchase rate of Treasury Bonds is converted into the weekly interest rate as the risk-free interest rate, and the Shanghai Stock Exchange A-Share composite index is taken as the market rate of return. From Shanghai Stock Exchange A-Share, 400 stocks with complete weekly closing prices is picked out randomly, and the rate of return in 850 weeks can be obtained through calculation. The common form of CAPM is = + − # 1 is the rate of return of asset , is the rate of return of the market portfolio, is risk-free rate of return. is used to assess the systemic risk of risky assets. With the deepening of research, scholars put forward different methods to calculate it. Here, we use the three model below. In GLS Model, here are stocks in a portfolio, is a × 1 matrix of , is a × 1 matrix of − , is the covariance matrix of the stocks. Based on the testing method of Fama-Macbeth [6] and the article written by Ali Jahankhani [7], we make empirical test on the above models. In this paper, the number of portfolios is 20, and each of them include 20 stocks. Besides, the data in 850 weeks are divided into 15 periods for research, each of which is divided into three stages: portfolio formation, estimation period and test period, as shown in Table 1.
Here, , shows the rate of return of the investment portfolio p in stage t (Test Period), that is, the weighted average value of the rate of return of each stock in the portfolio in stage t. , −1 is the value of portfolio p in stage t − 1(Estimation Period). (ε , −1 ) is the non-system risk.
is the number of samples in stage − 1 , and here = 50. , −1 is calculated by equation (1). , is error item related to the expected rate of return. There are four hypotheses of testing CAPM: H1: E(γ 2t ) = 0. If the hypothesis is held, it refuses the linear relationship between the rate of return and 2 , that may support expected rate of return and risk are linear; H2: E(γ 3t ) = 0 . If the hypothesis is held, it means unsystematic risk has no influence on expected rate of return; H3: E(γ 1t ) = ( ) − > 0 . If the hypothesis is held, the equation part represent that in regression function is in accordance with CAPM, and the inequation part means high risk will lead to high expected return; H4: γ 0t = . If the hypothesis is held, the constant in regression function accords with CAPM.
Then, under three model of CAPM, data in 15 periods is regressed. And the above four tests are tested at the significant level of 0.05.

The Example of E-S Model
Here, take the linear regression analysis of E-S model as an example. The results of stepwise linear regression are shown in Table 2. In Table 3, and ( ) are used to test H3. γ 1t = ( ) − is refused in 6 regressions, and looking at , the numbers of positive items and the negative ones have no obvious difference. These tell us γ 1t and ( ) − are neither close to each other nor obviously performing that one of them is greater than another. Besides, only one regression not only refuses γ 1t = 0 but also accepts γ 1t > 0. The actual data indicates that high risk may not be repaid by high return. In addition, and ( ) are calculated for testing H4. It is figured out that H4 is refused by 4 regressions, which means γ 0t and are not in strong accordance. And they neither have obvious greater or lesser relationship.

The Comparison of Three Models
Similarly, E-V model and GLS model are tested. The comparison of hypothesis testing result is shown in table4. Besides, they all tell γ 1t and ( ) − , γ 0t and have no greater or lesser relationship. Table 4 The comparison of the support proportion of each hypothesis The testing result of H1 and H2 shows that the linear relationship between expected rate of return and β 2 or ε can be refused under each of the three models. In addition, the result of H3 not supports positive relationship between high

Advances in Social Science, Education and Humanities Research, volume 428
risk and high return. Overall, E-S model is more accordant with CAPM than others, especially for hypothesis H3 and H4. Because theoretically depending more on , it can describe the influence of risk-free rate better. However, working on portfolio, with the acceptance proportion of most assumptions that not even higher than 80%, E-S model still cannot adapt to Shanghai A-share market wonderfully. Individual stocks, on the other hand, are more flexible and volatile, making it more difficult to be accurately predicted by E-S model.

Discussion
To find out the reason why all of three model not fit the real market, we analyze the limitations of CAPM from the perspective of investors and the securities market.

Investors
Firstly, against CAPM's assumptions, investors in reality are not completely rational. The psychological deviation of investors in decision-making will lead to a high overall price-earnings ratio, a high market turnover rate and a large market volatility. High price-earnings ratio affects the expected return, which in turn affects the value of obtained through regression. The high turnover rate and large market volatility will lead to a weakening of the applicability of the model. Secondly, it is not realistic for investors to focus on the return of the asset portfolio for only one period. It is relatively reasonable that investors should also pay attention to the risks and opportunity costs caused by time [8], which also reduces the risk to a certain extent.

Securities Market
Firstly, compared with western securities markets (such as the US securities market), China's securities market started relatively late. At a high-speed development stage, it is currently greatly affected by government actions and policies. Secondly, the prices in China's securities market have not yet fully reflected all valuable information. Insider information holders (including major shareholders, senior managers and fund managers, etc.) often get excess profits, which reduces the effectiveness of the market. Thirdly, Siquan Zheng [9] pointed out that the non-systematic risk of China's securities market is high. This is due to the existence of some institutional defects in China's securities market, such as the inability of state shares and legal person shares to circulate and the lack of risk-avoidance tools. The non-statutory and transparency of the amendments to these systems are not high, resulting in system uncertainty. Combined with our regression results, in these three models testing process, due to the large non-systematic risk and small value, more than half of the regression models shows a better F-test result when taking ε into consideration. Hence, the large systemic risk also affects the applicability of the capital asset pricing model in China's securities market to a certain extent.

Model
The structure of the BP neural network is shown as Figure 1 [10]. The input layer consists of the opening price, the maximum price, the minimum price, turnover and the closing price of the stock. And the output layer is the closing price of the next day. During the training process, genetic algorithms are used. The number of populations is the sum of the weights and thresholds from the input layer to the hidden layer, and the hidden layer to the output layer. The norm of the error matrix is used as the output of the fitness function. After selecting, crossing, and mutating, a new population is obtained, and then the weights and thresholds of the neural network are updated, which attempts to get an optimal solution.

Experience
The data of China World Trade for stocks with the code of 600007.SH was selected, and a total of 1,000 days from Advances in Social Science, Education and Humanities Research, volume 428 January 4, 2011 to February 17, 2015 (after excluding days without transaction information) were selected. The data set is divided into a training sample set and a test sample set according to 4:1, the previous 800 days were used as training sample sets. The next 200 days were used as the test sample set. The forecast data for the last 200 days is shown below.
When the number of trainings is 2000, the training target is 0.001, the learning rate is 0.2, and the number of hidden neurons is 5, the prediction result is shown in Figure 2 and Figure 3. The mean square error of the test sample set before and after optimization is 0.866964 and 0.216173.

Figure 2
Comparison of prediction results and actual results using BP neural network before optimization

Discussion
In the above two trainings, we find that after increasing the number of hidden neurons and training times, the mean square error of the prediction reduce both before and after optimization. Compared with CAPM, it seems smarter, and it can also be seen from the graph that more historical data will raise better short-term prediction effect. Nevertheless, The choice of learning rate often does not have a clear pattern on the effect of results, we can choose different values to train a few more times to achieve a better result.

CONCLUSION
In the aspect of CAPM empirical test, we work out that all of E-V model, E-S model and GLS model cannot adapt to Shanghai A-share market. These models are even not able to describe the trend of portfolios, which, to some extent, neutralize the non-system risk. Therefore, the future price of only one stock predicted by CAPM is more unreliable. For one hand, the internal cause is the limitation of CAPM itself, which relates to its presuppositions and the model simplification. For another, the external causes are that real investors are not so rational as they are expected to be, and that China's stock market is not completely efficient. There Advances in Social Science, Education and Humanities Research, volume 428 exists many effects surrounding the capital market, such as the government or policy intervene, the manipulation of insider information holders and its instability as a developing market. However, stock price prediction still draws widely attention, since its relationship to investors' return and economic level. As the rapid development of computer science, machine learning provides new methods to predict stock price. In this experiment, we only used the simplest BP neural network for stock price prediction. In the study of a lot of historical data, the results show that We can well predict the stock price in the short term in the future. Therefore, when more complex networks are established, We are also confident of looking forward to better results.


The impact of manager’s demographic characteristics on prospector strategy, use of management accounting systems and financial performance

Managers need the support of management accounting system (MAS) in making decisions and controlling activities. This article investigates the factors of CEO's personal characteristics that influence the choice of an appropriate prospector strategy and the intent of using MAS information. Besides, the moderating role of internal locus of control (ILOC) in such relationships was also examined in this research. The sample in this study includes CEOs of large and mid-sized companies in Vietnam. To test the hypotheses, we used the PLS-SEM method. The results indicate that all the effects are significant, except for the direct impact of age on financial performance and the moderating role of ILOC between education and the choices of prospector strategy. This study contributes to the accounting research field by confirming the role of managers’ characteristics, recognizing the important role of MAS and helping managers in designing MAS suitable with the specific characteristics of CEO. As a result, managers can operate firms more effectively.

INTRODUCTION
Accounting information is useful information helping managers in their decision-making (Demski, 2013). MAS is a sub-control element of "an overall organisational control package" (Otley et al., 1995, p.421) and a controlling & planning system that concentrates on reducing expected performance gaps before implementing the strategy and in the process of implementing the selected strategy (Downs, 1967). Therefore, MAS information is extremely important for the managers implementing strategies. However, the extent of MAS information use by managers in making decisions related to strategy implementation and performance improvement depends on several factors and also on the general context (Chenhall, 2003).
Demographic characteristics have an essential role not only in the results obtained from making managerial decisions but also for the level of success in implementing strategies (Jespersen & Bysted, 2016). Some researchers have studied how technically oriented education of top management impacts on their style of using interactive MAS (Wijesinghe & Samudrage, 2016). Others have studied how younger generation of managers with a different education is using innovative MAS in their strategy building (Naranjo-Gil et al., 2009;Wijesinghe & Samudrage, 2016). However, in an emerging market such as Viet Nam, with rational resources in many perspectives, the use of innovative MAS is not suitable but traditional MAS is rather used instead. However, research studies concerning age and educational background of Vietnamese managers and their impact on the use of traditional MAS is very limited (Hiebl, 2014). In addition, studies investigating the use of MAS information are often not concerned with the financial performance achieved by organization (Naranjo-Gil & Hartmann, 2006;Wijesinghe & Samudrage, 2016). Notably, in Hambrick (2007) two moderator variables were updated in UET, but more recent studies have not really paid attention to this. For example, Hambrick & Finkelstein (1987) indicated that ILOC has a moderator's role, but has not been interested in management accounting research (Wangrow et al., 2015). Hambrick & Mason (1984) were the first to introduce UET. Then, UET was reinforced and supported by Hambrick & Finkelstein (1987), Hambrick et al. (2005), Hambrick (2007) and Hiebl (2014). Recently, in the field of management accounting, UET has been used more actively but still, in a very limited scope (Hiebl, 2014;Zor et al., 2019). This study examines two characteristics (education background and age) that affect the choice of strategies, using MAS information and financial performance of businesses. The moderating role of ILOC is also considered.
This research contributes to management accounting literature in many different ways. In the proposed framework, the authors examine the linkages between age and education background, MAS information use, the choices of prospector strategy, and financial performance for empirical analysis. The ILOC's moderating roles on some relationships are also examined. We used PLS-SEM to test the hypotheses (Wold, 1980). The measurement model and the structural model are assessed during the analysis process through two steps.
The next two sections present the background theory. Based on that, the theoretical model is presented. These sections end with the hypotheses to be tested. Following are the results and discussion. Limitations and directions for further studies are presented in the final section. However, the method of measuring values and perceptions is complex and inaccurate. As a result, observation and measurement based on upper managers' demographics characteristics is used as an alternative method (Hambrick & Mason, 1984). Managerial discretion has been updated for UET (Hambrick, 2007;Hambrick et al., 2005) and the relationship between upper managers and strategic choices/outcomes is moderated by managerial discretion.

Demographic characteristics of managers
The age of managers (Colombelli, 2015) and their education background (Damanpour & Schneider, 2006) are considered to be the easiest factors to be observed and measured but they have a strong influence to the behavior of managers. More specifically, self-confidence, information use, risk-taking ability in decision making of upper managers are influenced by their age (Hambrick & Mason, 1984). The education background is considered as a representative factor to build the psychology, which influences the way upper managers are awarded by the world around them and have appropriate information processing (Finkelstein et al., 1996;Hambrick & Mason, 1984). The difference in academic background between business-oriented senior executives (economics, business, law, etc.) and other Non-business-oriented administrators (e.g. like engineering, physics and medicine, etc.) explains why many senior executives know the problems, have more knowledge or have better ways to solve the problems (Song, 1982).

Prospector Strategy
Research by Miles et al., (1978) divided organizational strategies into four categories: Defender, Prospector, Analyzers, and Reactors. Prospectors with characteristics like to respond quickly to environments, create and exploit new product and market development. They are also more dynamic than organizations in the same industry. During the analysis of each strategic trait, Miles et al., (1978) evaluated prospector strategy with the highest level of risk because this strategy needs a lot of corporate resources, and if the business does not control and not provide good supervise observation the financial harm will be potentially occurred.

Management accounting system (MAS)
Organization's outcome, a complex administrative or an aspect of organizational structure are characteristics of MAS (Chenhall, 2003;Chenhall & Morris, 1986;Strauß & Zecher, 2013). MAS has four Vo Tan Liem, Nguyen Ngoc Hien The impact of manager's demographic characteristics on prospector strategy, use … dimensions including broad scope, timeliness, aggregation, and integration (Chenhall & Morris, 1986;Liem & Hien, 2020). In prospective of scope, MAS would not only contain helpful information helping managers run organization, financial information in nature and essential history but also provide information that is external, non-financial, and future oriented. Characteristic of timeline information related to the frequency of reporting and rapid feedback (Liem & Hien, 2020). The aggregation of information according to functional areas is characteristics of aggregation information, and finally, integration relates to the information sharing between sub-units (Liem & Hien, 2020). UET believes that the use and design of a MAS can be expected to be influenced by the characteristics of the upper managers.

Internal Locus of control (ILOC)
ILOC is defined by the latitude of managerial action of decision maker (e.g., a top manager) in a known circumstance (Hambrick & Finkelstein, 1987). Leaders will have a wider range of options when they have higher discretion (Campbell et al., 2012), with a broader latitude of action (Hambrick & Abrahamson, 1995). Demographic characteristic of CEO, choice of strategy and a complex administrative system are moderated by managerial discretion's role (Liem & Hien, 2020). Reflecting managerial discretion by individual factors are less studies (Wangrow et al., 2015). This study concentrated strategic behaviour (Powell et al., 2011), so characteristics of individual level is chosen to represent for managerial discretion. In characteristic of CEO, locus of control (LOC) is the psychological characteristics of managers that have an impact on their decision (Hambrick & Finkelstein, 1987;Liem & Hien, 2020). Two components in LOC are: internals and externals of LOC. CEOs have propensity with ILOC believes their own actions and efforts will affect to their livesand these characteristics will explain behavirors in strategic leadership (Liem & Hien, 2020). From above reasons, this research chooses ILOC as a modesrate variable between characteristics of CEO and using MAS, choices of prospector strategy.

Age, education background and choices of prospector strategy
When age rises, flexibility falls. Besides, conservatism and resistance to change rise (Wiersema & Bantel, 1992). Upper managers are less oriented towards big changes in organizations because of great level of risks. Especially, managers of higher ages are not capable for risky strategies (Seo & Sharma, 2018) because they have been reduced their ability to assess new ideas quickly and integrated to make decisions to actions (Hambrick & Mason, 1984). In contrast, younger upper managers have propensity to take risk and tend to implement new projects that can promote their careers in the future (Finkelstein et al., 1996). Upper managers with younger ages are more suited to higher risk strategies (Hambrick & Mason, 1984). Therefore, lower age CEOs will favor to choose a risky strategy like prospector strategy (Miles et al., 1978).
H1a: The lower age of CEOs has positive impact on choices of prospector strategy. Based on UET, the strategies selection in the typologies of Miles et al. (1978) will be influenced by the CEO's education background (Hambrick & Mason, 1984;Pavlatos, 2012). A person with technically oriented education background generally has a different cognitive base compared to a person with business oriented (Hambrick & Mason, 1984). However, the ability to excellently plan finances and effectively control business resources tends to favor managers with a business orientated background education (Naranjo-Gil & Hartmann, 2007). In addition, they can also offer a good use of accounting information from a system administrative complexity (Rasid et al., 2014). So that, with business orientated education background, CEOs will have the ability to control risks better than uppers managers with non-business orientated education background. The above characteristics of business-oriented managers are essential for the choosing and pursuing of a risky strategy. As a result, uppers managers with business orientated education background will have more confidence and assertiveness in choosing prospector strategy.
H1b: CEOs with the more business orientated education background have positive impact on choices of prospector strategy.

Age, education background and use of MAS information
The higher the age of the managers, the lower the ability to use information and confidence to make decisions. Although, the searching for better information and the assessing the accuracy of information is better. At the same time, higher age CEOs take more time to make decisions (Taylor, 1975) so they don't need accounting information timely, broad scope, integrated and aggregated because they need long time to carefully consider before making decision, and they are more suitable to style of interactive use of MAS (Wijesinghe & Samudrage, 2016). In the same view, decision-making, controlling and performance evaluation is significant negatively associated with CFO age (Pavlatos, 2012). In addition, to react quickly and create profits for businesses, CEOs must take risks in the decision-making process. This risk-taking is appropriate for CEO lower age so they are more risk management implementation (Lee & Moon, 2016). Four perspectives of MAS have positive relationship with risk management activities (RMA) (Rasid et al., 2014). Besides, MAS provides information that is broad in scope, integrated, aggregated and be provided timely (Chenhall & Morris, 1986).

H2a: The lower age of CEOs has positive impact on using MAS information.
Most upper managers are trained and provided with basic knowledge in the areas of accounting and finance, or generally the business sector. However, there are still differences in using MAS information to implement the strategy, which depend on the level of the main academic background that they have prepared for their career (Hiebl, 2014). Managers with a business orientated are more confident to use of financial information. Since, this type of MAS information related with administrator of organizations (Finkelstein et al., 1996;Song, 1982). Besides, with perceived environmental uncertainty, the timelier, integrated and aggregated accounting information helps managers timely response to the fluctuation of environment (Chenhall & Morris, 1986). In contrary, managers with the propensity of technical orientated are positively related to use MASs with interactive style, positive use of innovative MASs (e.g. ABC, BSC, benchmarking) (Naranjo-Gil et al., 2009) and negative influence on using traditional MAS (Wijesinghe & Samudrage, 2016).
H2b: CEOs with the more business orientated education background have positive impact on using MAS information.

Age, education background and financial performance
Higher age managers have less tolerance for both health and well-being (Child, 1974) or at least they are not good in creating new ideas or learning new decision-making behaviours (Chown, 1960). In contrast, lower age managers try to create new things, solve problems and make decisions in a new way, as well as accepting risks, which lead to a sudden financial change of organization (Hambrick & Mason, 1984). Therefore, business performance is much higher or lower than the industry average because it depends on the success of the pursuing strategy (Hambrick & Mason, 1984). In summary, lower age managers may be more taking risk, fast to accept new ideas and take actions, in the other face, higher age managers have more experience and more time to develop powerful parties. All of these advantages are necessary for decision making to achieve high performance. A recent research indicated that the higher age managers have positive impact on firm performance (Wei et al., 2005).
H3a: The higher age of CEO has positive impact on firm financial performance.
The researches in medical, managers 's medical education toward take care and improve health for patient rather than the improvement financial performance of overall organization (Kurunmäki, 2004). Top managers with a non-business orientated education are more favour with the technical and operational process of the organization than managers with business orientated education. They are good at improving the content of processes (Finkelstein et al., 1996) and less towards improving the efficiencies of finance (Song, 1982). They also little focused on gains controls and efficiencies. In contrast, inclined toward the detailed control of outcomes of activities and processes has a positive relationship with manager in business education (Armstrong, 1987), because of the confidence, rely on distant and financial controls when managing the organization (Finkelstein et al., 1996;Song, 1982).
H3b: CEOs with the more business orientated education background have positive impact on firm financial performance.

Choices of prospector strategy and financial performance
Based on UET, the influence of choices strategy on performance is clearly shown. Therefore, there is a sufficient ground to indicate that a specific strategy like the choices of prospector strategy impact on firm financial performance. However, several studies had conflicting results about the choices of prospector influence on financial performance. Some researchers indicated that both financial performance and nonfinancial performance is influenced by prospector strategy (Köseoglu et al., 2013). In contrast, choices of strategy do not statistically significant influence financial performance (Chong & Chong, 1997). Starting from these conflicts, however, relying on a positive mind set, choosing an innovative strategy like prospector strategy to meet the uncertainty environment impact on performance.
H4: The choice of prospector strategy have positive impact on firm financial performance.

Choices of prospector strategy and use MAS information
Organizations pursuing prospector strategy, information on a wide range of issues and timelier, integrative, aggregated and broader information areuseful for day-to-day decisions (Bouwens & Abernethy, 2000;Chenhall & Morris, 1986). Besides, to face with prospector strategy's risk mess, risk management activities is supported by MAS information (Soin & Collier, 2013). In risk measurement, the more aggregated, reported and monitored about the risk, the more important roles of MAS, because in decision making process, MAS provide information for managers help them reduces the uncertainty (Winter, 2007). Risks deal with future events while broad scope MAS, which are future oriented, timely information, are required to respond to risk. Moreover, data integration is a key advantage for risk management, aggregated information summarizes informationthat expedite decision-making process (Rasid et al., 2014). The impact of certain decisions can be determined.
H5: Choice of prospector strategy haspositive impact on use MAS information.

Use MAS information and financial performance
Financial performance is impacted directly by using MAS information. Manager's knowledge can be having from MAS information when information can be processed and converted into knowledge of managers. At that time, for competitive advantages, MAS information becomes a unique strategic resource of managers. MAS can support managers' decisions to response to dynamic of environmental such as predicting the change of customer's need, facing to competitor's actions, continuously launched new products (Chenhall & Morris, 1986). MAS is not only one part of management control system (Collier, 2015) but also "can be used to compare and contrast the results obtained by each activity" (Otley, 1980, p.64) "when implementing the strategy, thereby promoting the search for the wrong cause" (Downs, 1967), helps managers making decisions to change the allocation and the use of resources (Collier, 2015, p.39). So, with the useful features of MAS information help CEO to make decisions, that enhance the organization's performance.
H6: Using MAS information has positive impact on firm financial performance.

The role of Internal Locus of control
To have better in coping with complex and unstable environments, CEOs need higher ILOC (Miller & Friesen, 1982) so that performance degradation will be minimized because they manage better in emotional exhaustion, reducing depersonalization, and increasing professional (Sirén et al., 2018). Moreover, with the difference of leadership abilities, CEO can use persuasion more often to influence others' behaviour, and they trust in their capacity to influence the environment (Boone et al., 1996). In Wangrow et al. (2015)'s review indicated that because of the riskiness of this strategy, the more firms' choice and pursuing prospector strategy, the more managerial discretion. Therefore, the more the confidence of CEO, the more the prospector strategy is choiced by CEO. Based on H1a, H1b; as lower age of CEO and education background of CEO are business, CEO increases choices of prospector strategy so that relationship between lower age, business orientated education and choice of prospector strategy greatly increases when ILOC of the CEO increases.
H7a: CEO with the more ILOC propensity, lower age impact on choice of prospector stratey will be stronger. H7b: CEO with the more ILOC propensity, business orientated education background impact on choice of prospector stratey will be stronger.
When executing the selected prospector strategy, control system to control whether or not the work is being done according to the plan and timely action if work didn't like they expected. However, this system must suitable with their characteristics. When information is relevant and when it is not useful for managers oriented on ILOC (Lefcourt, 1982). Between different parts of the organization, the ability of MAS can be synthesized and integrated information (Collier, 2015, p.42). So, MAS is seen as a preeminent technology from the creation of strategies to implementing them and providing clear accountability for integrating diverse activities (Otley et al., 1995), also we can say that if the more increase of ILOC's moderate role, MAS information is perceived to be useful and relevant by CEO. Based on hypothesis (H2a, H2b), above, as lower age of CEO and education background of CEO are business orientated, CEO increases using frequency of MAS information so that the relationship between lower age, business orientated education and using MAS information increases when ILOC of CEO increases.
H7c: CEO with the more ILOC propensity, lower age impact on using MAS information will be stronger. H7d: CEO with the more ILOC propensity, business orientated education background impact on using MAS information will be stronger.

Measurement
The exact age is used to measure the age of CEO (Pavlatos, 2012). The measurement of Naranjo-Gil et al. (2009) is used to measure CEO's educational background. Each CEO was asked to state their educational background in years of studies (college, bachelors, master degrees and courses attended) in two streams: (1) business orientated and (2) non-business orientated. Following, the variable "educational background" is measured by dividing the years studied in business programs with the total years of studies.
The MAS is adapted from the scale of Chenhall & Morris (1986), Agbejule (2005). As such, CEO was required to indicate the rate "extent of use" of MAS information in four perspectives consist of integration (four items), broad scope (four items), aggregation (three items) and timeliness (four items) in each organization. Prospector strategy is measured using the scale of 1-7 from (Segev, 1987). This research based on Mueller & Thomas (2001)'s measurement of LOC was adapted from the scale of Rotter (1966)'s. Firm performance is adapted from the scale of Calantone et al. (2002). This measurement scale includes five indicators: Return on sales (ROS), Return on investments (ROI), Sales growth, Return on assets (ROA) and Overall profitability.

Sample and data
This study is conducted in Vietnam, an emerging economy. A data set of 234 large and medium firms is featured. To have enough characteristics of CEOs and firms. E-mail surveys are then distributed to CEO from different Vietnamese business organizations.
CEOs of business organizations in Vietnam are chosen to send e-mails surveys. Based on e-mail addresses is collected from websites of businessperson association, department of planning and investment, and websites of businesses. 2,500 surveys were distributed to CEOs. An invitation letter with a link to the web-based survey was sent to CEOs' email addresses. This method has weaknesses of response errors (respondents provide inaccurate data) and non-response errors (target respondents do not reply). Nonresponse may cause a problem in the estimates of a survey if there are systematic differences between respondents and non-respondents. To deal with this problem, appropriate procedures were carried out, such as contacting the right person, following-up by sending reminder emails. Reminder mail was sent to the respondent every 2 weeks for 2 months after sending, in order to encourage feedback. Out of the 562 managers who clicked on the link to participate, only 317 answered the questions; 287 out of these 317 participants completed the questionnaires and submitted them; 38 responses from CEOs in small enterprises were excluded; 15 missing cases were removed from the data. Finally, 234 cases were used for data analysis providing a response rate of 41.8% (234 eligible responses/562 cases agreed to participate).

EMPIRICAL RESULTS
We used SPSS24 for basic statistical analysis. To be primarily for path analysis, we used Partial least squares-structural equation modelling (PLS-SEM) (Wold, 1980).

Respondents Profile
A data set of 234 large and medium firms is featured. The sample is restricted to this kind of firm only as it possesses sufficient financial resources to operate and the need to design a management accounting system. Source: Own development Table 1 show that, the final sample reflects 63 percent of male CEOs and 37 percent of female CEOs. CEOs have an average working time of 12 years. This shows that they have enough degree of influence over the organization in which they are working. The sample comprises 8 percent pre-undergraduates, 53 percent undergraduates, and 40 percent post-graduates. it shows most CEO education background are undergraduates. The age ranges from 30-64 for the highest percentage of the respondents.

Measurement model
Harman's single factor test was employed to evaluate the common method bias. All measurement constructs, we accompanied Un-rotated factor analysis (Excluding education and age) extracted 7 factors. The first factor accounts for 35.12% (<50%) of the total cumulative variance of the 7 factors is 76.6%. Thus, the Common method bias in this study is not a serious problem (Podsakoff et al., 2003).
Internal reliability is used to assessed scale reliability, Cronbach's alpha and rhoA is used to evalue composite reliability (Dijkstra & Henseler, 2015;Nunnally & Bernstein, 1994;Sarstedt et al., 2017). Outer loading of items greater than 0.7 and AVE of contructs greater than 0.5 are acceptable (Hair et al., 2016) are conditions of convergent validity. Bootstrapping 5000 timses was performed to test the results of the measurement model and to test the research hypothesis (Hair et al., 2016). The test results of reliability and convergence value in Table 2 show that the cronbach's alpha, Composite reliability coefficient is in the range [0.7; 0.95] and rho coefficient greater than 0.5. This shows Vo Tan Liem, Nguyen Ngoc Hien The impact of manager's demographic characteristics on prospector strategy, use … the scales for measuring the concepts in the model to reach reliability. the AVE and outer loadings values are used to evaluate the convergence value of the scale. ROS variable has outer loading = 0.46 <0.5, so remove this variable from the Financial performance. The test results show that the AVE value of the scales are greater than 0.5 and the outer loadings of the observed variables are greater than 0.7, so the scales reach convergence value (Figure 3).
Cross-loading factor coefficient, HTMT ratio and Fornell-Larker criterionis is used to evaluate discriminant values of concepts (Dijkstra & Henseler, 2015;Hair et al., 2016). Test results cross-loading show, Cross-loading coefficient in its structure is much larger than in other structures. Besides the results of Fornell-Larcker criterion (Table 3) and factor HTMT (

Structural model
Variance-inflating factor (VIF) is used to evaluated multicollinearity between independence variables. The scale of Prospector strategy had VIF = 0.253 (lower 0.5), so it appeared multicollinearity but trivial, can be acceptable (Hair et al., 2016). The remaining scales have VIF<2, so they didn't appear to be multicollinearity.

Figure 3. The results of theoretical model analysis by PLS-SEM
Source: Own development R 2 value is calculated to assess the predictive capability of dependence variables (Hair et al., 2016). The results in table 5, R 2 value of MAS information is 0.575, Prospector strategy is 0.475 and Financial performance is 0.493, which can be considered to have an enough level of prediction (Hair et al., 2016). Besides, predictive relevance (Q 2 ) is also used to evaluate the out-of-sample predictive power. The factor Q 2 of dependence variables is greater than 0. Consequently, it supports the predictive power of the model. Results of testing the theoretical model with bootstrap 5.000 times in model PLS-SEM show, exclude the relationship between Age and Financial Performance is not statistically significant. Besides, the remaining relationships in the theoretical model are statistically significant with reliability greater than 95% (Table 5). Testing the moderator of Internal Locus of control is a major objective in this research. To test the role of the moderator variable, first, evaluating the measurement model in the presence of the Internal locus of control variable. The results indicate that the measurement concepts are reliable and valuable. Next, to assess the impact of the moderator variable, the two-stage approach (Chin et al., 2003) was applied. The first phase was used to estimate the main impact model, while the second phase multiplied the moderator variables and exogenous variables to measure interaction terms (Age x Internal locus of control; Edu x Internal locus of control; Age x Internal locus of control; Edu x Internal locus of control).  Table 6 shows that the ILOC does not play a moderator role in the relationship between Education and Prospector strategy (p-value = 0.203 > 0.05, Reject hypothesis H7b -See table 6). The remaining hypotheses are statistically significant (p-value < 0.05).

Theoretical implications
In this study, some theoretical implications as follows: Firstly, the impact direct between demographic characteristics (age, education) of CEO and choices of prospector strategy (Hambrick & Mason, 1984;Song, 1982;Wijesinghe & Samudrage, 2016), MAS (e.g. Wijesinghe & Samudrage (2016);Naranjo-Gil et al. (2009);Pavlatos (2012), financial performance (Hambrick & Mason, 1984;Hiebl, 2014) was examined. However, the impact of age on financial performance is not significant. This result agrees with research of Hambrick & Mason (1984) and Bertrand & Schoar (2003) because they concluded that higher or lower performance depends on the success of chose prospector strategy. This study also supports for positive influence of MAS on financial performance.
Secondly, the moderator role of ILOC between age, education and choices of prospector strategy; use MAS information was tested, which had not been examined in previous studies. Although, the moderate role of ILOC of CEO in relationship between education background and choices of prospector strategy is not supported but this study provides. Further, through empirical evidence, the result in this study indicated the importance role of CEO's ILOC in decision making choices of prospector strategy to enhance the financial performance of firms in an emerging economy like Viet Nam.
Finally, this study helps to reduce the shortage of researches at age, education of CEO/strategy/accounting interface (Hiebl, 2014;Naranjo-Gil & Hartmann, 2006Pavlatos, 2012;Zor et al., 2019). More obviously, model representsthe combination between age and education of CEO, a personality characteristic (ILOC), and a marketing variable (choices of prospector strategy) and an accounting variable (the use of MAS) in the theoretical model.

Managerial implication
The model of Hambrick and Mason was reinforced by this study 's result. So, this study also confirmed the applicability of UET in Vietnamese environment. There are some managerial implication: Firstly, with the CEO 's ILOC, it enhanced: (1) the negative effect from age to the choice of prospector strategy, from age to use MAS information; (2) the positive effect from education background (business orientation) to the choice of prospector strategy, from education background (business orientation) to use MAS information. It showed that the demand for using MAS information is also affected by CEO's ILOC. Therefore, when designing MAS in a firm, beside CEO 's demographic characteristics, MAS designer need to pay attention to ILOC (a psychological feature). In addition, when the organization was determined to pursue the strategy prospector, this type of strategy must be suitable with CEO 's demographic characteristics (lower age and business orientation) and ILOC. Secondly, to gain competitive advantages, designing and using MAS will help to make implication the clearly way to implement it. In this view, CEO 's business orientation education background combine with suitable stratey (prospector strategy) is basic to make it become reality by enhance firm performance. Finally, two benefit from the results of this study are: (1) CEO 's designing and using MAS will be guidance and (2) towarding enhanced performance, prospector strategy should be suitable with CEO 's characteristics (age, education background, ILOC).