Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation

Ling, Pick-Soon; Lee, Kelvin Yong Ming; Ling, Liing-Sing; Suhaimi, Mohd Kamarul Anwar Mohd Article Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Ling, Pick-Soon; Lee, Kelvin Yong Ming; Ling, Liing-Sing; Suhaimi, Mohd Kamarul Anwar Mohd (2024) : Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-17, https://doi.org/10.1080/23311975.2023.2295603 This Version is available at: https://hdl.handle.net/10419/325939 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation Pick-Soon Ling, Kelvin Yong Ming Lee, Liing-Sing Ling & Mohd Kamarul Anwar Mohd Suhaimi To cite this article: Pick-Soon Ling, Kelvin Yong Ming Lee, Liing-Sing Ling & Mohd Kamarul Anwar Mohd Suhaimi (2024) Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation, Cogent Business & Management, 11:1, 2295603, DOI: 10.1080/23311975.2023.2295603 To link to this article: https://doi.org/10.1080/23311975.2023.2295603 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 22 Jan 2024. Submit your article to this journal Article views: 3204 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 MANAGEMENT | RESEARCH LETTER Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2295603 Investors’ intention to use mobile investment: an extended mobile technology acceptance model with personal factors and perceived reputation Pick-Soon Linga, Kelvin Yong Ming Leeb, Liing-Sing Linga and Mohd Kamarul Anwar Mohd Suhaimia aschool of Business and Management, university of technology sarawak, sarawak, Malaysia; bschool of accounting and Finance, taylor’s Business school, taylor’s university, selangor, Malaysia ABSTRACT Mobile investment has been introduced with technological advancements and the enhancement of mobile device functions. However, relatively limited studies have been particularly focused on the determinants of adoption intention on mobile investment. Therefore, this study aimed at exploring the factors affecting investment intention (INT) to use mobile investment by considering the influence of personal factors (mobile self-efficacy [MSE], mobile innovativeness [MI], and attitudes [ATT]), and perceived reputation (PR) through an extended mobile technology acceptance model (MTAM). Purposive sampling was utilised to obtain 213 completed responses, which were then analysed using the partial least squares structural equation modelling (PLS-SEM), and the importance-performance map analysis (IPMA). The results showed that mobile usefulness (MU), MI, PR, and ATT substantially affect INT usage, while MSE had no significant effect on INT usage. Additionally, both MSE and PR significantly influenced ATT, but MI had an insignificant influence on ATT. This study disclosed the missing information by identifying the critical factors of mobile investment adoption using a novel framework developed from the perspective of personal factors and platform providers’ PR. Furthermore, the study provided some significant practical implications, as the findings could be referred to by the stakeholders in formulating policies and strategies to stimulate consumers to adopt mobile investment as their investment platform. Introduction The advancement of technology and sophistication of mobile devices have resulted in the transformation of traditional business activities. For example, with technological innovation, more business activities have been shifted from offline to online modes, such as online shopping, internet banking, online investment, and the like. With the innovation of mobile devices, the mobile function has been extended from communications to become business purposes. Therefore, several business activities have been changed to mobile-based, such as mobile banking, mobile payment, mobile shopping, and others. Rapid development in mobile technology has led to an increase in mobile internet users (Chong etal., 2021). Technology innovation and evolution in financial services also disrupted conventional financial activities, changing the structure of financial institutions and consumers’ financial behaviour (Fan, 2022). These innovations and evolutions also increased the consumers’ accessibility to several business activities, including investment management (Chong et al., 2021). Therefore, mobile investment has been introduced, whereas investment management activities have been performed by utilising the functions of mobile devices. © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group. CONTACT Pick-soon Ling [email protected].my school of Business and Management, university of technology sarawak, sarawak, Malaysia. https://doi.org/10.1080/23311975.2023.2295603 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. KEYWORDS Mobile investment; mobile technology acceptance model; mobile technology; perceived reputation REVIEWING EDITOR Pablo Ruiz, Universidad de Castilla-La Mancha, Spain SUBJECTS Social Sciences; Economics, Finance, Business & Industry; Finance; Investment & Securities; Business, Management and Accounting; Marketing 2 P.-S. LING ET AL. Mobile investment or mobile investing is called investment transactions and investment information searching through robot advisors using mobile devices, such as smartphones or tablets (Fan, 2022). Investors can make the investment transaction instantaneously, and manage their investment portfolios anywhere just by using their mobile devices (Chong et al., 2021). Mobile investment also offers various financial services, and investment information can be accessed by using mobile devices at a lower cost (Hikida & Perry, 2020). Advancements in mobile technology would make investment activities faster, and increase flexibility and transparency (Chong et al., 2021). Additionally, mobile investment allowed investors to monitor their investment portfolios easily, as several functions could be done using mobile devices, such as placing orders, conducting an analysis of the company’s fundamental information, checking for stock performance, and performing technical analysis (Chong et al., 2021). Therefore, investors could easily manage their investment portfolio, and respond to the market instantaneously using mobile investment. Mobile investment platform also allows investors to stay connected with the market in real-time, especially for active traders, to make informed decisions. This will enable investors to make timely decisions and take advantage of opportunities. In Malaysia, several mobile investment platforms have been introduced, such as GO+, introduced by Touch n Go; Raiz, a collaboration between Permodalan Nasional Berhad (PNB) and Raiz Invest Limited (Australia); StashAway, established by StashAway Malaysia Sdn Bhd; MYTHEO, operated by GAX MD Sdn Bhd; and other platforms that are introduced by the investment banks or fund management companies. Although several advantages are associated with mobile investment, the usage of mobile investment applications, such as mobile trading or online trading participation in Malaysia is still at a lower level. Chong et al. (2021) mentioned that nearly 77% of Malaysians used the internet, and 89% used smartphones to surf the internet. However, the statistics of Bursa Malaysia further showed that the Malaysian online trading participation rate was approximately 43% in 2020. This has raised doubts, as mobile investment adoption was at a lower level, despite the fact that it provided numerous advantages, and the percentage of access to the internet using smartphones was relatively high. Although many studies have examined the adoption of mobile technology, such as mobile payment (Al-Saedi et al., 2020; Handarkho & Harjoseputro, 2020; Yan et al., 2021), mobile tourism (Wan et al., 2022), mobile shopping (Ng et al., 2022), mobile banking (Ho et al., 2020; Purohit & Arora, 2023; Singh & Srivastava, 2020), and others, there is still a gap in the literature on mobile investment adoption, whereas the empirical evidence on this area is still scarce. To date, only a few studies related to mobile technology application in investment were found, such as stock trading application adoption (Chong et al., 2021), investment intention (INT) in online peer-to-peer lending (Lin & Huang, 2021), mobile financial service (Yan etal., 2023), and mobile investment technology adoption (Fan, 2022; Nair et al., 2023). Additionally, evidence is deficient on impact of the platform provider’s perceived reputation (PR) on mobile investment adoption. To close the gap, this study examines the underlying factors that predict mobile investment adoption by focusing on the investors’ personal factors and platform providers’ PR to offer a new shred regarding INT to use mobile investment. Therefore, the study objective is to identify the factors that significantly influence INT to adopt mobile investment amongst Malaysian investors by considering the investors’ personal factors and platform provider’s PR. To better examine mobile investment adoption, the mobile technology acceptance model (MTAM) has been used as the foundation model for this study. Literature showed that most of the previous studies used the technology acceptance model (TAM) (Bailey et al., 2020; Purohit & Arora, 2023; Sarmah et al., 2021), unified theory of acceptance and use of technology (UTAUT) or UTAUT2 (Al-Saedi et al., 2020; Dhiman et al., 2020), and the theory of planned behaviour (TPB) model, or integrated these models (Ariffin et al., 2021; Chong et al., 2021; Flavian et al., 2020; Ho et al., 2020), as their framework to study the factors of mobile technology adoption. However, these models are not explicitly designed for mobile technology adoption. Therefore, MTAM was chosen for this study, as the model was introduced mainly for mobile technology adoption. Moreover, three additional variables that reflect the personal factors, namely mobile self-efficacy (MSE), mobile innovativeness (MI), and attitudes (ATT) were included as exogenous variables to predict INT to use mobile investment. In the initial MTAM model, only two predictors were suggested (mobile usefulness [MU] and mobile ease of use [MEOU]), and it was required to include other related predictors to provide better insight into mobile technology adoption (Lew et al., 2021). COGENT BUSINESS & MANAGEMENT 3 Additionally, the platform provider’s PR was also included in the model due to its significant role in nurturing mobile investment usage. The study’s findings are crucial, as they could contribute to the literature on behavioural finance and mobile technology adoption. This is because the study identifies the factors that determine investors’ adoption of mobile investment platform in managing their investment. This finding could fill the research gaps in this area, as there were deficient studies on mobile investment adoption in literature from the perspective of the personal factors and platform provider’s PR. Additionally, the study’s findings are important for stakeholders, such as the government authorities and businesses, especially for the securities commission, investment management companies, and securities firms. This enables them to leverage their limited resources to attract more investors to use mobile investment platforms, in correspondence to the national digital economy agenda. Moreover, this study discovers the PR effect of the mobile investment platform providers. The platform providers should increase their adoption by enhancing their reputation, which significantly affect the investors’ ATT and INT to adopt mobile investment. Literature review Mobile technology acceptance model (MTAM) The MTAM was introduced by Ooi and Tan (2016) to predict INT of an individual to adopt mobile technology. This model was proposed due to the limitations in several prominent models in literature that were used to explain the adoption of new technology. For example, the Technology Acceptance Model (TAM) was introduced to describe an individual’s usage of electronic email in an organisational context. Therefore, adopting technology is mandatory for work purposes, and the organisation bears the associated costs. Similarly, UTAUT model has been criticised, as this model was used to predict the behavioural INT of an employee to apply the technology while at work. By acknowledging the difference in an individual’s INT to use the technology in the mobile context, Ooi and Tan (2016) proposed MTAM that was tailored for mobile technology, whereas previous models were mainly designed for other settings. Two primary variables were suggested in MTAM, namely MU and MEOU. Theoretically, MU in MTAM is similar to the perceived usefulness, and performance expectancy in TAM and UTAUT. At the same time, MEOU is identical to the perceived ease of use, and effort expectancy in both models. However, due to limited explanatory power of the two initial variables, as suggested in MTAM, several additional variables are integrated into MTAM to increase the model comprehension. For example, optimism and personal innovativeness were added into MTAM for the study of mobile payment (Yan et al., 2021), and MI, MSE, perceived financial cost, and perceived risk were also included in MTAM to study the consumer’s INT to use wearable payment (Loh et al., 2022). Therefore, MTAM was determined to be the most appropriate model in this research context. It was extended with three personal factors (ATT, MSE, and MI), which were proven to be important in determining INT to use mobile technology (Chong etal., 2021; Lew etal., 2021; Loh et al., 2022). Meanwhile, evidence of the mobile investment platform providers’ PR, as an exogenous factor towards INT to use mobile investment is still scarce. Hypotheses development Mobile usefulness (MU) Mobile usefulness (MU) is an individual’s perception of the enhancement in performance from using mobile technology (Ooi & Tan, 2016). With the advancement of mobile devices, the adoption of mobile technology, such as mobile investment is perceived to improve usefulness of the technology. When individuals perceive that mobile technology could enhance their performance, they will most likely adopt it. The significant role of usefulness in technology adoption was established in prior studies, but in different settings. For example, Lau etal. (2021) revealed the significant influence of MU on INT to use mobile taxi booking. Wan et al. (2022) found that MU significantly impacted mobile tourism shopping INT. Similarly, Ng et al. (2022) remarked on the significant effect of MU on INT to use mobile fashion shopping. Loh et al. (2022) revealed that MU significantly influenced wearable payment adoption INT. However, 4 P.-S. LING ET AL. inconclusive findings on the role of usefulness on the behavioural INT to adopt technology were also revealed in some studies. For example, Sarmah et al. (2021) found insignificant role of perceived usefulness on mobile wallet adoption INT of the millennials in India. Therefore, further studies should be conducted to investigate the effect of MU on INT to adopt mobile investment. The following hypothesis is proposed, as investors tend to use mobile investment if it provides more usefulness. H1: MU has a positively significant relationship with INT to use mobile investment. Mobile ease of use (MEOU) According to Ooi and Tan (2016), MEOU refers to an individual’s perception regarding the difficulty of learning and adopting mobile technology, such as mobile investment in this study. An individual is likely to use mobile technology if it does not need an extra effort to adopt such technology (Yan etal., 2021). Therefore, technology usage will increase if it does not require additional actions (Ng et al., 2022). Empirically, several studies examined the influence of MEOU on mobile technology adoption, and found a significant effect. Ng et al. (2022) found that MEOU was substantially related to mobile fashion shopping use INT. Similarly, Loh et al. (2022) also remarked on the same effect on wearable payment. The significant role of MEOU was also revealed in the mobile tourism shopping adoption INT (Wan et al., 2022). Lew etal. (2021) further found that INT to use mobile wallets was also significantly influenced by MEOU. Unfortunately, contrary findings were also reported in some other studies (Ooi & Tan, 2016; Lau etal., 2021; Yan etal., 2021). According to Lau etal. (2021), the effect of MEOU on INT to use technology was diminished due to the increased familiarity with mobile devices and simplicity of mobile services. With the inconclusive findings on the role of MEOU on INT, there is a need for further studies to examine this relationship again. The following hypothesis is suggested, as the investors are expected to be more likely to use mobile investment if it only requires minimal efforts. H2: MEOU has a positively significant relationship with INT to use mobile investment. Mobile self-efficacy (MSE) The MSE is individuals’ perception of their ability to learn and use mobile technology (Lew et al., 2021). Individuals who believe they can perform the technology will have a favourable ATT towards the platform. This is aligned with Zhu etal. (2010), who remarked that a higher self-efficacy could form a livelier ATT for m-auction. Similarly, in the context of self-service technology in libraries, Hsiao and Tang (2015) also found that self-efficacy significantly influenced ATT. This postulated the effect of MSE on ATT, whereas ATT towards mobile investment could be enhanced through their self-efficacy. The investors are expected to have positive and favourable ATT if they have sufficient self-efficacy on mobile devices. Moreover, if individuals perceive that they have such efficacy to perform their investment through the mobile platform, they tend to use the mobile investment platform to manage their investments. The influence of MSE has been revealed in literature, such as Singh and Srivastava (2020) who found that MSE significantly affected mobile banking usage. Similarly, Lew et al. (2021) also showed a significant positive effect of MSE on INT to use a mobile wallet. Al-Saedi et al. (2020) also found that mobile payment adoption INT was significantly influenced by self-efficacy. Nevertheless, literature also documented the insignificant influence of self-efficacy on ATT and INT. For example, Loh etal. (2022) found that MSE did not significantly influence the wearable payment adoption INT. Besides the MSE, the insignificant impact of self-efficacy on technology adoption was also found in other contexts, such as smartphone fitness applications (Dhiman et al., 2020), mobile commerce (Tarhini et al., 2019), mobile payment (Lui etal., 2021), and the like. Therefore, the following statements are hypothesised to further investigate the effect of MSE on ATT and INT. H3: MSE has a positively significant relationship with ATT to use mobile investment. H4: MSE has a positively significant relationship with INT to use mobile investment. COGENT BUSINESS & MANAGEMENT 5 Mobile innovativeness (MI) The level of an individual’s willingness to try a new mobile technology is referred to as MI (Loh et al., 2022). When individuals are highly willing to try a new mobile technology, this could indicate that their MI is increased (Boateng et al., 2016). Therefore, they may have a good perception or positive ATT towards the mobile investment platform when innovativeness enhances their curiosity. This postulation is supported by studies of Boateng et al. (2016) and Patil et al. (2020), who revealed the positive significant influence of personal innovativeness on ATT towards mobile advertising and mobile payment, respectively. For that reason, MI is predicted to have a significant influence on ATT. Moreover, individuals could have a higher INT when they are highly innovative in using mobile technology. The substantial effect of individuals’ innovativeness towards their INT has been well documented. For example, Loh etal. (2022) revealed that INT to use wearable payment was significantly influenced by MI. Similarly, the influence of an individual’s innovativeness on INT to adopt the technology was also found in other studies, such as live streaming service (Singh et al., 2021), mobile payment (Handarkho & Harjoseputro, 2020), mobile banking (Ho etal., 2020), and smartphone fitness application (Dhiman etal., 2020). Furthermore, insignificant influence of an individual’s innovativeness towards INT to use mobile payment was found by Yan et al. (2021) and Lui et al. (2021). Therefore, further studies should be conducted to explore the effects of MI on ATT and INT. The following hypotheses are proposed to be investigated in this study. H5: MI has a positively significant relationship with ATT to use mobile investment. H6: MI has a positively significant relationship with INT to use mobile investment. Perceived reputation (PR) Mobile investment is a platform designed for managing investment portfolios using mobile devices. Therefore, the mobile investment platform provider’s PR is expected to influence INT to use mobile investment platforms. As defined by Xin et al. (2015), the service providers’ PR was the degree of an individual’s belief in the service provider regarding their competency, honesty, and benevolence. This signified that if the service provider has a good reputation, it will strengthen the individual’s ATT towards the platform. The user tends to adopt the platform that has a high reputation. Moreover, this PR is crucial for mobile technology, whereby the transaction is virtually done. Therefore, the platform provider’s PR is crucial for an individual to adapt its technology. The success of mobile transactions relies on the faithful and ethical practices of the mobile service providers and their technology (Xin et al., 2015). Dahlberg et al. (2003) found that an individual was likely to use mobile payment if the vendors are well-known and established companies. Similarly, Warsame and Ireri (2018) and Nguyen et al. (2022) remarked on the crucial impact of reputation on Islamic banking and mobile banking adoption, respectively. Consequently, the following hypotheses are postulated. H7: PR has a positively significant relationship with ATT to use mobile investment. H8: PR has a positively significant relationship with INT to use mobile investment. Attitude (ATT) toward mobile investment Attitude (ATT) is the magnitude of an individual having a favourable or unfavourable assessment of technology. In this study, the ATT towards mobile investment is referred to as the individuals’ perception towards mobile investment platform, whether the perception is positive or negative. Theoretically, if an individual has a positive or favourable ATT towards technology, the INT to use that technology is higher than the negative or unfavourable perception. Significant influence of ATT towards INT has been widely recognised in literature. For example, Ho etal. (2020) and Purohit and Arora (2023) revealed that ATT significantly influenced INT to use mobile banking. Similarly, Chong et al. (2021) also found that the individual’s INT to adopt mobile stock trading was significantly affected by ATT. The significant impact of ATT on mobile payment technology was also revealed by 6 P.-S. LING ET AL. Bailey et al. (2020), Flavian et al. (2020), and Teng et al. (2020). Therefore, the following hypothesis is suggested. H9: ATT has a positively significant relationship with INT to use mobile investment. The proposed research framework is provided in Figure 1, developed from the discussion above. Research methodology The targeted population for this study was the investors in Malaysia, as this study aimed at examining the determinant factors that might significantly influence INT to use mobile investment platforms. Therefore, purposive sampling technique was used to gather the primary responses from the public. A screening question was included to ensure that only Malaysian investors participated in this study. A total of 213 completed responses were gathered through the survey. As suggested by the power analysis using G*Power software, 146 respondents were the minimum sample size for the proposed research model in this study. This sample size was determined through the priori power analysis using a medium effect size of 0.15, an alpha value of 0.05, and a power level of 0.95. Therefore, a final sample of 213 was sufficient to examine the framework of the study, as the number was greater than the suggested minimum sample size. For the measurement items of the proposed model, a total of 28 measurement items were adapted from prior studies, and used to develop the study questionnaire. Specifically, four items each for MU and MEOU were adapted from a study by Lau etal. (2021), while four items each for MSE, MI, and INT to use mobile investment were adapted from a study by Loh et al. (2022). Additionally, four items for ATT towards mobile investment were adapted from Bailey et al. (2020), and three items for the platform providers’ PR were adapted from Chandra et al. (2010). However, three items were deleted due to the lower outer loading than 0.708, including MEOU1, MSE4, and MI3. The seven-point Likert scale, ranging from 1 to 7 for strongly disagree to strongly agree was used for respondents to measure the level of agreement on the measurement items. The primary responses from participating respondents were collected using the online survey platform Google Forms from the Malaysian investors. The proposed research model was then analysed by utilising the partial least squares structural equation modelling (PLS-SEM) approach using the SmartPLS software. The Mardia’s multivariate normality test results showed that the data was not normally distributed, as the kurtosis coefficient (113.1198) was greater than 20 (Byrne, 2013; Kline, 2011). Therefore, PLS-SEM was the most appropriate technique for this study (Hair et al., 2019). Figure 1. Proposed research model. COGENT BUSINESS & MANAGEMENT 7 Data analysis As presented in Table 1, 64% and 36% of the respondents were females and males, respectively. Regarding the age range, around one-fourth of the respondents were between 19to 24-year-old, followed by 24% were between 25to 29-year-old, and 30to 34-year-old. At the same time, the other age range accounted for the remaining 27%. Moreover, around 64% of the respondents were single, and the remaining were married. Majority of the respondents were employees (59%), followed by students (24%), self-employed (15%), and others (2%). For the highest education level, 73% of respondents had at least a certificate, diploma, or bachelor’s degree, 16% had postgraduate degrees, and the remaining 11% only had primary or secondary education. Assessment of measurement model The two-stage analysis involved the measurement assessment, and structural models were performed. In the first measurement model assessment, some necessary reliability and validity tests were performed, and the results are provided in Table 2. Firstly, both the average variances extracted (AVE) and outer loading were used to assess the validity for both constructs and items level. The results showed that the convergent validity for both constructs and items was established, as the AVE value was greater than 0.5000 (Flavian etal., 2020). Additionally, all items had a loading value of greater than 0.7080 (Hair etal., 2017), except for three items (MEOU1, MSE4, and MI3) with loading values of lower than 0.7080, and thus were deleted. Both the composite reliability (CR) and Cronbach’s alpha were adopted, and all constructs had values of higher than 0.7000, indicating that reliability was also achieved. Additionally, the Heterotrait-Monotrait (HTMT) correlation ratio was also performed to assess the discriminant validity. The result in Table 3 showed that all values were lower than 0.9000, and the discriminant validity was also satisfactory (Henseler et al., 2015). Meanwhile, the variance inflation factor (VIF) for all the study constructs obtained from the full collinearity test suggested by Kock (2015) is also presented in Table 2. The result showed that the data was absent from multicollinearity, as the VIF value was lower than 5 (Anwar et al., 2021; Hair et al., 2017). Assessment of structural model The study continued with the second stage of PLS-SEM by assessing the structural model. As shown at the bottom of Table 4, 62% of the variance in ATT was successfully explained by MSE, PR, and MI, while MU, MEOU, ATT, MSE, PR, and MI explained 69% of the variance in INT. Moreover, the predictive Table 1. Profiles of the respondents. Profiles Frequency Percentage Gender Male 76 35.68 Female 137 64.32 Age 19–24 54 25.35 25–29 51 23.94 30–34 51 23.94 35–39 26 12.21 40–44 18 8.45 45 and above 13 6.10 Marital status Married 77 36.15 single 136 63.85 Occupation employee 125 58.69 self-employed 32 15.02 students 51 23.94 others 5 2.35 Highest education level Certificate/Diploma/Bachelor Degree 156 73.24 Master and PhD 34 15.96 Primary and secondary school 23 10.80 14 P.-S. LING ET AL. About the authors Pick-Soon Ling is a Lecturer at the School of Business and Management, University of Technology Sarawak (UTS). He graduated with a PhD, a Master of Economics, and a Bachelor of Business Administration with Honours from the National University of Malaysia (UKM). Kelvin Yong Ming Lee is a Senior Lecturer at the School of Accounting and Finance, Taylor’s University. He graduated with a PhD (Finance), MSc (Finance) and a Bachelor of Finance with Honours from the Universiti Malaysia Sarawak (UNIMAS). Liing-Sing Ling is a Lecturer at the School of Business and Management, University of Technology Sarawak (UTS). Besides she is also a Fellow member of the Association of Chartered Certified Accountants (ACCA) and member of the Malaysian Institute of Accountants. Mohd Kamarul Anwar Mohd Suhaimi is a Law Lecturer at the School of Business and Management, University of Technology Sarawak. He was formerly admitted as an advocate and solicitor of the High Court of Malaya. References Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). 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(2021) • Interaction with mobile investment does not require a lot of intellectual effort. • It would be easy for me to become skilful at using mobile investment. • Learning how to use mobile investment would be easy for me. • Mobile investment is flexible to interact with. • Overall, I find mobile investment easy to use. Mobile Self-Efficacy – Adapted from Loh et al. (2022) • I feel confident using the mobile investment to complete my investment efficiently. • I feel confident using the mobile investment to make my investment even if there was no one around to tell me how it works. • I feel confident using the mobile investment to make my investment if I had only the step-by-step manual for reference. • I feel confident using the mobile investment to make my investment if I had just the built in-help facility for assistance (e.g. Apple, Siri, Google Assistant). Mobile Innovativeness – Adapted from Loh et al. (2022) • I am interested in exploring new mobile investment technologies. • If I hear about a new mobile investment technology, I will look for ways to experiment with it. • Among my peers, I am usually the first to explore new mobile investment technologies. • I think I will use the mobile investment to make my investment even if I do not know anyone else who has done it before. Perceived Reputation of Mobile Investment Service Provider – Adapted from Chandra et al. (2010) • I believe this mobile service provider (such as Touch n Go, Raiz, etc.) has a good reputation. • I believe this mobile service provider (such as Touch n Go, Raiz, etc.) has a reputation for being fair. • I believe this mobile service provider (such as Touch n Go, Raiz, etc.) has a reputation for being honest. COGENT BUSINESS & MANAGEMENT 17 Attitude toward Mobile Investment – Adapted from Bailey et al. (2020) • Using a mobile investment platform is a good idea. • Using a mobile investment platform is beneficial. • I have a positive attitude toward using mobile investment platforms. • I have a positive attitude toward using mobile investment platforms. Intention to use Mobile Investment – Adapted from Loh et al. (2022) • I am open to use a mobile investment to make my investment in the near future. • Given the opportunity, I will use the mobile investment to make my investment. • I am likely to use the mobile investment to make my investment in the near future. • I intend to use the mobile investment to make my investment if the opportunity arises.