Factors Influencing Online Purchase Intention among Staff from Royal Malaysian Police (RMP) Headquarters

Online purchase has become a norm in Malaysia after the COVID-19 pandemic. It is one of measures to cope with the rapid spread of the virus. Royal Malaysian Police (RMP) staff are not left behind in following the current trend. The present study is intended to determine the factors that influence them to engage in online purchase although they are very busy in ensuring the public safety. Data were collected using online survey distributed conveniently to the staff. After the lapse of the allocated data collection period, 150 responses were received and analyzed. Out of five factors that have been selected as the independent variables only two are significant to influence online purchase intention among the RMP staff, which are attitude and perceived usefulness. Perceived ease of use, subjective norms, and perceived behavioral control are not significant to influence staff online purchase intention. The findings indicate that only those who have the right attitude towards online purchase and those who find online purchase is beneficial to them will engage in online purchase.


Introduction
Online purchase intent is a fast-growing phenomenon in today's world. Today, online purchase intention has grown in popularity among individuals since they are too busy with their everyday activities to do shopping in person. Malaysia has a high rate of e-Commerce use, according to statistics. Malaysia has a total of 16.53 million internet consumers (50 per cent of the population). Because of the COVID-19 phenomenon, online purchase intention becomes a primary purchasing option for individuals. Unfortunately, some sellers do not understand how to set up their company online, but anybody with social media access may start an internet business from the comfort of their own home. The interface of the website, on the other hand, will affect client participation and their desire to return to the website. The majority of businesses today continue to operate conventionally. Malaysian sellers and suppliers, for example, continue to rely on the traditional brick and-mortar model, in which they must ensure that their products are compatible, attractive, and competitive to compete with such massive players in the online shopping industries as Lazada, Shopee, Zalora, 11street, Ezbuy and FashionValet, and Go Shop. Small sellers and suppliers are finding it difficult to compete with online business behemoths because they lack knowledge of the aspects of online business that might persuade clients to buy their goods and services online. Meanwhile, corporations use social media and mobile applications to sell their goods, make special deals, build their client base, stay in touch with existing customers, and attract future clients (Cheung & To, 2016;Feng et al., 2016).
According to e-Marketer (2015), worldwide mobile commercials, particularly so-called in-app advertisements, are predicted to triple from USD69 billion in 2015 to USD196 billion in 2019. Furthermore, due to the worldwide outbreak, several governments have adopted movement restriction measures to prevent or contain the spread of the COVID-19 virus, and Malaysia is no exception. This regulation encouraged more customers to make online purchases, resulting in a growth in ecommerce sales. Because of a lack of awareness of their online buying behaviors, online marketers have struggled to acquire clients as more people want to purchase online. As a consequence of this research, online marketers will better understand the factors that impact customers' online purchase intention. Existing research is insufficient to address the issue of online purchasing intention since it has been undertaken in several situations, with few studies conducted in Malaysia, including prominent online sellers such as Lazada, Shopee, Zalora, 11street, Ezbuy and FashionValet, and Go Shop. As a result, the current research aims to bridge the existing gap by studying the variables that affect consumers' online purchasing intention. Even though many studies have been done on those factors, there are still variations in their findings.
There have been a few studies undertaken in Malaysia on the variables affecting online purchase intention in the literature, particularly in the government sector. Little was known about the factors affecting online purchase intention among customers, and there was still a scarcity of literature on the factors driving online purchase intention, particularly among public workers. As a result of the reviewed literature, the purpose of this research is to fill in the gap. The objective of this study is as follows; • To determine the relationship between influencing factors; attitude, perceived behavioral control, subjective norms, perceived ease of use and perceived usefulness (independent variables) with online purchase intention among consumers at different departments at RMP headquarters.

Literature Review Online Purchase Intention
The degree to which a buyer is willing to acquire a product from an online retailer is referred to as online purchasing intention (Pavlou, 2003). Heijdein et al (2001) defined online purchasing intention as the consumer's desire to acquire a product or service from a particular website. The correlation between online purchase intention and actual online purchase behavior is positive (Lim et al., 2016). Fishbein and Ajzen (1975) defined a buy intention as an actual conduct that predicts a person's behavioral intention in the sense that attitudes represent beliefs, intentions properly reflect attitudes, and behavior accurately reflect intentions. Online purchase intention is influenced by various factors. However, most researchers agree on five main factors that will contribute to this phenomenon. They are attitude, perceived behavioral control, subjective norms, perceived ease of use and perceived usefulness. These factors are derived from two theories, which are the theory of planned behavior (Fishbein & Ajzen, 1975) and the technology acceptance model (Davis, 1989).

Attitude
Attitude refers to an individual's inner feelings about whether they like or dislike something, such as a service or a product. It is the degree to which an individual has a good or negative view of conduct (Albarracn et al., 2001). Their essential beliefs determine a consumer's attitude. It is defined as the degree to which an individual conducts a positive or negative behavioral assessment (Fishbein & Ajzen, 1977). More specifically, attitudes change throughout time as people create new notions about the subject or object under consideration (Shaouf et al., 2016). Attitudes are typically challenging to alter, but they may be modified by psychological motivation. They also learn and evolve over time (Lien & Cao, 2014).
The attitude of the consumer influences their intention (Fishbein & Ajzen, 1975). The shopping attitude of consumers has been shown to have a positive relationship with their purchasing intention in the context of online shopping (Ha, 2020;Ha et al., 2019;Yoh et al., 2003). Many empirical studies support this relationship (Ha, 2020;Ha et al., 2019;Lin, 2007;Pavlou & Fygenson, 2006). The more positive a consumer's attitude toward a website/online store, the more likely they are to shop at that website/store.

Perceived Behavioral Control
Perceived behavioral control assesses the difficulty of carrying out an activity. Perceived behavioral control is connected to the availability (or lack thereof) of resources required to engage in certain behaviors (Ajzen, 1991;Gopi & Ramayah, 2007). Perceived behavioral control refers to people's assessments of their ability to do a specific task. It refers to the difficulty of adopting a behavior. Some actions are indeed outside one's voluntary control, and Ajzen (1991) said that perceived behavioral control can account for a broader spectrum of behaviors more accurately by including perceived behavioral control into their reasoning. According to Ajzen (2002), perceived behavioral control is made up of two closely linked (and associated) variables: perceived self-efficacy (belief in one's potential; Bandura, 1991) and perceived controllability (believing in one's own will; Ajzen, 2002). When these two factors are combined, they form behavioral control, which directly and indirectly predicts behavior through an intention. According to the theory of planned behavior, perceived behavioral control is an individual's belief that they can accomplish the activity (Ajzen, 1991). The more in control a person feels about making an online purchase, the more likely they are going to do so. The intention is influenced by the impression of volitional control or the perceived difficulty of the activity (Chang, 1998).
In the context of online shopping, perceived behavior control relates to consumers' perceptions of the availability of required resources, knowledge, and opportunities to shop online (Lin, 2007). Perceived behavioral control has been shown to have a positive impact on the intention of online shoppers (Lin, 2007).

Subjective Norms
Subjective norms are felt societal pressures to participate in certain conduct. Subjective norms are based on an individual's perception of what should and should not be done considering the advantage or punishment that may arise from such behavior. Subjective expectations are a commonly utilized idea as a source of hesitation (Sandve & Gaard, 2014) since individuals are more inclined to act if their role models do (Schepers & Wetzels, 2007).
Meanwhile, some studies show that the opinions of the referent group have a positive impact on online customers' purchasing intentions (Ha et al., 2019;Lin, 2007;Ha, 2020). Subjective norms in e-Commerce reflect consumers' perceptions of the influence of reference groups on online shopping ability (Lin, 2007). According to the findings of the study, the opinion of the reference group has a positive relationship with the shopping intention of online customers. It also implies that the more the reference group encourages online shopping, the more online customers are likely to shop online, and vice versa.

Perceived Ease of Use
Perceived ease of use is the degree to which a person feels that utilizing a particular system would be painless (Davis, 1989). Perceived ease of use is defined as how a person feels it would be simple to utilize a specific application (Davis, 1989). The degree to which users perceive how simple it is to utilize technology is characterized as perceived ease of use. If the technology is easy to use, the hurdles have been overcome. When it is challenging to use and the setup is complicated, no one likes it.
Consumer purchase intention will increase as a result of the online shopping platforms, product searching and checkout process efficiency, and it will also directly improve consumers' perceived ease of use. According to Selamat et al (2009), when purchasing products or services, consumers prefer to use a simple online shopping platform. As a result, consumers prefer to use the technology that is simple to use and requires little effort to learn; thus, this statement supports the perceived ease of use that affects and influences consumers to choose the online platform to purchase. Several prior studies have shown that perceived ease of use is used to determine online purchase intention, and the results show that perceived ease of use has a significant influence on consumer online purchase intention. According to the research of Athapathuthu and Kulathunga (2018); Butt (2016), perceived ease of use had a positive and significant effect on online purchase intention.

Perceived Usefulness
Perceived usefulness is described as a measurement in the usage of a reliable technology that may benefit those who utilize it (Davis, 1986). Several prominent authors (Davis, 1986;Wang et al., 2003;Noviarni, 2014) agreed to define perceived advantages as users' personal ability for the future. Employing a specific application system would boost performance in an organizational environment. Most prior research utilized the term "perceived usefulness," which refers to the advantages of online purchasing such as convenience, price comparison, pleasure, and improved customer-retailer relationships (Martin et al., 2015).
In the context of online shopping, perceived usefulness refers to the degree to which a consumer believes that online shopping will improve the effectiveness of their shopping activities (Shih, 2004). There is evidence that perceived usefulness influences online shopping intention (Gefen et al., 2003a;Ha, 2020;Ha & Nguyen, 2013;Ha et al., 2019). Dash and Saji (2006) discovered a direct relationship between perceived usefulness and purchase intention in business-to-consumer (B2C) online shopping in an Indian context. Previous research by Athapathuthu and Kulathunga (2018); Butt (2016); Shaheen et al (2012) discovered that perceptions of usefulness have a positive and significant effect on online transaction interest.

Methodology
In this study, a quantitative research design was used. The quantitative research method uses a questionnaire to collect the required data from the target respondents. The results are presented numerically, and they are used to test the research hypotheses (Finster, 2013). The study population was made up of consumers from different departments at the Royal Malaysian Police headquarters. To gather information for this study, the researchers used a nonprobability sampling technique known as convenience sampling. Convenience sampling refers to the collection of data from members of the population who are conveniently available to provide it (Sekaran & Bougie, 2013). According to Krejcie and Morgan's (1970) table, the sample size for this study is 150. Because of the recent COVID-19 pandemic outbreak, the researchers used an online survey via Google forms to collect data. Google forms were distributed via social media platforms such as the WhatsApp group among consumers at different departments at the Royal Malaysian Police headquarters. The data collection process took approximately one month. The data were analyzed using SPSS version 23.

Findings and Discussion
From 150 sets of questionnaires distributed, the study managed to collect all the responses, making the response rate to be 100%. which is 10% with ten respondents, meanwhile 8.7 % for 13 respondents with MYR 5000-5999. Lastly, based on the average use of internet, the highest average use of internet in daily basis is 3-4 hours with 42 respondents (28%), followed by 5-6 hours with 39 respondents (26%), 7-8 hours with 35 respondents (23.3 %), more than 9 hours with 21 respondents (14%) and 1-2 hours average use of internet in daily basis with 13 respondents (8.7%). According to the results of factor analysis for the independent variables, the total percentage (%) variance explained is 92.3%. Next, Kaiser-Meyer-Olkin Measure of Sampling Adequacy was .921, higher than the minimum value of 0.6, indicating sufficient intercorrelations, while Bartlett's Test of Sphericity was significant (Approx. Chi-Square=3911.908, p<0.01). The result of factor analysis indicates the existence of five factors that constitute the independent variables as originally conceptualized. The factors are perceived usefulness, perceived ease of use, subjective norms, attitudes and perceived behavioral control. According to the results of factor analysis for the dependent variables, Kaiser-Meyer-Olkin Measure of Sampling Adequacy was .764 which is it greater than 0.6, indicating that it is sufficient intercorrelations while Bartlett's Test of Sphericity was significant (Approx. Chi-Square=624.513, p<0.01). The result shows the existence of a uni-dimensional factor of the dependent variable, which is online purchase intention.  (1-tailed). N=150. Cronbach's alphas are along the diagonal in the parentheses.

Results of Reliability and Correlation Analysis
Correlation analysis was used to test the determination of the 'net strength' relationship between two variables (Sweet & Martin, 2008). Correlation analysis is used to determine whether or not two variables have a relationship. If the correlation value is zero, the variable has no relationship. Meanwhile, if the value is close to one, it indicates that it is the strongest value and that the relationship between variables is strong. Table above shows the correlation analysis results of independent variables; attitude, perceived behavioral control, subjective norms, perceived ease of use and perceived usefulness with dependent variable; which is online purchase intention. First, attitude and online purchase intention are significantly correlated with strong correlation (r =.747**, p<0.01). Second, perceived behavioral control (PBC) and online purchase intention are significantly correlated with a strong correlation (r =.684**, p<0.01). Third, subjective norms and online purchase intention are significantly correlated with moderate correlation (r =.583**, p<0.01). Next, perceived ease of use and online purchase intention are significantly correlated with strong correlation (r = .690**, p<0.01). Lastly, perceived usefulness and online purchase intention are significantly correlated with a strong correlation (r=.784**, p<0.01).
Cronbach Alpha value for perceived usefulness was .984, followed by online purchase intention.973, perceived ease of use was .964, perceived behavioral control was .956, attitude was .946, and subjective norms was .940. According to the Rule of Thumbs on Cronbach's Alpha Coefficient, the strength of association for all variables is excellent. As a result, it is possible to conclude that all items measuring the intended variables are highly reliable. The results of a multiple regression analysis show the value of R Square of .645, which means 64.5% variance in the dependent variable: online purchase intention is explained by the independent variables: attitude, perceived behavioral control, subjective norms, perceived ease of use and perceived usefulness.

Model
From the results, attitude is significant to influence online purchase intention (p<0.01, β= .428). Second, perceived behavior control is not significant to influence online purchase intention (p >0.05, β= .048). Third, subjective norm is not significant to influence online purchase intention (p >0.05, β= .124). Next, perceived ease of use is not significant to influence online purchase intention (p >0.01, β= .064). Lastly, perceived usefulness is significant to influence online purchase intention (p <0.01, β= .250).
Based on the findings, there is a significant relationship between attitude and online purchase intention. The result of this research proves that consumers' attitude to online shopping has a positive impact on their shopping intention. The more positive a consumer's attitude toward a website/online store, the more likely they are to shop at that website/store. Consumers who find the purchasing process is easy, convenient, and straightforward will make a purchase decision. This finding is consistent with previous research by (Lin, 2007;Bigne-Alcaniz et al., 2008).
Based on the finding, there is no significant relationship between subjective norms and online purchase intention. This study's finding contradicts with the previous study's findings of Patel and Asthana (2012); Rehman and Coughlan (2012), which show that the opinion of the reference group has a positive relationship with online shopping intentions. It also implies that the more the reference group encourages online shopping, the more online customers are likely to shop online, and vice versa. This finding is similar to (Lin's research, 2007).
The finding shows no significant relationship between perceived behavioral control and online purchase intention. This study's findings contradict with previous research by Nayyar et al (2011), who found that perceived behavioral control is the primary driver of first impressions to customers. Complicated processes and unnecessary requirements should be simplified. Online shops should be perceived as easy to use, and in reality, this influences how many consumers will make purchases on such a site. Customers frequently want to shop on a simple platform to use and will save them time.
The finding shows no significant relationship between perceived ease of use and online purchase intention. The current research finding contradicts with several previous research outcomes, including those by (Al-Debei et al., 2015;Lim et al., 2016;Salehi et al., 2011;Sheik et al., 2015). If the technology is user-friendly, customers are likely to use it. But, when it is difficult to use, and the configuration is confusing, no one likes it. According to Selamat et al (2009), consumers prefer to purchase products or services through a simple online shopping platform.
Based on the finding, there is a significant relationship between perceived usefulness and online purchase intention. The results of this research have proved that consumers' perceived usefulness will lead to online shopping. Several studies have shown that perceived usefulness directly influences purchase intention in e-Commerce contexts (Gefen et al., 2003;Gefen & Straub, 2000). According to the prior findings of Dash and Saji (2006), there is a direct relationship between perceived usefulness and purchase intention in B2C online shopping in the Indian context. Evidence proves that online shopping intention is significantly affected by perceived usefulness (Gefen et al., 2003a;Ha, 2020;Ha & Nguyen, 2013;Ha et al., 2019).

Implications of the Study and Suggestions for Future Study
In this section, the researcher makes several recommendations for future study in determining factors influencing online purchase intention. The recommendations are as follows: i) The purpose of this study is to determine the influencing factors of online purchase intention: attitude, perceived behavioral control, subjective norms, perceived ease of use, perceived usefulness. However, the results show that only attitude and perceived usefulness have a significant relationship with online purchase intention, while perceived behavioral control, subjective norms, and perceived ease of use do not have a significant relation with online purchase intention. As a result, it is suggested that further research should look for other factors that may influence consumers. ii) A larger sample size is recommended for future research because the sample size obtained for this study is too small due to access limitation. Because this study only received 150 samples, the number of samples being studied is relatively small compared to the total population. The researcher believes that a larger sample size will provide better representativeness of the population. iii) It is suggested that a similar study be replicated with consumers from different backgrounds in other Malaysian government organizations to generate more rigorous data on how the five factors comprising attitude, subjective norms, perceived behavioral control, perceived ease of use and perceived ease usefulness have a significant relationship with online purchase intention. Furthermore, additional research can provide enough data for comparison to be made. iv) The present study only focused on certain factors to determine their influence on the dependent variable. Future researchers should include other influential variables to measure consumers' online purchase intention, such as trust, security, perceived risk, and others so that the findings will enrich the existing body of knowledge in the field. v) It is suggested to use different methods for data collection. The researcher only used a questionnaire as a survey method to collect data for this study. For future research, researchers can use qualitative methods, to learn more about human behaviors, attitudes, and experiences through observation and interview or focus group study. vi) In future, researchers may employ stratified sampling. Stratified sampling allows the researcher to divide the population into distinct groups known as strata. Then, from each group, a probability sample is drawn. Stratified sampling has several advantages over other types of sampling. For example, using stratified sampling may allow the reduction of the sample size required to achieve a given precision.

Conclusion
Online purchase behavior has become the norm among Malaysians. The COVID-19 pandemic has contributed in transforming Malaysians to be more receptive to this phenomenon. The subject of the present study is the Royal Malaysian Police because this group is quite reluctant to accept the new norm. Using online survey approach to collect the required data, a total of 150 responses were secured from various departments at the Royal Malaysian Police headquarters. The results of the multiple regression analysis indicate that only two factors; attitude and perceived usefulness, are significant to influence online purchase intention. Therefore, it is suggested that in order to make police force to have online purchase intention, their attitude and perception on the online shopping have to be changed. They need to be guided especially during the first attempt to do the online buying transaction to ensure that they had positive online shopping experience.
The originality of this study is it investigates the factors influencing online purchase intention among the RMP staff whereby the factors are attitude, perceived behavioral control, subjective norms, perceived ease of use and perceived usefulness are something that has been overlooked by the previous scholars. The significant of the study will contribute to e-Commerce marketers and online retailers to improve their strategy particularly during Covid-19 pandemic. In addition, the result of the study shows that attitude, β=.428 is the most important factors for the e-Commerce marketers in improving online purchase intention among the RMP staff. As for the context of the study, this study also determines the relationship among factors influencing online purchase intention specifically for government sectors since there are still limited research conducted in Malaysia.