Mobile Stock Trading (MST) and its Social Impact: A Case Study in Hong Kong

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1 Mobile Stock Trading (MST) and its Social Impact: A Case Study in Hong Kong K. M. Sam 1, C. R. Chatwin 2, I. C. Ma 3 1 Department of Accounting and Information Management, University of Macau, Macau, China 2 Department of Engineering and Design, University of Sussex, Brighton, United Kingdom 3 Department of Finance and Business Economics, University of Macau, Macau, China Abstract - Smartphones are becoming the mainstream mobile devices used by Hong Kong residents. The popularity of smartphones has led to the emergence of a new way of trading in financial securities and products so called: mobile stock trading. This new technology has several attractive features that have driven this new market forward. Investors can place orders at anytime and anywhere without any geographic restriction as long as they can access the internet. By using the Diffusion of Innovation model (DOI) and related literature, we found that three factors: perceived usefulness, trialability and observability - contribute to the determination of customer attitudes in adopting mobile stock trading. The results revealed practical implications for the future development and implementation of mobile stock trading. Keywords Mobile stock trading, diffusion of innovation theory, smartphone I. INTRODUCTION As of 31 December 2010, the Hong Kong Stock Exchange ranks sixth in the world, making it the secondlargest stock exchange in the Asia Pacific region after Tokyo, Japan. In order to maintain its market share, it is necessary to increase the total transaction volume in this knowledge-based economy where the Internet and Communication Technology has been emerging and provides a flexible way for investors to perform stock trading. A Cash Market Transaction Survey [1] showed that 69 percent of the stock traders are online stock traders. The survey claimed that online traders have more frequent transactions compared to the non-online traders. In this new technology era, the introduction of smartphones has greatly altered the way people live and conduct their daily lives. According to the statistics obtained by the Radio Television of Hong Kong [2], 62% of Hong Kong citizens own a smartphone. Nielsen s Smartphone Insights Study [3] claimed that 76 percent of smartphone users in Hong Kong accessed the mobile internet in the 30 days up to the 20 June The study also found that in the 30 days up to the 20 th June 2012, Hong Kong had the third highest incidence of mobile apps usage (74%). Many financial institutions such as the Bank of East Asia have promoted their own systems and apps for electronic and mobile trading to increase their competitiveness. Nielsen s Global Survey [4] found that only thirty-one percent of global investors use mobile phones for investment transactions. A mobile Banking Perception Study [5] found that only four percent of Hong Kong respondents are currently using mobile stock trading. The survey results showed that using a mobile phone for investment transactions is still not very popular. The purpose of this study is to identify the factors contributing to the investors attitude in adopting mobile stock trading in Hong Kong. II. RESEARCH MODEL The research model is based on the Diffusion of Innovation Theory (DOI) proposed firstly by Rogers [6]. It aims to investigate to what extent new technologies and products spread through cultures. The model highlights five key characteristics of an innovation relative advantage, compatibility, complexity, trialability and observability. Rogers [7] claims that these five characteristics can affect individuals in innovation adoption. In this research, these five characteristics will be analyzed to find out whether they can affect a person s behavior intention in adopting mobile stock trading in Hong Kong. Our research model to explain the use of mobile stock trading is demonstrated in Fig. 1. Behavior Intention H 1 H 2 H 3 H 4 H 5 Perceived usefulness Compatibility Perceived ease of use Trialability Observability Fig. 1. The research model /13/$ IEEE

2 A. Behavior Intention Behavioral intention is adopted from the Technology Acceptance Model (TAM), which aims to investigate users attitudes toward using information technologies [8]. When a person has a favorable attitude towards certain behavior, he or she will have a stronger desire to carry out the behavior. Ajzen [9] points out that attitude that leads to a certain behavior is regarded as a function of the salient beliefs representing the behavior s perceived consequences. Furthermore, Ajzen and Fishbein [10] claim that the positive or negative characteristics associated with an object determine whether the attitude will be favorable or not. Hence, each factor is hypothesized for the relationship with the behavioral intention towards mobile trading adoption. The hypothesized relationships help to understand the behavioral intention of the investors towards mobile trading in the Hong Kong stock market. B. Perceived Usefulness Perceived usefulness is the degree to which an innovation is perceived as being better than the idea it supersedes. Rogers explains that it is the recipients perception of the advantages of innovation that is important. Moore and Benbasat [11] as well as Taylor and Todd [12] show that this idea is similar to the concept of relative advantage. Mariga [13] and Huei [14] claim that perceived usefulness is one of the significant determinants for the adoption of M-commerce services. Safeena et al. [15] and Jeong and Yoon [16] also found out that perceived usefulness affects the intention to adopt mobile banking positively. Thus, the hypothesis can be H 1 : Perceived usefulness has a positive effect on behavior C. Compatibility Compatibility is the degree to which an innovation is perceived as consistent with the existing values, past experiences, and the needs of potential adopters [7]. AL- Majali [17] found that compatibility significantly affects the attitude towards the internet trading service in Jordan. It is also the most influential factor in determining mobile payment acceptance [18]. Thus, the hypothesis can be H 2 : Compatibility has a positive effect on behavior D. Perceived Ease of Use Complexity is the degree to which an innovation is perceived as difficult to understand and use [5]. As Moore and Benbasat [11] and Taylor and Todd [8] suggested, the idea of perceived ease of use is just the opposite of complexity. Safeena et al. [15] report that bank customers adoption of mobile banking is more likely when it is easy to use. Jeong and Yoon [16] also performed a test on the significance of perceived ease of use on the intention to adopt m-banking. Their results showed that consumers prefer banking transactions which are simple, easy and fast in process and environment. Thus, the hypothesis can be H 3 : Perceived ease of use has a positive effect on behavior E. Trialability Trialability is the degree to which an innovation may be experimented with on a limited basis [7]. Many mobile trading systems now provide potential customers with a trial account for some limited period of time to try out the actual effectiveness of mobile trading. AL-Majali [17] concludes that trialability affects the attitude towards internet trading service significantly in Jordan. Thus, the hypothesis can be H 4 : Trialability has a positive effect on behavior intention to use mobile stock trading. F. Observability Observability is the degree to which the results of an innovation are visible to its adopters [7]. Since smartphones can be carried anywhere and people usually have their phones with them all day, people in the vicinity of the users can observe both visually and in an auditory sense [19]. They can observe how quickly and easily the users can place orders with smartphones. Safeena et al. [11] believe that social influence (observability) affects the use of mobile technology positively as people think that their image and status in society can be improved by using advanced technology. Thus, the hypothesis can be H 5 : Observability has a positive effect on behavior III. METHODOLOGY The purpose of the study is to analyze factors that have an influence on the adoption and use of mobile stock trading in Hong Kong. The data in this study were collected through a questionnaire survey, the first part of which consists of questions to obtain demographic information. The second part includes questions based on Roger s Diffusion of Innovation Model. The questions in the second part are designed to have a 5-point Likert type agreement scale ranging from one (strongly disagree) to five (strongly agree). It consists of six sub-parts which are: perceived usefulness, observability, compatibility,

3 perceived ease of use, trialability and behavioral intention shown in Table 2. The population used in this study is the stock investors working in different industries in Hong Kong. This study applied the method (1) suggested by Bowerman et al. [20] to calculate the sample size: 1 where equals sample size, equals the confidence level, p equals the estimated prevalence of mobile stock trading in Hong Kong and B equals the tolerance. For this study, p = 0.4 based on the rate of 31% in the Nielsen s Global Survey in 2012 [4]. Based on Bowerman et al. [20], who suggested, to achieve a normal distribution, = 1.96 by assuming confidence to be 95%, and error tolerance = 5%. Therefore, the sample size is determined to be 369. The questionnaire was distributed to different industries in Hong Kong via mail drop-off and approaches. On this basis, a sample of 545 usable responses was gathered from diverse respondents with different demographic characteristics. Descriptive statistics related to the sample are presented in Table 1. TABLE 1 CHARACTERISTICS OF RESPONDENTS Demographics Number Percent Gender Female Male Age < (1) > Education level Primary school degree High school degree Bachelor degree Master degree or above Industries Below HK HK HK HK Above HK In order to investigate the measurement model, there are two stages to follow. The first stage is exploratory factor analysis (EFA) applied to group correlated measures under the same factor. Results regarding EFA are demonstrated in Table 2 together with composite reliability (a) and factor loading values. According to factor loadings, it was found that the items of the survey are appropriate measures for their corresponding constructs. Additionally, Cronbach s alpha (i.e. reliability) values of all the variables were higher than This demonstrates that the internal consistency of the survey data is acceptable and reliable according to George and Mallery [21]. The second stage is a multiple linear regression model used to test the effect of perceived usefulness, perceived ease of use, observability, compatibility and trialability on the dependent variable behavioral intention. The results of regression are provided in Table 3. TABLE 2. FACTOR LOADINGS OF MEASUREMENT ITEMS Construct Measurement items Loading Perceived usefulness Using mobile stock trading will improve my performance on investment = The transaction cost of mobile stock trading is very low The speed of mobile stock trading is very high I can obtain more detailed and updated information through mobile stock trading Compatibility Mobile stock trading does not lead to significant changes in my life style = Mobile stock trading fits my needs Using mobile trading to place an order is the same as using telephone to tell a broker all transaction details Perceived ease of use It is easy to use mobile stock trading system to do what I want = Interaction with mobile trading site does not require a lot of mental effort Learning to operate a mobile trading platform is easy Trialability Before deciding to use mobile stock trading, I want to be able to properly try it out = Before deciding to use mobile trading, I want to be able to use its trial version to see what it can do Before deciding to use mobile trading, I want to be able to experiment with it as necessary Observability I am able to observe the outcome of mobile stock trading on people who use it = Observing the outcome of mobile stock trading from others motivates me to use mobile stock trading By viewing others using mobile trading, I would like to learn more about it Behavioral intention I will continuously benefit from mobile stock trading in the future = I will recommend using mobile stock trading to people around me I am in favor of improved functionality of mobile stock trading. 0.79

4 TABLE 3. RESULTS OF THE REGRESSION ANALYSIS Hypothesis Dependent variable Independent variable Coefficient value H 1 Behavioral intention Perceived usefulness ** H 2 Behavioral intention Compatibility * H 3 Behavioral intention Perceived ease of use * H 4 Behavioral intention Trialability * H 5 Behavioral intention Observability ** Note: *** 0.001; ** 0.01; * 0.05, R 2 = 81.7% IV. RESULTS Suppose our decision is made at an = 0.05 level of significance, the mentioned hypotheses will not be rejected if the value is < Overall, the results shown in Table 3 demonstrated that the coefficients of the variables including perceived usefulness, trialability and observability are significant. That is, perceived usefulness, trialability and observability significantly affect the behavioral The coefficient of determination (R 2 ) indicates that the three factors (i.e., perceived usefulness, trialability and observability) together explained approximately 81.7% of the total variance in behavioral intention. Additionally, we considered the regression equation of behavioral intention. According to the regression equation regarding behavioral intention (BI), the constant value of this regression equation is and there is a positive relationship between behavioral intention and the following predictors: perceived usefulness (coefficient value: 0.412), trialability (coefficient value: 0.169) and observability (coefficient value: 0.248). As a result, the hypotheses H 1, H 4 and H 5 are supported. This implies that as perceived usefulness, trailability or observability increases, so does the behavioral intention of users. V. CONCLUSION According to the Nielsen s Global Survey [3], mobile stock trading is still not very popular. However, the majority of smartphone users in Hong Kong have access to the mobile internet. As a result, it is interesting to find out the characteristics that have affected people s behavior intention in adopting mobile stock trading in Hong Kong. Based on the DOI model, several characteristics are identified. According to the results, there is a positive influence of observability on behavioral intention to use mobile stock trading. This also means that a user that observes the advantages of using mobile stock trading for his/her self from others is more likely to use mobile stock trading than a user who does not observe any benefits of mobile stock trading. Furthermore, our results show that perceived usefulness is a significant antecedent of behavioral intention in the context of mobile stock trading. This means that mobile stock trading leads to a better condition in citizens lives than the condition before mobile stock trading was established. Trialability is also an important factor contributing to the individual intention to use mobile stock trading. If trial versions of mobile stock apps are available to users, investors will be willing to use it and see what it can do. To sum up, the ability to understand the attitudes of Hong Kong investors enables domestic mobile application designers to improve their mobile stock trading services to address user attitudes and needs. As a result, the adoption rate of mobile stock trading can be increased in order to increase the transaction volume in the Hong Kong stock market. The study has some limitations and will be further enhanced in the future. In this study, we examined the acceptance of mobile stock trading in one city of China, i.e. Hong Kong. While there are other implementations of mobile stock trading in China, this study is limited to the use of mobile stock trading for one sample case. The relationship between the adoption of mobile stock trading and the demographic factors such as age and income has not been discovered. Finally, there might be other factors that are also important in determining the attitude towards adopting mobile stock trading, which are not included in this research. ACKNOWLEDGMENT This research was supported by the Department of Accounting and Information Management at the University of Macau. In addition, the survey could be completed quickly due to the support of my friends, Mr. David Leong and Mr. Andy Lee, who are familiar with the executives at several business institutions and government departments in Hong Kong so that the questionnaire copies could be distributed to the respondents quickly. REFERENCES [1] Cash Market Transaction Survey 2010/11. (2011). HKEx. Retrieved 14 November 2012, from 011/Documents/32_c.pdf [2] Rust, The current state of the digital marketplace in Hong Kong: Evolution or revolution? July Retrieved 10 November 2012, from [3] Nielsen s Smartphone Insights Study, Smartphone Ownership on the Rise in Asia Pacific, Whilst Advertisers

5 Struggle to Engage with Consumers via Mobile Ads: Nielsen, 20 June, Retrieved 10 November 2012, from [4] Nielsen s Global Survey, Nielsen Identifies Investment Strategies and Financial Habits of the Global Consumer, 11 July Retrieved 15 April 2013, from [5] Anuradha Shukla, Hong Kong slow in adopting mobile banking, 1 December Retrieved 2 March 2013, from [6] E. M. Rogers, Diffusion of innovations. 3 rd ed. New York: Free Press, [7] E. M. Rogers, Diffusion of innovations, 4 th ed. New York: Free Press, [8] F. D. Davis, Perceived usefulness, perceived ease of use and use acceptance of information technology, MIS Quarterly, vol. 13, no. 3, pp , [9] I. Ajzen, The theory of planned behavior, Organizational behavior and human decision processes, vol. 50, no. 2, pp , [10] I. Ajzen and M. Fishbein, Attitudes and the attitudebehavior relation: Reasoned and automatic processes, European review of social psychology, vol. 11, no. 1, pp. 1-33, [11] G. Moore and I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation, Information systems research, vol. 2, no. 3, pp , [12] S. Taylor and P. Todd, Understanding information technology usage: A test of competing models, Information systems research, vol. 6, pp , [13] J. R. Mariga, Managing E-Commerce and Mobile Computing Technology, 2003, Purdue University, USA [14] P. V. Huei, The study on the acceptance of wireless computing devices among consumers in Penang, Unpublished MBA thesis, Nottingham Trent University. Olympia College, Penang, Malaysia. [15] R. Safeena, N. Hundewale and A. Kamani, Customer s Adoption of Mobile-Commerce A Study on Emerging Economy, International Journal of e-education, e- Business, e-management and e-learning, vol. 1, no. 3, August [16] B. K. Jeong, and T. E. Yoon, An Empirical Investigation on Consumer Acceptance of Mobile Banking Services, Business and Management Research, vol. 2, no. 1, [17] M. AL-Majali, No More Traditional Stock Market Exchange: A Study of Internet Trading Service (ITS) in Jordan, Journal of Internet Banking and Commerce, vol. 17, no. 1, April [18] A. C. Teo, C. M. Cheah, L. Y. Leong, T. S. Hew and Y. L. Shum, What matters most in mobile payment acceptance? A Structural Analysis, International Journal of Network and Mobile Technologies, vol. 3, no. 3, [19] E. M. Rogers, Diffusion of innovation. 5 th edition. Free press, A Division of Simon & Schuster, Inc. New York. [20] B. L. Bowerman, R. T. O Connell, J. B. Orris, Essentials of business statistics. North America: McGraw-Hall, [21] D. George and P. Mallery. SPSS for Windows step by step: A simple guide and reference update (4 th ed). Boston: Allyn & Bacon, 2003.

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