factors affecting consumers' adoption of online shopping in hong kong

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Factors Affecting Consumers’ Adoption of Online shopping in Hong Kong by Lester Wan A dissertation submitted for partial satisfaction of the requirements for the degree of Master of Business Administration in International Management in the University of London

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Page 1: Factors Affecting Consumers' Adoption of Online Shopping in Hong Kong

Factors Affecting Consumers’ Adoption of Online shopping in Hong Kong

by Lester Wan

A dissertation submitted for partial satisfaction of the requirements for the degree of

Master of Business Administration in

International Management

in the

University of London

Page 2: Factors Affecting Consumers' Adoption of Online Shopping in Hong Kong

Abstract

Research Background The advancement of information technology over the past few twenty years has created a great impact in mankind and it has changed the day to day operations of everyone as well as created new and more opportunities in new industries, revolutionising the busi-ness environment of numerous sectors. One of the industries that have the heaviest im-pact is the retail sector, where pure-play online retailers have emerged, and traditional bricks-and-mortar shops have to alter their business models to keep pace with the trend.

There are many arguments and individual views on online shopping. Most would say that technology gives us convenience with 24 hours around the clock and allows you to read-ily search and view information. However others might argue that there are fraud, securi-ty and privacy concerns, and online shopping would cause stress when there are hidden costs and terms and conditions are unclear. The objective of this study is to investigate what affects consumer adoption of online shopping in Hong Kong, and to develop a ro-bust research model that can predict actual online shopping behaviour.

Research Methodology An exploratory study approach was first applied to identify key factors that had been identified by past literatures to have an impact on the adoption of online shopping. An explanatory study approach was then pursued so that the relationship between key fac-tors and the online shopping intention and actual behaviour is to be found.

Statistical tests are also applied to investigate the causal relationship between variables. A cross-sectional study is applied and this study is investigating the online shopping be-haviour of Hong Kong online shoppers in the past year. Data was then analysed through various statistical method. Demographics and online buying behaviour were presented through tables and charts. Correlation analysis was conducted to provide a quick over-view of how constructs relate with each other. Regression analysis was conducted to test hypotheses developed and formalise a model to predict actual online shopping be-haviour of consumers in Hong Kong.

Key Findings and Implications The survey conducted shows that actual online shopping behaviour is significantly af-fected by online shopping intention, which is influenced by attitude towards online shop-ping, normative beliefs and consumers’ task orientation. Consumers’ attitude towards online shopping is affected by the perceived benefits, past online shopping satisfaction and consumers’ experience orientation.

Families and friends may have a certain amount of influence on online shopping inten-tion, and business can leverage the potential through social marketing and word of mouth marketing. Marketing strategies that creates incentives to encourage and intro-duce family and friends or group discounts may promote awareness and purchases. Perceived benefits is one of the crucial constructs influencing attitude towards online shopping. Hence, online stores may strengthen their service through broadening product range and enabling multi-channel retail distribution with more delivery and pick-up op-tions. Past online shopping satisfaction is also another critical factor which influences consumers’ perceived benefits and attitude towards online shopping. Online stores are recommended to provide clear and accurate information so that consumers know clearly what they purchase. Products are to be delivered on-time and undamaged, which may help maintain high level of customer satisfaction and encourage repetitive purchases.

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Chapter 1 Introduction

1.1 Research Background

The advancement of information technology over the past few twenty years has created a great impact in mankind and it has changed the day to day operations of everyone as well as created new and more opportunities in new industries, revolutionis-ing the business environment of numerous sectors. One of the industries that have the heaviest impact is the retail sector, where pure-play online retailers have emerged, and traditional bricks-and-mortar shops have to alter their business models to keep pace with the trend.

There are many arguments and individual views on online shopping. Most would say that technology gives us convenience with 24-hour round the clock service and allows you to readily search and view information. Some would say online shopping also offers cost savings, including travel costs and product costs. However others might argue that there are fraud, security and privacy concerns, and online shopping would cause stress when there are hidden costs and terms and conditions are unclear.

The popularisation of smartphones and tablets has further driven the shift from physical shop visits to online information searching and purchases. A study in the United States showed that online shopping is mainly cherished for their ease of check-out, large variety of brands and products offered and the diversity of delivery options (UPS 2013). When compared with physical stores, online retailers have the advantage of saving overheads on rents and shop-floor staff. Hence one might reckon that the popu-larisation of e-commerce is driven by its competitive edge, i.e. lower price, but studies showed that the time saving and convenience online purchases offer are the major fac-tors that attract time-starved consumers (Bellman et al., 1999).

The information and communication technology infrastructure in Hong Kong is well developed, having 83% household broadband penetration in 2013 (OGCIO, 2014). A recent research also showed that the smartphone penetration in Hong Kong is as high as 87%, and tablet penetration has grown significantly from 30% in 2012 to 57% after a year (Nielson, 2013). A recent study from MasterCard (2013) showed that two-thirds of Hong Kong respondents shop online. Compared with the internet penetration, this shows that there are still massive opportunities for organisations to seize higher market share through satisfying changing customer behaviours in online shopping. Hence there is a need to investigate and identify what encourages Hong Kong con-sumers to go online when they want to make a purchase.

There have been various studies that look into consumer adoption of online shopping. Many of them studied with an information system adoption approach, as they expected that consumers would view online shopping as an innovative behaviour. Some based their studies on Davis’s Technology Acceptance Model (TAM) (1986) (Çelik & Yıl-maz, 2011; Gefen et al., 2003; Lin, 2007; Lim & Ting, 2012; Turan, 2012). Some based their studies on Ajzen’s Theory of Planned Behaviour (TPB) (George, 2004, Hansen et al., 2008; Lee & Ngoc, 2010; Lin, 2007). Online Shopping Acceptance Model developed by Zhou et al. (2007) will be used instead in this research, as the model includes an ex-tensive range of factors that have been proven to cause an influence on consumer adop-tion of online shopping in various studies.

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1.2 Research Objective

The key research of this paper is: How do different antecedents, for example, consumer demographics, personal traits like shopping orientation and innovativeness, normative beliefs, past online shopping experiences and satisfaction, benefits percep-tion, risk perception, attitude, etc., affect the adoption of online shopping in Hong Kong? This may help organisations in the e-commerce sector identify their consumer profile. This may also help them understand what factors give online shopping an advantage over traditional physical store shopping, what consumers need and expect when they go online shopping, and at the end equip these organisations derive their strategies to fully leverage the changing buying behaviour.

1.3 Structure of the Dissertation

Chapter 1 Introduction:

It provides an introduction on the dissertation. It provides the background on why the author draws upon the research topic and establishes the objective. It also pro-vides a profile on how the dissertation is structured.

Chapter 2 Literature Review:

In this chapter, literature of related disciplines will be reviewed. Classic mod-els of consumer buying behaviour and decision making process will be briefly discussed. We will look into Davis’s Technology Acceptance Model, which is the classic model when investigating user adoption of information systems. Ajzen’s Theory of Planned Behaviour will also be studied. Last but not least, Zhou et al.’s Online Shopping Acceptance Model, which summarised studies on online shopping adoption in various perspectives, will be studied. These would help identify what factors should be taken into consideration when hypotheses are developed and the questionnaire is designed for quantifying the relation-ship between various constructs.

Chapter 3 Conceptual Model:

In this chapter, hypotheses will be developed after examination of various lit-eratures. The conceptual model of factors that affect consumers’ adoption of online shopping will be established, based on the hypotheses outlined.

Chapter 4 Research Methodology:

In this chapter, the research question and objective will be revisited. The re-search methodology and study approach will be discussed. The instrument to be utilised, i.e. the questionnaire, will be discussed and reviewed. Reliability test will be done to en-sure that the questionnaire is reliable and consistently reproduce the construct it is mea-suring. Finally, the sampling method will be discussed.

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Chapter 5 Analysis and Findings:

In this chapter, analysis will be done on the data collected from the survey conducted. Respondents’ demographic statistics and online shopping behaviour will be presented. Descriptive statistics of various constructs will be examined. Correlation analysis will then be done to provide a quick overview on how constructs relate with each other. Finally regression analysis will be conducted to test the hypotheses estab-lished in Chapter 3.

Chapter 6 Discussions, Implications and Conclusion:

In this chapter, we will summarise our findings, theoretical implications. and managerial implications for business practitioners in the online retail sector. Limitations identified with this research will be discussed and areas for future research will be sug-gested.

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Chapter 2 Literature Review

2.1 Introduction

In this chapter, three models of user adoption of new technology or informa-tion system will be reviewed. These models have been widely used Technology accep-tance model (TAM) developed by Davis (1986) is a simple model that has been used by academics for studying user acceptance of new technologies, and recently for studying user acceptance of online shopping. Theory of Planned Behaviour (TPB) developed by Ajzen (1991) which has been applied for predicting behaviours will also be reviewed. Last but not least, Online Shopping Acceptance Model (OSAM) developed by Zhou et al. (2007) primarily for consumers’ adoption of online shopping will be reviewed and dis-cussed.

2.2 Online Shopping Acceptance Models

2.2.1 Technology Acceptance Model (TAM) (Davis, 1986)

Since the advancement of information technology, there have been models established by academics to determine factors that mediate the susceptibility of users or consumers towards new information systems. One of the most widely studied and ap-plied models is the Technology Acceptance Model developed by Davis in the 1980s. His work showed that perceived usefulness (PU) and perceived ease of use (PEOU) are the factors that determine the attitude towards new information systems that in turn affect the behavioural intention. This intention would lead to actual use of the system, which is considered as a successful launch of the new information system (Davis, 1985). The causal relationship proposed by the model is shown in Figure 1.

Figure 1 Technology Acceptance Model

!

Source: Adapted from Davis (1985)

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Later he also developed a set of measurement scales for these two variables - PE and PEOU (Davis, 1989). Since its establishment, the TAM has been widely applied as a basis for predicting user acceptance of new technology in various sectors, for in-stances, mobile phone adoption (Kwon & Chidambaram, 2000), internet usage (Moon & Kim, 2001), e-learning (Park, 2009; Al-Adwan et al., 2013; Sharma & Chandel, 2013), mobile-banking (Lule et al., 2012), etc. Lim and Ting (2012) applied TAM to investigate the adoption behaviour of online shopping customers and found that both PEOU and PU would have positive effect on attitude towards online shopping. Gefen et al. (2003) proved that customers were also influenced by their trust on the concerned online shop, apart from PEOU and PU suggested by TAM.

However, Brown et al. (2002) found that the effect of PU was much less sig-nificant on system acceptance in mandatory usage than in voluntary environment in the banking sector, while study of Shroff et al. (2011) on students’ behavioural intention on e-portfolio system had similar conclusion. Lai & Li (2005) found that PU is not as significant as there are other useful alternatives that can help users achieve similar goals.

Venkatesh and Davis developed an extension of TAM, named it Technology Acceptance Model 2 in 2000. It argued that perceived usefulness was mediated by so-cial influences like subjective norm, and by cognitive instrumental processes like job rel-evance and output quality (Venkatesh & Davis, 2000). The model is shown on Figure 2.

Figure 2 Technology Acceptance Model 2

!

Source: Adapted from Venkatesh & Davis (2000)

There have been various extensions to the classic model of TAM, as it was widely believed that PU and PEOU are not the only belief constructs that determine the behavioural intention. For instances, subjective norms (Malhotra & Galletta, 1999; Venkatesh & Davis, 2000; Amoako-Gyampah & Salam, 2004; Kim et al., 2009, Park,

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2009; Choi & Chung, 2012), system experience (Burton-Jones & Hubona, 2006), demo-graphics (Porter & Donthu, 2006; Li & Lai, 2011) were shown to have direct effect on the actual usage of a new information system.

Both TAM and TAM2 have provided a basis for explaining user adoption be-haviour of online shopping. However, it has only included the examination of perceived benefits, but not that of perceived risks, which have been proven to have considerable effect on consumer attitudes. Studies showed that consumers are reluctant to purchase online due to various perceived risks, such as security of private information and finan-cial transactions (Teo, 2002 & 2006).

2.2.2 Theory of Planned Behaviour (TPB) (Ajzen, 1991)

Fishbein and Ajzen developed the Theory of Reasoned Action (TRA) to ex-plain how different kinds of behaviour were predicted by the concerned behavioural in-tention, which was further determined by the attitude towards the behaviour and subjec-tive norm. Ajzen later extended TRA to the theory of Planned Behaviour (TPB) by adding the perceived behavioural control (Figure 3). Attitude towards a certain behaviour was said to be related to the behavioural beliefs, which linked the behaviour to the perceived outcome and certain attributes like cost incurred. Subjective norm was said to be related to the normative beliefs that were concerned with the likelihood that important individuals or group members would approve or disapprove this certain behaviour. The perceived behavioural control was said to be related to the control beliefs that were concerned with how easy or difficult the behaviour was and how the behaviour was facilitated.

Figure 3 Theory of Planned Behaviour

! Source: Adapter from Ajzen (1991)

Various studies on online purchasing behaviour through the TPB framework turned out with different conclusions. Hansen et al. (2008) examined both TRA and TPB through the application of these models in online grocery shopping. They suggested that attitude towards the behaviour and subjective norm had significant effect on the behav-ioural intention. However, perceived behavioural control was found not to be one of the determinants, as they explained that information technology infrastructure might be read-ily available and consumers did not perceive any major obstacles in performing online

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shopping. This might be the same case in Hong Kong, as the facilitating conditions such as internet infrastructure and smart devices are well developed and readily available. The study of George (2004) suggested that perceived behavioural control was a deter-minant in addition to attitude towards online shopping, but subjective norm was not sta-tistically significant. Lee and Ngoc (2010) supported the TPB framework and added trust as the fourth determinant on the behavioural intention through the investigation of online shopping intentions of Vietnamese students.

2.2.3 Online Shopping Acceptance Model (OSAM) (Zhou et al., 2007)

Zhou et al. (2007) developed a reference model (Figure 4) for explaining cus-tomer acceptance of online shopping through a survey of related studies. Their in-depth literature review revealed that an extensive number of factors have been proven by em-pirical studies to influence customers’ online shopping intention and in turn the actual online shopping behaviour. The constructs identified by the model are illustrated in the following section.

Figure 4 Online Shopping Acceptance Model

!

Source: Adapter from Zhou et al. (2007)

2.2.3.1 Online shopping

Customer behaviour of online shopping was identified to solely dependent on the behavioural intention of online shopping. This is analogous to TAM and TAM2, but is different from TPB, which identified perceived behavioural control as a factor influencing the behaviour.

2.2.3.2 Online experience

Zhou et al. identified online experience as the first step towards actual pur-chases. Online experience was said to be developed through interaction with the web-site and easy navigation between pages. It was also said to be a direct and indirect mix of “online functionality, information, emotions, cues, stimuli and products/services” (Con-stantinides, 2004). Lee and Joshi (2007) distinguished between technology usage and

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service encounter contexts in online experience. Online experience is analogous to shopping experience in bricks-and-mortar retail shops, which generates positive emotion that may trigger purchases . In physical stores shopping experience is affected by shop layout, sales representative attitudes, check-out process, etc.

Good online experience is brought by easy account registration, simple check-out process, user-friendly navigation, accurate product information and clear pictures, etc. (JupiterResearch, 2006). Bad online experience, for instances, lack of product in-formation and unclear delivery costs, causes purchase abandonment (LivePerson, 2013). This is also analogous to perceived ease of use (PEOU), as identified in TAM and proven in various studies to have an influence on the attitude towards online shopping (Amoroso & Hunsinger, 2009; Kamarulzaman, 2008; Rizwan et al., 2013, ) or directly on the behavioural intention to online shopping (Lee et al., 2006, Lee & Huang, 2009).

Although satisfaction also comprises interaction experiences with the website (Lee & Joshi, 2007), it is easier to separate the whole online shopping satisfaction into pre-purchase online experience and post-purchase satisfaction in this study. Pre-pur-chase online experience includes interactions between the consumer and the website, impression of overall appearance of online stores and the experience throughout the process from information searching to actual purchases.

In OSAM, internet experience is negatively associated with the online experi-ence such that novice users enjoy online experience more than experienced users. On-line experience is said to be influenced by shopping motivation. Goal-oriented utilitarian customers are less affected by online experience, while hedonic customers who are “window-shoppers” enjoy interactive environment more than pure-text environment.

2.2.3.3 Online Shopping Intention

Online shopping behaviour has been said to be directly associated with the behavioural intention in TAM, TPB and OSAM. However, continuous online shopping in-tention is to be distinguished from first time shopping intention. First time shopping can be either shopping at a new website or shopping at online websites instead of traditional bricks-and-mortar stores. Zhou et al. also identified that past online shopping satisfaction had a greater effect on continuous buying intention, while innovativeness and perceived usefulness had a greater moderating effect on first time online intention.

In OSAM, online experience, consumers’ innovativeness, attitude towards on-line shopping, normative beliefs and satisfaction are positively associated with online shopping intention, while shopping motivation and shopping orientation have an influ-ence on online shopping intention.

2.2.3.4 Past Online Shopping Satisfaction

Online shopping satisfaction is generated through online shopping purchases, and was said to be the post-acceptance attitude formed through various stages during the whole purchase process, including information searching and product selection, per-sonal information registration and check-out, after sales enquiries, returns or refund, etc. The pre-purchase interaction with the website as well as online sales and technical rep-resentatives will be regarded as online experience, while post-check-out experience is classified as satisfaction for easier division of two constructs in this research for easier and clearer explanation. Post-check-out experience and satisfaction is dependent on customer services, reliability and product and service portfolio (Zeng et al., 2009).

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In OSAM, consumers’ perceived outcome and confirmation of expectation from past experiences are positively associated with their satisfaction with online shop-ping. Consumers with higher perceived usefulness are expected to have higher satisfac-tion. Past experiences which meet consumer expectations enhances satisfaction.

2.2.3.5 Attitude towards Online Shopping

Attitude was defined as “a person’s relatively consistent evaluations, feelings, and tendencies towards an object or idea” (Kotler & Armstrong, 2014). Attitude towards online shopping is the subjective evaluation of this behaviour, for instances, online shop-ping is fun, convenient and safe, etc. In OSAM, attitude towards online shopping was directly influenced by the perceived outcome of the behaviour.

2.2.3.6 Perceived outcome

Zhou et al. replaced perceived usefulness in TAM with perceived outcome, which covers both the perceived benefits and the perceived risks. Perceived benefits refer to the perceived advantages of online shopping over traditional physical store shopping, for instances, wider product range, lower retail prices, accessibility regardless of location and time zones, etc. Zhou et al. added perceived risks into the equation, as trust and security have been proven to be an important factor influencing consumers’ attitude towards online shopping (Bhatnagar et al., 2000; Park & Kim, 2003; Teo, 2002, 2006; Yatigamman, 2010). Perceived risks can be divided into financial risk, product risk and delivery risk. Financial risk is associated with the Internet as the transaction medi-um, which puts consumers at risk of losing money via credit card fraud and leakage of private information, while product risk is associated with consumers’ belief on whether the product purchased and received will function to their expectations (Bhatnagar et al., 2000). Delivery risk is associated with consumers’ belief on whether the product will be delivered on time.

In OSAM, positive satisfaction from past experiences and higher internet ex-periences are positively associated with the perceived outcome. Past online shopping experience has a negative impact on the perceived outcome of shopping with a specific merchant. For easier management of data and analysis in this study, the construct per-ceived outcome was separated into two constructs, perceived benefits and perceived risks.

2.2.3.7 Shopping motivation

Utilitarian consumers want to purchase goods online for saving time and mon-ey, while hedonic consumers browse online shop websites for information gathering, so-ciality, fun and enjoyment (Close & Kukar-Kinney, 2010). Utilitarian consumers are goal oriented and value convenience, selection, information availability and control. Hedonic consumers on the other hand value positive sociality and experiential features, for in-stance, video streaming, forums, auction (Wolfinbarger & Gilly, 2000). Study of Wolfin-barger and Gilly (2001) also found that task-oriented motives are more common among online shoppers than are experiential purposes. It has also been studied that individual consumer’s shopping motivation is stable over time and across different retail domains (Büttner et al., 2014) In OSAM, internet experience influences shopping motivation such that experienced users are more likely to shop online for task-oriented activities while novice users are more likely to target for experiential activities.

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2.2.3.8 Innovativeness

Innovativeness was defined to be the degree to which an individual is relative-ly earlier in adopting an innovation in the time-adoption approach from Rogers & Shoe-maker (1971). Innovative consumers are more willing to try new technologies (Javadi et al., 2012). This is related to the consumer adoption of online shopping, as online shop-ping is viewed as an innovation compared with shopping at bricks-and-mortar stores. It was suggested that innovativeness had a positive impact on the online shopping be-haviour (Javadi et al., 2012; Keisidou et al., 2011); on the behavioural intention (Ahmed et al., 2013; Rizwan et al., 2013); and on the attitude towards online shopping (Hsu & Bayarsaikhan, 2012).

2.2.3.9 Shopping Orientation

Vijayasarathy (2003) suggested that shopping orientations can help online stores identify and understand attributes of consumers who more readily adopt online shopping. Vijayasarathy & Jones (2000) identified seven types of shopping orientations. Consumers of home shopping orientation prefer shopping from home to minimise travel time. In contrast, mall shoppers prefer to visit physical stores in shopping centres. Eco-nomic shoppers target at lowest price and best quality. Personalised shoppers value in-teraction with individual salesperson that they know. Ethical shoppers tend to shop in local stores in the community rather than large retail chains. Convenience shoppers val-ue efficiency in their shopping activities. Enthusiastic shoppers seek fun and enjoyment during the shopping process. In OSAM, shopping orientation is affected by the con-sumer’s demographics, such as gender and culture.

2.2.3.10 Normative Beliefs

Normative beliefs are concerned with “the likelihood that important referent individuals or groups approve or disapprove of performing a given behaviour” (Ajzen, 1991, p. 195). Referents can be friends, parents, siblings, co-workers, members in the same social network, etc. Normative beliefs have been proven in empirical studies to have an impact on the behavioural intention to online shopping (Al-maghrabi & Dennis, 2010; Amoako-Gyampah & Salam, 2004; Choi & Chung, 2012; Chuchinprakarn, 2005; Kim et al., 2009; Malhotra & Galletta, 1999; Park, 2009; Venkatesh & Davis, 2000; Yuli-hasri et al., 2011). In OSAM, Zhou et al. identified consumers’ level of collectivism is pos-itively associated with their normative beliefs.

2.2.3.11 Internet Experience

Some empirical studies do show that internet experience has a positive influ-ence on intention to online shopping (Bhatnagar et al. 2000; Chang et al., 2005, Farag et al., 2006), while some showed that it was not a significant predictor on the attitude or the intention (Amoroso & Hunsinger, 2009; Bigne et al., 2005). However, experienced users tend to have lower perceived risk (Bhatnagar & Ghose, 2004).

2.2.3.12 Consumer Demographics

There is mixed result when investigating the relationship between behavioural intention and consumer demographics. Farag et al. (2006) identified that male, younger customers with higher education have higher tendency to go for online shopping. While Yatigamman (2010) suggested that occupation and income were the significant factors, Jusoh & Ling (2012) suggested age and income, rather than occupation, are the domi-

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nant factors. Brosdahl & Almousa (2013) found significant differences in attitudes and behavioural intention when comparing online shoppers in the United States and in the Saudi Arabia. Gong et al.’s study (2012) had similar findings when comparing online consumers in the mainland China and in the United States.

2.3 Summary

Various models of consumers’ acceptance of online shopping have been re-viewed. OSAM provided a more extensive explanation through including most factors that have been proven to affect consumers’ adoption of online shopping. Hence we will utilise OSAM as a basis for developing hypotheses to be tested in this research, and es-tablishing our conceptual research model for predicting consumers’ adoption of online shopping in Hong Kong.

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Chapter 3 Conceptual Model

3.1 Introduction

After the review of literatures on different online shopping adoption models and their applications, we will develop hypotheses for testing relationships between various constructs. We will then summarise these hypotheses with a conceptual model.

3.2 Hypotheses Development

3.2.1 Online Shopping

The relationship between online shopping and online shopping intention was proven in various studies (Al-Jabari et al., 2012; Amoroso & Hunsinger, 2009; Li & Huang, 2009; Lin, 2007; Turan, 2012). Hence hypothesis H1 is developed.

H1: Online shopping intention has a positive effect on actual online shopping behaviour.

3.2.2 Online Shopping Intention

It was said that positive online experience could generate positive emotion dur-ing navigation and might trigger purchases. The positive influence of online experience on online shopping intention was proven in various studies (Al-Maghrabi & Dennis, 2010; Gefen et al., 2003; Haq, 2012; Li & Huang, 2009; Su et al., 2009; Turan, 2012). Hence hypothesis H2 is developed.

H2: Online experience has a positive effect on online shopping intention.

It was said in OSAM that consumer innovativeness is positively associated with online shopping intention and this was also proven by Citrin et al.’s (2000) and Hsu’s (2012) studies. This may be because online shopping may be interpreted as an innova-tive behaviour when compared with traditional bricks-and-mortar shopping. Hence hy-pothesis H3 is formulated.

H3: Innovativeness has a positive effect on online shopping intention

It has been widely supported that attitude was one of the most influencing con-structs on online shopping intention, and the relationship was proven in various studies (Amoroso & Hunsinger, 2009; Hansen et al., 2008; Heijden et al., 2003; Hsu, 2012; Lim & Ting, 2012; Lin, 2007; Thananuraksakul, 2007; Turan, 2012). Hence hypothesis H4 is developed.

H4: Attitude towards online shopping has a positive effect on online shopping intention

It was said that beliefs of or recommendations from friends, families or close acquaintances may contribute a positive impact on online shopping intention (Al-Maghrabi & Dennis, 2010; Al-Jabari et al., 2012; Amoroso & Hunsinger, 2009; Çelik & Yılmaz, 2011; Hansen et al., 2008; Thananuraksakul, 2007; Turan, 2012). Hence hy-pothesis H5 is developed.

H5: Normative beliefs has a positive effect on online shopping intention

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It was proposed in the OSAM and proven in study from Haq (2012) that past online shopping satisfaction has a positive influence on online shopping intention. Hence hypothesis H6 is developed.

H6: Past shopping satisfaction has a positive effect on online shopping intention

It was proposed in the OSAM that shopping orientation and shopping motiva-tion have an influence on online shopping intention. There are various orientations de-fined in previous studies, for instances convenience orientation, economic orientation, recreation orientation, and we identified two main orientations for easier differentiation, namely task orientation and experience orientation. Task-oriented consumers target for convenience, economic benefits, information availability and product availability, while experience-oriented consumers shop online for fun, interests, surprises and sociality. Hence hypotheses H7 and H8 are developed.

H7: Task Orientation has a positive effect on online shopping intention

H8: Experience Orientation has a positive effect on online shopping intention

3.2.3 Attitude Towards Online Shopping

It was proposed and proven by various studies that perceived usefulness, or perceived benefits in OSAM notation, is positively associated with consumers’ attitude towards online shopping (Amoroso & Hunsinger, 2009; Çelik & Yılmaz, 2011; Hsu, 2012; Lim & Ting, 2012; Lin, 2007; Turan, 2012), while perceived risks is negatively associated with consumers’ attitude towards online shopping (Heijden et al., 2003; Hsu, 2012). OSAM combined two constructs into one single construct – Perceived Outcome, but they are treated as separated variables in this study for ease of instrument design and analy-sis. Hence hypotheses H9 and H10 are developed.

H9: Perceived benefits has a positive effect on attitude towards online shopping

H10: Perceived risks has a negative effect on attitude towards online shopping

3.2.4 Perceived Benefits

It was proposed in the OSAM that higher level of past online shopping satisfac-tion would result in a higher level of perceived benefits. Hence hypothesis H11 is devel-oped.

H11: Past shopping satisfaction has a positive effect on perceived benefits

3.2.5 Perceived Risks

It was proposed in the OSAM that past online shopping satisfaction was nega-tively associated with the perceived risks with online shopping. Hence hypothesis H12 is developed.

H12: Past shopping satisfaction has a negative effect on perceived risks

3.3 Research Model After the development of all the hypotheses, we summarise by formalising

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the below model to investigate the relationship between various factors and online shop-ping intention and the actual online shopping behaviour in Hong Kong.

Figure 5 Conceptual Research Model

!

3.4 Conclusion

In this chapter, we have developed the hypotheses to be tested in this re-search. They are mostly developed based on OSAM established by Zhou et al. (2007). Internet Experience as included in OSAM is not included in the hypotheses as Hong Kong consumers are expected to be highly experienced and competent in using the In-ternet. In the next chapter, the research methodology will be explained, including the questionnaire design, sampling methodology and reliability test.

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Chapter 4 Research Methodology

4.1 Introduction

The emergence of the e-commerce platform has reshaped the playing field of the retail industry. There are international online stores successfully established which can sell products to customers virtually in the whole globe, while entrepreneurs can also set up their own retail business with minimal investment. Traditional physical stores are struggling to compete with their multichannel strategy, offering customers with more or-der and delivery options. It is crucial for these players to understand the rationale behind customers going for online shopping.

The goal of the study is to examine the effects of customers’ demographics, internet experience, perceived benefits, perceived risks, normative beliefs, attitude to-wards online shopping, innovativeness, online experience, shopping motivations and past online shopping satisfaction on customers’ online shopping intention and hence their actual online shopping behaviour.

An exploratory study approach was first applied to identify key factors that had been identified by past literatures to have an impact on the adoption of online shop-ping. Interviews with individuals were made to learn about whether Hong Kong online shoppers differ from online shoppers studied in past literatures. This exploratory study helps the author draw up the conceptual model described in section 3.

After these explorations, an explanatory study approach was pursued as the relationship between key factors and the online shopping intention and actual behaviour is to be found. Statistical tests are also applied to investigate the causal relationship be-tween variables. A cross-sectional study is applied and this study is investigating the on-line shopping behaviour of Hong Kong online shoppers in the past year.

4.2 Questionnaire Design

In order to investigate the effects of various factors on the online shopping intention and the actual online shopping behaviour, a questionnaire has been designed to include two general parts. The questionnaire accesses all variables of the conceptual model through 53 questions. The first part collects information about different variables in order to support or reject the hypotheses, while the second part collects information about different demographic characteristics of respondents, which can be used to under-stand the variations in different categories and their impact on the online shopping inten-tion and the actual behaviour. The questions are adapted from previous studies. After discussion with pilot survey respondents, slight amendments were done so that scales are more understandable and have a better fit with the Hong Kong consumer market. The scales of the questionnaire and the references are listed in Table 1.

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Table 1 Scales of the Questionnaire

Construct Items Reference

Perceived Benefits

Convenience: 1. It is convenient for me to shop online 2. I can easily access internet and shop

whenever I like Product Selection: 3. There is a broader selection of retailers,

products and brands online 4. Products from everywhere around the

world are available online Economic Benefits: 5. Products are bought cheaper online

Forsythe et al., 2006 Bhatnagar et al., 2000

Perceived Risks

Financial risks: 6. Online retailers are not trustworthy 7. I feel it is insecure to put my personal

information or credit card number during online purchases

8. I may be overcharged for shipping or handling during online shopping

Product risks: 9. I cannot see or try or experience the real

product when I shop online 10. I may buy the wrong product due to

inaccurate information provided by the online store

11. The product I order online may be damaged when it arrives.

Time risks: 12. I may not get the product on time 13. Online stores cannot confirm when the

product will arrive.

Forsythe et al., 2006

Normative Beliefs

14. My family and my friends would think I should shop online

15. My family and my friends shop online 16. When I decide what and where to buy

products, my family’s and my friend’s opinions influence my decision.

17. I will have no problem shopping online if my friends and family are doing it without any problem

Chuchinprakarn, 2005 Javadi et al., 2012

Innovative-ness

18. I would be the first few amongst my friends to visit and try new online store.

19. I like to explore new online stores. 20. When I hear about a new online store, I

would pay it a visit (I would browse it).

McKnight et al., 2002

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Task Orientation

21. I purchase products on online stores instead of physical stores for convenience, as it saves time and travel.

22. I purchase products on online stores instead of physical stores for wider product selection

23. I purchase products on online stores instead of physical stores as there are more detailed information available

Wolfinbarger & Gilly, 2000

Experience Orientation

24. I enjoy the fun of surfing various online store sites

25. I like to share experiences and interests with other online shoppers

26. I like to find surprises during my online shopping. i.e. Unknown bargains, new products, etc.

Wolfinbarger & Gilly, 2000

Attitude towards Online Shopping

27. I have fun shopping online 28. My general view of online shopping is

positive. 29. Using online sites to buy a product rather

than from a physical store is a good idea

Amoroso & Hunsinger, 2009 Javadi et al., 2012 Rizwan et al., 2013

Online Experience

30. Online shopping has an easy and simple check-out process

31. Online stores provide in-depth information about products

32. I feel comfortable and easy navigating through pages on online stores

33. Online stores offer interactive features such as product animation (e.g. zoom, rotation, etc.)

34. Online stores allow personalisation, such a s a c c o u n t m a n a g e m e n t , recommendations according to past purchase records, etc.

35. Online stores’ customer service provides support to inquires about processes or products promptly

Shergill & Chen, 2005 JupiterResearch, 2006

Online Shopping Intention

36. I intend to purchase products online in the future

37. It is likely that I will continue to purchase products online in the future

38. When I need to buy a particular product, I would search an online store.

39. It is likely that I will shop online instead of visiting a physical store

Lim & Ting, 2012

Online Shopping Behaviour

40. How long have you been using the Internet shopping?

41. How many times have you purchased products online during the past year?

42. How much have you spent (HKD) on online purchases during the past year?

Javadi et al., 2012

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The first section asks about variables that are included in the research model. It was developed based on questionnaires used in previous literatures (Table 1). A pilot survey was conducted with four people to evaluate how well the questionnaire was un-derstood. Some of the questions were rephrased for clarity and some were removed for ambiguity. For instance, “The idea of using Web sites to buy a product or service is ap-pealing” and “I like the idea of buying a product or service via online Web sites” were combined into “My general view of online shopping is positive”. All items were rated with a seven-point Likert Scale from “Strongly Disagree” (1) to “Strongly Agree” (7), apart from the items for online shopping behaviour. The second section inquires about the demographic information of the respondents, including gender, age, education level, in-come and occupation.

4.3 Reliability Test

The scale of the instrument should be reliable and consistent and should consistently reproduce the construct it is measuring. The internal consistency was tested by Cronbach’s alpha. Result showed that all of the constructs were with α higher than 0.7, except Normative Beliefs and Online Shopping Behaviour. Analysis showed that cor-relation of item 16 and 17, “When I decide what or where to buy, my family’s and my friend’s opinions influence my decision” and “I will have no problem shopping online if my family and my friends are doing it without any problem”, were low, causing α to be 0.59 which is lower than the acceptable level 0.6 as suggested by Churchill (1979). After dropping these two items, α of Normative Beliefs increased to 0.67 which is barely ac-ceptable and cannot be further increased by dropping any other item. The reliability for the construct Online Shopping Behaviour is barely acceptable and cannot be further im-proved through dropping any item. The reliability test result was shown in Table 2. Ex-cept Normative Beliefs and Online Shopping Behaviour, all items employed provide rea-sonable internal consistency in the reliability analysis.

Satisfaction 43. Product information are accurate on online stores

44. Delivery or pick-up options offered by online stores are convenient

45. Purchased product from online stores are delivered on time

46. Purchased product from online stores are delivered undamaged

47. There has been no hassle I request for return or refund.

Vegiayan et al., 2013

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Table 2 Reliability Test

4.4 Sampling

Non-probability sampling method was utilised in this study, as the target of the study is the whole Hong Kong population. Convenience sampling is selected due to limited time and resources. Convenience sampling targets respondents who are readily available (Latham, 2007) and is usually used for collecting a large number of completed questionnaires quickly (Zikmund, 1997). The questionnaire was distributed to 115 re-spondents in Causeway Bay and Mong Kok on 21 June 2014, 29 June 2014, and 5 July 2014. These two areas were selected as the people flows of Causeway Bay and Mong Kok are the highest in Hong Kong Island and Kowloon respectively. The respondents are to be experienced online shoppers. The purpose of the study and the questions were explained to the respondents so that they could well understand the items and could smoothly fill in the questionnaire. A total of 106 questionnaires were selected and the rest was discarded due to incomplete responses. After completing the questionnaires, the data was input into a Microsoft Excel sheet for further regression analysis.

Construct Items Cronbach’s Alpha Coefficient

Perceived Benefits 5 0.7433

Perceived Risks 8 0.7888

Normative Beliefs (Removed Item 16 & 17) 2 0.6677

Innovativeness 3 0.7840

Task Orientation 3 0.7127

Experience Orientation 3 0.7610

Attitude Towards Online Shopping 3 0.8269

Online Experience 6 0.8123

Online Shopping Intention 4 0.8582

Online Shopping Behaviour 3 0.6699

Past Online Shopping Satisfaction 5 0.8229

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Chapter 5 Analysis and Findings

5.1 Profile of Respondents

5.1.1 Demographics

In the questionnaire survey, 106 respondents including different gender, age, occupation, monthly income and education level were surveyed. The proportions of male and female respondents are 45.3% and 54.7% respectively. Nearly half of the respon-dents are of age between 26 and 33, while respondents of age between 34 and 41 make up one third of the responded population. This shows similar result as a recent survey done by MasterCard (2013) and Lim & Ting’s study (2012) and shows that the online shopping population is still relatively young in Hong Kong. One-tenth of the respondents are of age between 42 and 49, and only 4.7 are of age between 18 and 25. Minority of the respondents are of age between 50 and 57 (2.8%) and above 57 (1.9%).

Nearly half of the respondents (42.5%) have completed their undergraduate degree, while another 40.6% have completed postgraduate degree. A little more than ten percent (11.3%) have completed high diploma, associate degree or similar professional training. The rest (5.7%) have completed secondary school, while no respondents have not completed their college study. 30% of respondents have a monthly income between 26000 and 36000 Hong Kong dollars. One quarter of respondents are earning between 18000 and 26000 dollars, while another quarter are earning between 36000 and 50000 dollars. The rest of the samples earn above 50000 dollars (7.5 %), between 8000 and 12000 (1.9%) and less than 8000 (1.9%). Respondents’ occupations are widely dis-tributed amongst various areas, for example, business and financial operations, architec-ture and engineering, production and manufacturing, management, etc. Table 3 lists the sample profile.

Table 3 Demographic Profile of Respondents

Variable Frequency Percentage

Gender

Male 48 45.3%

Female 58 54.7%

Age

Under 18 0 0.0%

18 - 25 5 4.7%

26 - 33 51 48.1%

34 - 41 34 32.1%

42 - 49 11 10.4%

50 - 57 3 2.8%

Above 57 2 1.9%

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Table 3 Demographic Profile of Respondents (Continued)

Variable Frequency Percentage

Education Level

Did not complete secondary school 0 0.0%

Completed secondary school 6 5.7%

Completed High Diploma / Associate Degree / Other Professional Training 12 11.3%

Completed Undergraduate Degree 45 42.5%

Completed Postgraduate Degree 43 40.6%

Monthly Income

8000 or less 2 1.9%

8001 - 12000 2 1.9%

12001 - 18000 9 8.5%

18001 - 26000 27 25.5%

26001 - 36000 32 30.2%

36000 - 50000 26 24.5%

50001 or above 8 7.5%

Occupation

Business and Financial Operations 21 19.8%

Architecture and Engineering 15 14.2%

Production and Manufacturing 15 14.2%

Management 10 9.4%

Office/Administrative Support 10 9.4%

Sales 7 6.6%

Media and Communications 6 5.7%

Computer and Mathematics 5 4.7%

Art and Design 4 3.8%

Education, Training and Library 3 2.8%

Personal Care and Service 3 2.8%

Legal Occupations 2 1.9%

Transportation 2 1.9%

Life Science 1 0.9%

Social Science 1 0.9%

Community and Social Service 1 0.9%

Page 24: Factors Affecting Consumers' Adoption of Online Shopping in Hong Kong

5.1.2 Product Categories

Hong Kong shoppers purchase online a wide range of product categories. The most popular products are airline tickets and travel packages. The comparably pop-ular products are apparel, fashion, jewellery and accessories. Hong Kong online shop-pers also buy financial products likes stocks and insurances, and buy software like mu-sic, movies, games and computer software. Figure 6 provides a clear picture of what re-spondents bought in the past 12 months.

Figure 6 Product Categories bought in the past year

! 5.1.3 Online Shopping Behaviour

In the samples, over 60% of respondents have online shopping experience of more than 2 years, with 36.8% of them shopping for 2 – 4 years and 27.4% shopping for more than 4 years (Figure 7). The online shopping frequency is much more discrete, with 26.4% having shopped 1 – 3 times, 24.5% having shopped 4 – 6 times and 22.6% hav-ing shopped online for more than 15 times during the past year (Figure 8). Nearly half of the respondents (44.3%) have spent over 5000 Hong Kong dollars (Figure 9).

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Figure 7 Online Shopping Experiences of the Respondents

!

Figure 8 Online Shopping Habit (Frequency)

!

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Figure 9 Online Shopping Spending

!

5.1.4 Descriptive Statistics

Table 4 shows the descriptive statistics of the constructs and the individual items. Respondents perceive that online shopping can offer them with high level of bene-fits (mean = 5.2), including convenience (mean = 5.6), easy accessibility (mean = 5.7) as well as broad selection (mean = 5.2). Respondents also acknowledge the value of being able to buy products from online stores in other geographical locations (mean = 5.0) with a cheaper price (mean = 4.7).

Respondents still perceive a certain level of risks in online shopping (mean = 4.6). Respondents do not have a high perception that online stores are not trustworthy (mean = 3.9), and that they may be overcharged for the whole buying process (mean = 4.1). Product risk is the highest concern amongst all. Respondents are not comfortable with the inability of seeing or experiencing the real product during online purchases (mean = 5.3). They also fear that they may buy the wrong product because of misleading or incorrect information provided by online stores (mean = 5.2).

Respondents have more confidence in online shopping when their families or friends do not have problems purchasing products online (mean = 5.0), and a majority of respondents have families or friends buying products online (mean = 4.9). Respondents innovativeness is fair, with an overall mean 4.3. They may not be the first to visit and try new stores (mean = 4.0), but they fairly like exploring new Web store sites (mean = 4.5).

The attitude towards online shopping among respondents is fairly high (mean = 5.0). The general view of online shopping is positive (mean = 5.2), and the respon-dents have fun shopping online (mean = 5.0). Respondents also have a good experi-ence with online stores (mean = 4.7), valuing the easy and simple check-out process (mean = 5.0) and comfortable navigation (mean = 4.9). They also appreciate the possi-bility of personalisation (mean = 4.9) and the interactive features (mean = 4.8) offered by online stores. The future online shopping intention is also a high level (mean = 4.9), but

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respondents also acknowledge that they will not abandon purchases through physical stores, with a mean 4.3 for “It is likely that I will shop online instead of visiting a physical store” comparing with mean over 5 on other three items concerning future online shop-ping intention.

The past online shopping satisfaction of respondents is also fairly high (mean = 4.7). They are moderately satisfied with the information provided (mean = 4.7), the availability of different delivery or pick-up options (mean = 4.9), on-time delivery (mean = 4.7) and products undamaged (mean = 4.8). In contrast, respondents feel a certain de-gree of hassle when they request for return or refund (mean = 4.3 for “There has been no hassle when I request for return or refund”).

Table 4 Descriptive Statistics

Construct Item Minimum Maximum Mean Standard Deviation

Perceived Bene-fits

It is convenient for me to shop online.

1 7 5.6 1.3

I can easily access internet and shop whenever I like.

1 7 5.7 1.1

There is a broader selection of retailers, products and brands online.

2 7 5.2 1.1

Products from everywhere around the world are avail-able online

2 7 5.0 1.2

Products are bought cheap-er online

2 7 4.7 1.0

5.2

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Table 4 Descriptive Statistics (Continued)

Construct Item Minimum Maximum Mean Standard Deviation

Perceived Risks

Online retailers are not trustworthy

2 7 3.9 1.0

I feel it is insecure to put my personal information or credit card number on online purchases

2 7 4.4 1.3

I may be overcharged for shipping or handling during online shopping

1 7 4.1 1.2

I cannot see or try or experience the real product when I shop online

1 7 5.3 1.4

I may buy the wrong product due to inaccurate information provided by the online store

2 7 5.2 1.2

The product I order online may be damaged when it arrives

2 7 4.8 1.2

I may not get the product on time

2 7 4.6 1.1

Online stores cannot confirm when the product will arrive.

2 7 4.3 1.3

4.6

Normative Be-liefs

My family and my friends would think I should shop online

1 7 4.4 1.1

My family and my friends shop online

1 7 4.9 1.2

When I decide what or where to buy, my family’s and my friend's opinions influence my decision

1 7 4.3 1.4

I will have no problem shop-ping online if my family and my friends are doing it with-out any problem

2 7 5.0 1.1

4.7

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Table 4 Descriptive Statistics (Continued)

Construct Item Minimum Maximum Mean Standard Deviation

Innovativeness

I would be the first few amongst my friends to visit and try new online stores

1 7 4.0 1.5

I like to explore new online stores

1 7 4.5 1.5

When I hear about a new online store, I would pay it a visit (I would browse it).

1 7 4.5 1.4

Average: 4.3

Attitude Towards Online Shopping

I have fun shopping online 2 7 5.0 1.1

My general view of online shopping is positive

2 7 5.2 1.0

Using the Internet to buy a product rather than from a physical store is a good idea

1 7 4.8 1.1

5.0

Task Orientation

I purchase products on online stores instead of physical stores for convenience, as it saves time and travel.

1 7 4.8 1.3

I purchase products on online stores instead of physical stores for wider product selection

2 7 4.7 1.3

I purchase products on Web stores instead of physical stores as there are more detailed information available

1 7 4.0 1.3

4.5

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Table 4 Descriptive Statistics (Continued)

Construct Item Minimum Maximum Mean Standard Deviation

Experience Orientation

I enjoy the fun of surfing various online store sites

2 7 4.8 1.2

I like to share experiences and interests with other online shoppers

1 7 4.3 1.3

I like to find surprises during my online shopping, e.g. unknown bargain, new products, etc.

1 7 4.6 1.5

4.6

Online Experience

Online shopping has an easy and simple check-out process

2 7 5.0 1.0

Online stores provide in-depth information about products

1 7 4.4 1.2

I feel comfortable and easy navigating through pages on online stores

2 7 4.9 1.0

Online stores offer interactive features such as product animation (e.g. zoom, rotation, etc.)

2 7 4.8 1.1

Web stores allow personalisation, such as account 4, recommendations according to past purchase records, etc.

2 7 4.9 1.0

Online stores’ customer service provides support to inquires about processes or products promptly

1 7 4.4 1.1

4.7

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Table 4 Descriptive Statistics (Continued)

5.2 Effect of Demographics

In order to study the effect of the demographic profiles on various constructs built in this research, t-test and one-way analysis of variance (ANOVA) are used. T-test analysis is applied to compare the mean differences of each variable between male and female respondents. As shown in Table 5, male and female respondents did not have significant difference (p value < 0.05) in the mean scores for all variables at 95 % confi-dence level. As a result, gender was shown not to cause any influence on all the vari-ables.

Construct Item Minimum Maximum Mean Standard Deviation

Online Shopping Intention

I intend to purchase prod-ucts online in the future

1 7 5.0 1.2

It is likely that I will continue to purchase products online in the future

1 7 5.2 1.2

When I need to buy a par-ticular product, I would search for an online store

2 7 5.1 1.2

It is likely that I will shop online instead of visiting a physical store

2 7 4.3 1.2

Average: 4.9

Past Online Shopping Satisfaction

Product information are ac-curate on online stores

2 7 4.7 1.0

Delivery or pick-up options offered by online stores are convenient

3 7 4.9 0.9

Purchased products from online stores are delivered on time

2 7 4.7 0.9

Purchased product from online stores are delivered undamaged

3 7 4.8 1.0

There has been no hassle when I request for return or refund

2 7 4.3 1.1

4.7

Page 32: Factors Affecting Consumers' Adoption of Online Shopping in Hong Kong

Table 5 T-Test Analysis on Gender

One-way ANOVA is applied to test the difference of variables in terms of age groups. As shown in Table 6, only Perceived Benefits (PB) showed significant difference across different age groups (p = 0.044) at 95 % confidence level. All other variables did not show significant difference. This shows that the age of respondents have an effect on their Perceived Benefits of online shopping.

t-value p-value

Perceived Benefits 0.824 0.414

Perceived Risks 0.825 0.413

Normative Beliefs 0.409 0.684

Innovativeness 0.943 0.351

Task Orientation 0.703 0.485

Experience Orientation 0.094 0.925

Attitude Towards Online Shopping 0.721 0.474

Online Experience 0.806 0.424

Online Buying Intention 0.376 0.709

Online Shopping Behaviour 0.836 0.407

Past Online Shopping Experience 0.433 0.667

Page 33: Factors Affecting Consumers' Adoption of Online Shopping in Hong Kong

Table 6 ANOVA on Age Groups

One-way ANOVA is applied to test the difference of variables in terms of edu-cation level groups. As shown in Table 7, only the actual online shopping behaviour (OSB) showed significant difference across different education level groups (p = 0.021) at 95 % confidence level. All other variables did not show significant difference. This shows that the education level of respondents has an effect on their actual online shop-ping behaviour.

PB PR NB INN TO EO ATOS

OE OSI OSB POSS

Mean (18-25) 5.320 4.625 3.267 4.800 4.933 4.867 5.400 5.000 5.350 3.933 4.720

Mean (26-33) 5.180 4.471 3.033 4.294 4.425 4.595 5.007 4.820 4.907 5.092 4.659

Mean (34-41) 5.435 4.787 3.216 4.392 4.598 4.647 5.049 4.593 4.897 5.167 4.718

Mean (42-49) 5.055 4.602 3.212 4.455 4.455 4.545 4.939 4.833 4.795 5.121 4.945

Mean (50-57) 5.400 4.708 2.889 4.000 4.000 3.889 4.333 4.111 4.750 4.444 4.000

Mean (Above 57)

3.600 3.750 2.333 3.500 2.833 3.167 4.667 4.167 4.250 3.500 3.600

Overall Mean 5.232 4.586 3.104 4.343 4.465 4.572 5.006 4.725 4.896 5.016 4.672

SS Within Groups

61.553

60.008

47.365

156.498

103.285

121.936

82.853

58.187

100.187

172.797

56.488

df Within Groups

100.000

100.000

100.000

100.000

100.000

100.000

100.000

100.000

100.000

100.000

100.000

MS Within Groups

0.616 0.600 0.474 1.565 1.033 1.219 0.829 0.582 1.002 1.728 0.565

SS Between Groups

7.338 3.503 2.271 3.159 7.755 6.010 2.476 3.315 2.046 12.622 4.567

df Between Groups

5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000 5.000

MS Between Groups

1.468 0.701 0.454 0.632 1.551 1.202 0.495 0.663 0.409 2.524 0.913

F 2.384 1.167 0.959 0.404 1.502 0.986 0.598 1.140 0.409 1.461 1.617

P-value 0.044 0.330 0.447 0.845 0.196 0.430 0.702 0.344 0.842 0.209 0.162

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Table 7 ANOVA on Education Level Groups

where Mean 1 is the mean scores of the education level group “Completed secondary school”; Mean 2 is the mean scores of the education level group “Completed High Diploma / Associate Degree / Other Professional Training”; Mean 3 is the mean scores of the education level group “Completed Undergraduate Degree”; Mean 4 is the mean scores of the education level group “Completed Postgraduate Degree”

One-way ANOVA is applied to test the difference of variables in terms of monthly income groups. As shown in Table 8, no variable showed significant difference across different monthly income groups at 95 % confidence level. This shows that the monthly income of respondents did not have any effect on various constructs in our on-line shopping model.

PB PR NB INN TO EO ATOS

OE OSI OSB POSS

Mean 1 5.267 4.813 2.833 5.000 4.667 5.000 5.167 4.361 5.125 5.278 4.367

Mean 2 4.950 4.604 3.167 4.194 4.250 4.694 4.611 4.583 4.542 3.917 4.467

Mean 3 5.364 4.503 3.207 4.296 4.415 4.659 5.156 4.815 4.917 5.081 4.804

Mean 4 5.167 4.637 3.016 4.341 4.550 4.388 4.938 4.721 4.942 5.217 4.633

Overall Mean 5.232 4.586 3.104 4.343 4.465 4.572 5.006 4.725 4.896 5.016 4.672

SS Within Groups

66.961

62.778

48.332

156.704

109.814

124.862

82.098

60.103

100.303

168.574

59.134

df Within Groups

102.000

102.000

102.000

102.000

102.000

102.000

102.000

102.000

102.000

102.000

102.000

MS Within Groups

0.656 0.615 0.474 1.536 1.077 1.224 0.805 0.589 0.983 1.653 0.580

SS Between Groups

1.930 0.734 1.305 2.953 1.226 3.084 3.232 1.399 1.931 16.845 1.922

df Between Groups

3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000 3.000

MS Between Groups

0.643 0.245 0.435 0.984 0.409 1.028 1.077 0.466 0.644 5.615 0.641

F 0.980 0.397 0.918 0.641 0.379 0.840 1.338 0.791 0.655 3.397 1.105

P-value 0.405 0.755 0.435 0.590 0.768 0.475 0.266 0.502 0.582 0.021 0.351

Page 35: Factors Affecting Consumers' Adoption of Online Shopping in Hong Kong

Table 8 ANOVA on Monthly Income Groups

PB PR NB INN TO EO ATOS

OE OSI OSB POSS

Mean (8000 or Less)

5.400 4.125 3.500 5.833 5.167 5.667 6.167 4.750 6.125 6.333 4.100

Mean (8001-12000)

5.800 5.188 3.333 5.500 5.000 5.000 5.333 5.250 5.125 4.167 5.300

Mean (12001-18000)

5.733 4.569 3.407 4.741 5.074 4.481 5.481 5.148 5.444 4.704 5.244

Mean (18001-26000)

5.030 4.620 2.963 4.148 4.370 4.815 4.802 4.747 4.528 4.642 4.504

Mean (26001-36000)

5.156 4.605 3.094 4.000 4.396 4.635 4.938 4.667 4.938 5.365 4.681

Mean (36001-50000)

5.262 4.553 3.103 4.577 4.449 4.192 4.936 4.583 4.827 4.821 4.646

Mean (50001 or above)

5.375 4.484 3.125 4.500 4.125 4.458 5.292 4.729 5.219 5.750 4.625

Overall Mean 5.232 4.586 3.104 4.343 4.465 4.572 5.006 4.725 4.896 5.016 4.672

SS Within Groups

64.452

62.205

47.845

144.704

104.817

119.537

78.337

58.695

91.727

166.658

55.860

df Within Groups

99.000

99.000

99.000

99.000

99.000

99.000

99.000

99.000

99.000

99.000 99.000

MS Within Groups

0.651 0.628 0.483 1.462 1.059 1.207 0.791 0.593 0.927 1.683 0.564

SS Between Groups

4.439 1.306 1.791 14.953

6.222 8.409 6.993 2.808 10.506

18.760 5.195

df Between Groups

6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000

MS Between Groups

0.740 0.218 0.299 2.492 1.037 1.401 1.165 0.468 1.751 3.127 0.866

F 1.137 0.346 0.618 1.705 0.980 1.161 1.473 0.789 1.890 1.857 1.534

P-value 0.347 0.911 0.715 0.128 0.443 0.333 0.195 0.581 0.090 0.096 0.175

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One-way ANOVA is applied to test the difference of variables in terms of occu-pation groups. As shown in Table 9, only experience orientation showed significant dif-ference across different occupation groups (p = 0.034) at 95 % confidence level. This shows that the the occupation of respondents had significant effect on their experience orientation.

Table 9 ANOVA on Occupation Groups

PB PR NB INN TO EO ATOS OE OSI OSB POSS

Mean (Business & Financial Operations)

5.152 4.589 3.048 4.524 4.508 4.619 5.143 4.913 4.964 4.762 4.848

Mean (Architecture & Engineering)

5.173 4.592 3.111 4.333 4.733 4.356 4.689 4.656 4.833 5.178 4.373

Mean (Production & Manufacturing)

5.427 4.783 3.444 4.156 4.844 4.778 5.200 4.544 5.217 4.778 4.760

Mean (Management) 4.640 4.088 3.167 4.600 4.167 4.733 5.000 4.800 4.750 5.233 4.520

Mean (Office/Administrative Support)

5.240 4.550 3.000 4.400 3.900 4.333 4.967 4.783 4.125 5.433 4.920

Mean (Sales) 5.543 4.679 2.762 3.333 4.143 4.095 4.905 4.357 5.071 5.000 4.543

Mean (Media & Comms)

5.267 4.625 3.444 4.333 4.111 4.667 4.944 4.778 5.208 4.944 4.833

Mean (Computer & Mathematics)

5.680 5.100 3.067 4.000 4.267 3.800 4.733 4.867 4.700 4.067 4.800

Mean (Art & Design) 5.050 4.250 2.917 4.583 4.750 5.250 4.833 4.708 4.625 5.750 4.600

Mean (Education, Training & Library)

5.000 4.833 2.444 3.889 3.667 4.111 4.222 4.556 4.167 4.333 3.800

Mean (Personal Care & Service)

5.133 3.958 3.111 6.000 5.444 6.556 6.222 5.000 5.833 6.444 4.800

Mean (Legal & Operations)

5.600 4.813 2.500 4.000 4.667 3.667 6.000 4.500 5.125 5.000 5.000

Mean (Transportation)

5.600 4.438 2.667 5.167 4.500 5.167 4.833 5.000 5.875 5.167 4.500

Mean (Life Science) 6.400 5.000 4.333 4.333 6.333 6.667 6.333 6.000 6.750 6.000 6.000

Mean (Social Science)

5.000 5.375 3.667 5.000 4.333 4.000 4.667 4.000 4.000 4.000 3.200

Mean (Community & Social Service)

5.400 4.500 3.000 3.000 4.000 3.000 3.667 3.833 3.750 5.000 4.800

Overall Mean 5.232 4.586 3.104 4.343 4.465 4.572 5.006 4.725 4.896 5.016 4.672

SS Within Groups 60.634 56.273 41.765 137.110

93.082 97.294 70.275 55.534 81.283 164.345 51.180

df Within Groups 90.000 90.000 90.000 90.000 90.000 90.000 90.000 90.000 90.000 90.000 89.000

MS Within Groups 0.674 0.625 0.464 1.523 1.034 1.081 0.781 0.617 0.903 1.826 0.575

SS Between Groups 8.257 7.238 7.871 22.547 17.958 30.651 15.055 5.969 20.950 21.074 9.845

df Between Groups 15.000 15.000 15.000 15.000 15.000 15.000 15.000 15.000 15.000 15.000 15.000

MS Between Groups 0.550 0.483 0.525 1.503 1.197 2.043 1.004 0.398 1.397 1.405 0.656

F 0.817 0.772 1.131 0.987 1.158 1.890 1.285 0.645 1.546 0.769 1.141

P-value 0.656 0.756 0.341 0.476 0.319 0.034 0.228 0.830 0.106 0.708 0.333

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5.3 Correlation Analysis

The correlation matrix is then examined for investigating the relationship be-tween different constructs. Although correlation does not imply causal relationship, it does identify statistical dependence and provides an initial test on how well the hypothe-ses are supported. The correlation matrix for our data is shown in Table 10. With a sam-ple of 106, the correlation coefficient needs to be over 0.191 so that we have 95% confi-dence level that the correlation is not by chance. The correlation coefficients that are un-der 0.191 were made grey on the table for easier review.

Table 10 Correlation Matrix

where PB denotes Perceived Benefits; PR denotes Perceived Risks; NB denotes Normative Beliefs; ATOS denotes Attitude Towards Online Shopping; OE denotes Online Experience; INN denotes Innovativeness; TO denotes Task Orientation; EO denotes Experience Orientation; POSS denotes Past Online Shopping Satisfaction; OSI denotes Online Shopping Intention; OSB denotes Online Shopping Behaviour

We found moderate correlation between online shopping intention and actual online shopping behaviour (r = 0.37). Correlation coefficient (r = 0.57) between online experience and online shopping intention is also fairly high. The correlation between in-novativeness and online shopping intention is significant. The relationship between atti-tude towards online shopping and online shopping intention is highly correlated (r = 0.80). The correlation between Normative beliefs and online shopping intention are moderately significant (r = 0.51). There is a moderate correlation (r = 0.54) between past online shopping satisfaction and online shopping intention. The correlation coefficient between Task Orientation and Online Shopping Intention is relatively high (r = 0.63). There is also a fair degree of correlation between experience orientation of respondents and online shopping intention (r = 0.49).

There is a moderate level of correlation (r = 0.56) between Perceived Benefits and Attitude Towards Online Shopping. The result of the questionnaire shows that there

PB PR ATOS INN OE TO EO NB OBI POSS OSB

PB 1.00

PR 0.25 1.00

ATOS 0.56 0.06 1.00

INN 0.25 -0.10 0.50 1.00

OE 0.45 0.00 0.62 0.31 1.00

TO 0.41 0.02 0.58 0.52 0.50 1.00

EO 0.20 -0.09 0.59 0.58 0.54 0.54 1.00

NB 0.36 0.00 0.46 0.31 0.45 0.34 0.37 1.00

OBI 0.62 0.06 0.80 0.48 0.57 0.63 0.49 0.51 1.00

POSS 0.51 0.01 0.58 0.18 0.67 0.38 0.34 0.39 0.54 1.00

OBB 0.26 -0.06 0.38 0.45 0.11 0.25 0.35 0.25 0.37 0.11 1.00

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is no significant correlation between Perceived Risks and Attitude Towards Online Shop-ping (r = 0.06). There is also a fair degree of correlation (r = 0.51) between Perceived Benefits and Past Online Shopping Satisfaction. The correlation coefficient between Perceived Risks and Past Online Shopping Satisfaction is not significant (r = 0.01).

5.4 Regression Analysis

Regression analysis will be utilised to test the hypotheses and to allow valida-tion of the instrument. When the p-value of the regression is at the level 0.05 (p > 0.05), the model is considered insignificant and the hypothesis cannot be supported. The soft-ware Microsoft Excel Home and Business 2010 is used. All regression data is shown on Appendix II.

5.4.1 Online Shopping Behaviour

Regression analysis is run with Online Shopping Behaviour as the dependent variable and Online Shopping Intention as the independent variable. Online Shopping Intention explains 13.5% (R2 = 0.135) of the variance in Online Shopping Behaviour. On-line Shopping Intention is statistically significant with p-value lower than 0.05 (p=0.000) with regression coefficient β 0.49. This provides strong support for hypothesis H1.

5.4.2 Online Shopping Intention

Regression analysis is run with Online Shopping Intention as the dependent variable, and Online experience, Innovativeness, Attitude Towards Online Shopping, Normative Beliefs, Past Online Shopping Satisfaction, Task Orientation and Experience Orientation as the independent variables. The regression model provides a high expla-nation (70.6%) of the variance in Online Shopping Intention, with a R Square value 0.706. Attitude Towards Online Shopping (p = 0.000), Normative Beliefs (p = 0.019) and Task Orientation (p = 0.003) are statistically significant with p-value lower than 0.05, which provides strong support for hypothesis H4, H5 and H7. Attitude Towards Online Shopping is the strongest predictor of Online Shopping Intention with β = 0.62, followed by Normative Beliefs (β = 0.22) and Task Orientation (β = 0.21). In contrast, Online expe-rience (p = 0.910), Innovativeness (p = 0.403), Past online shopping satisfaction (p = 0.351) and Experience Orientation (p = 0.266) are not statistically significant with p-value higher than 0.05. The hypotheses H2, H3, H6 and H8 are not supported.

Regression was run again with only the significant independent variables, i.e. attitude towards online shopping, normative beliefs and task orientation. All three vari-ables are significant with confidence level 99% (p < 0.01), and the model explains 69.8% (R2 = 0.698) of the variance in online shopping intention. Attitude towards online shop-ping has the biggest influence (β = 0.65), while normative beliefs and task orientation have similar degree of impact (β = 0.23 & β = 0.22 respectively).

5.4.3 Attitude Towards Online Shopping

Regression analysis is run with Attitude Towards Online Shopping as the de-pendent variable, and Perceived Benefits, Perceived Risks, Online Experience, Innova-tiveness, Past Online Shopping Satisfaction, Task Orientation and Experience Orienta-tion as the independent variables. The regression model explains 63.8 % (R2 = 0.638) of the variance in Attitude Towards Online Shopping. Perceived Benefits is statistically sig-nificant with p-value lower than 0.05 (p = 0.000), which provides strong support for hy-pothesis H9, while perceived Risks is not statistically significant with p-value higher than

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0.05 (p = 0.289). The hypotheses H10 are not supported. In addition, Past Online Shop-ping Satisfaction (p = 0.022) and Experience Orientation (p = 0.005) are also statistically significant with p-value lower than 0.05. Perceived Benefits is the strongest predictor (β = 0.29) of Attitude Towards Online Shopping, followed by Past Online Shopping Satisfac-tion (β = 0.24) and Experience Orientation (β = 0.20). In contrast, Online Experience (p = 0.168), Innovativeness (p = 0.065), and Task Orientation (p = 0.143) are not statistically significant in determining respondents’ Attitude Towards Online Shopping.

Regression was run again with only the significant independent variables, i.e. perceived benefits, past online shopping satisfaction and experience orientation. All three variables are significant with confidence level 99% (p < 0.01), and the model ex-plains 60.0% (R2 = 0.600) of the variance in online shopping intention. Perceived bene-fits has the highest influence (β = 0.39) on the attitude towards online shopping. Experi-ence orientation and past online shopping satisfaction also have similar degree of effect (β = 0.36 & β = 0.30 respectively).

5.4.4 Perceived Benefits

Regression analysis is run with Perceived Benefits as the dependent variable, and Past Online Shopping Satisfaction as the independent variable. The regression model explains 26.0% (R2 = 0.260) of the variance in Perceived Benefits. Past Online Shopping Satisfaction is statistically significant with p-value lower than 0.05 (p=0.000) with a fairly high influence (β = 0.54). These provide strong support for hypothesis H10.

5.4.5 Perceived Risks

Regression analysis is run with Perceived Risks as the dependent variable, and Past Online Shopping Satisfaction as the independent variable. The regression model does not explain any of the variance in Perceived Risks. Past Online Shopping Satisfaction is not statistically significant with p-value higher than 0.05 (p=0.890) with virtually no impact (β = 0.01). These do not offer any support for hypothesis H12.

5.5 Summary

In this chapter, the demographic profiles of the respondents were reviewed. Respondents are evenly distributed between male and female. High proportion of re-spondents are relatively young and in their late twenties or early thirties, and they are highly educated. Respondents work in various sectors. Respondents bought various products in the past year, with travel products and apparel & accessories being the most popular. Most respondents are experienced in online shopping, and have spent a gener-ous amount of earnings in online shopping.

Descriptive statistics of responds on the questionnaire were also examined. Convenience and easy accessibility are the most valued benefits, as consumers can shop online anytime and anywhere. Respondents do not have a high perception that on-line stores are not trustworthy, but the product risk brought with online shopping is the highest concern, as they cannot see the actual product and may buy the wrong product due to inaccurate information. General attitude towards online shopping is fairly positive and the normative beliefs are moderately high. Respondents are also relatively satisfied with past online shopping experiences.

Correlation analysis was done to provide a quick overview of how various constructs relate with each other. Finally, regression analysis was conducted to test hy-

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potheses established in Chapter 3. As previous studies, we found that the actual online shopping behaviour is significantly affected by respondents’ online shopping intention which is significantly influenced by their attitude, normative beliefs and task orientation. Their attitude towards online shopping was in turn related with their perceived benefits, past online shopping satisfaction and experience orientation.

In the next chapter, we will summarise our findings, theoretical implications. and managerial implications for business practitioners in the online retail sector. Limita-tions identified with this research will be discussed and areas for future research will be suggested.

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Chapter 6 Discussions, Implications and Conclusion

6.1 Summary of Main Findings

The purpose of this study is to create a model to study consumers’ acceptance of online shopping in Hong Kong, based on the Online Shopping Acceptance Model and related studies. A large part of the proposed model can be verified with the findings from the instrument. The proposed model may be useful for further academic research on re-lated perspective, as well as businesses which have the desire to expand their offerings through the e-commerce platform.

We found that the actual online shopping behaviour is in part affected by con-sumers’ online shopping intention in Hong Kong. Their intentions are largely influenced by their attitudes towards online shopping, and also moderately affected by the con-sumers’ task orientation and normative beliefs. Attitude towards online shopping is influ-enced by consumers’ perceived benefits, experience orientation and past online shop-ping satisfaction. Perceived benefits is also affected by past online shopping satisfaction.

6.2 Theoretical Implications

Table 11 provides a summary of correlation analysis and regression analysis on each of the hypotheses.

Table 11 Summary of Findings on Hypotheses

Hypothesis Independent Variable

Dependent Variable

Correlation Analysis

Sig Support (sig<= 0.05)

Regression Analysis

Sig Support (sig<= 0.01)

H1 Online Shopping Intention

Online Shopping Behaviour

r = 0.367 0.000 Yes β = 0.494 0.000 Yes

H2 Online Experience

Online Shopping Intention

r = 0.566 0.000 Yes NA NA No

H3 Innovativeness

Online Shopping Intention

r = 0.476 0.000 Yes NA NA No

H4 Attitude Towards Online Shopping

Online Shopping Intention

r = 0.799 0.000 Yes β = 0.650 0.000 Yes

H5 Normative Beliefs

Online Shopping Intention

r = 0.512 0.000 Yes β = 0.233 0.010 Yes

H6 Past Online Shopping Satisfaction

Online Shopping Intention

r = 0.537 0.000 Yes NA NA No

H7 Task Orientation

Online Shopping Intention

r = 0.627 0.000 Yes β = 0.215 0.001 Yes

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It can be concluded that a higher online shopping intention results in a higher actual online shopping behaviour (H1). This is in line with previous studies (Al-Jabari et al., 2012; Amoroso & Hunsinger 2009; Li & Huang, 2009; Lin, 2007; Turan, 2012).

We discovered a mixed result on the relationship between online experience and online shopping intention (H2). The correlation between online experience and on-line shopping intention was found to be significant, which is in line with most previous studies (Al-Maghrabi & Dennis, 2009; Gefen et al., 2003; Haq, 2012; Li & Huang, 2009; Su et al., 2009; Turan, 2012), but online experience was not proven as one of the signifi-cant predictors of online shopping intention during the regression analysis, which is simi-lar to the result found by Hansen et al. (2008). This may be due to the advancement of information technology which facilitates the development of website quality. Online re-tailers popular in Hong Kong may have designed their websites well with easy navigation and simple check-out processes, on which online shoppers may find less hassle in mak-ing purchases.

We had a mixed finding on the relationship between respondents’ innovative-ness and online shopping intention (H3). The correlation between innovativeness and online shopping intention was found to be significant, which is in line with previous stud-ies (Citrin et al., 2000; Hsu & Bayarsaikhan, 2012), but it was not a significant predictors of online shopping intention in the regression analysis. This may be because online shopping is no longer viewed as a new retail channel as it has been a long period since the first introduction of e-commerce.

It can be concluded that a more positive attitude towards online shopping re-sults in a higher online shopping intention (H4), which is in line with previous studies (Al-Jabari et al., 2012; Amoroso & Hunsinger, 2009; Hansen et al., 2008; Heijden et al., 2003; Hsu & Bayarsaikhan, 2012; Lim & Ting, 2012; Lin, 2007; Thananuraksakul, 2007; Turan, 2012). Attitude towards online shopping was also proven to be the strongest pre-dictor of online shopping intention during the regression analysis.

It can be concluded that higher normative beliefs against online shopping re-sults in a higher online shopping intention (H5), which is similar to the results obtained in

H8 Experience Orientation

Online Shopping Intention

r = 0.493 0.000 Yes NA NA No

H9 Perceived Benefits

Attitude Towards Online Shopping

r = 0.563 0.000 Yes β = 0.387 0.000 Yes

H10 Perceived Risks

Attitude Towards Online Shopping

r = 0.059 0.548 No NA NA No

H11 Past Online Shopping Satisfaction

Perceived Benefits

r = 0.510 0.000 Yes β = 0.542 0.000 Yes

H12 Past Online Shopping Satisfaction

Perceived Risks

r = 0.014 0.887 No NA NA No

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previous studies (Al-Jabari et al., 2012; Al-Maghrabi & Dennis, 2009; Amoroso & Hun-singer, 2009; Hansen et al., 2008; Thananuraksakul, 2007; Turan, 2012). Normative be-liefs were also found to be one of the predictors of online shopping intentions in the re-gression analysis.

We had a mixed finding on the relationship between past online shopping sat-isfaction and online shopping intention (H6). Although the correlation between was proven to be significant, past online shopping satisfaction did not emerge as a significant predictor for online shopping intention. This is similar to the result found by Haq (2012).

We had a mixed finding on the effect of Hong Kong consumers’ shopping ori-entation on online shopping intention (H7 and H8). The correlation between task orienta-tion degree and online shopping intention was found significant, and task orientation was also one of the significant predictors of online shopping intention. This is similar to the conclusion found by Jayawardhena et al. (2007). Experience orientation was also found significantly correlated with online shopping intention, but was proven not significantly predicting the online shopping intention in regression analysis.

It can be concluded that perceived benefits is a strong predictor of attitude to-wards online shopping (H9), with significance found during correlation and regression analysis. This is similar to conclusion of previous studies (Amoroso & Hunsinger, 2009; Hsu & Bayarsaikhan, 2012; Lim & Ting, 2012; Lin, 2007; Turan 2012). However, we did not find that perceived risks had a significant impact on attitude towards online shopping in both correlation and regression analysis (H10).

It can be concluded that past online shopping satisfaction had a significant im-pact on perceived benefits (H11), proven through both correlation and regression analy-sis. Past online shopping satisfaction was found not to have significant effect on per-ceived risks (H12).

The final model proven through regression analysis is shown in Figure 6.

Figure 6 Analysis results of Research Model

!

* p < 0.01

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6.3 Managerial Implications

In line with previous studies, we found that consumers’ online shopping inten-tion has a positive effect on the actual online shopping behaviour, including frequency and purchasing amount. The regression model is quite robust for predicting online shop-ping intention (R2 = 0.698), with task orientation, normative beliefs and attitude towards online shopping as the significant constructs. This implies that families and friends may have a certain amount of influence on online shopping intention, and business can leverage the potential through social marketing and word of mouth marketing. Marketing strategies that creates incentives to encourage and introduce family and friends or group discounts may promote awareness and purchases.

Attitude towards online shopping has a high positive influence on online shop-ping intention (β = 0.65). A robust regression model has been developed for attitude to-wards online shopping (R2 = 0.638), with experience orientation, past online shopping satisfaction and perceived benefits being the significant constructs. Past online shopping satisfaction has both direct and indirect influence on attitude, with indirect impact through perceived benefits. Both task and experience orientations of consumers have an effect on online shopping intention, with experience orientation influencing indirectly through attitude.

It is surprising that perceived risks did not turn out as a significant variable in affecting consumers’ attitude or online shopping intention. This may be due to the online payment security established and on-time delivery offered by online stores. It is also sur-prising that online experience did not contribute to the model. This may be due to the fact that a high proportion of Hong Kong consumers are time conscious with a busy and hectic lifestyle, which leads to the emphasis on convenience and economic advantages that online shopping can bring.

As perceived benefits is one of the crucial constructs influencing attitude to-wards online shopping, in order to attract online purchases, online stores may strength-en their service through broadening product range and enabling multi-channel retail dis-tribution with more delivery and pick-up options which can offer higher convenience for time-starved consumers which are typical in Hong Kong. Past online shopping satisfac-tion is also another critical factor which influences consumers’ perceived benefits and attitude towards online shopping. Online stores may provide clear and accurate informa-tion so that consumers know clearly what they purchase. Products are to be delivered on-time and undamaged, which may help maintain high level of customer satisfaction and encourage repetitive purchases.

6.4 Limitations and Areas for Future Research

Convenience sampling was utilised, with respondents within a certain area at a certain time. Hence samples may not fully represent the whole Hong Kong consumer population. Generalisation must be done with caution. The survey may also suffer a non-response bias, and the sample size is relatively small. To fully evaluate Hong Kong con-sumers’ adoption of online shopping, a large sample size with random sampling is more desirable.

Product category was not considered in this paper. It was proven in previous studies (Bhatnager et al., 2000; Keisidou et al., 2011) that attitude towards online shop-ping is different across different product categories. Future research may study the dif-

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ferentiations of the results with different products in Hong Kong.

The main focus of this research was to investigate what encourages Hong Kong consumers to choose online stores against traditional bricks-and-mortar stores. Future research may identify which attributes equip an online store with a competitive edge against other online competitors. In addition, we focus on business-to-consumer (B2C) online purchases. Future research may be done on the business-to-business (B2B) sector.

This study does not review whether respondents make deliberate purchases or impulse purchases. A study performed by User Interface Engineering (2001) showed that 40% of money spent on online stores is attributed to impulse purchases. Future study may review what factors stimulate users’ impulse purchases during information searching or web surfing. Future study may also review whether there is value in having physical stores and how traditional bricks-and-mortar stores can survive with the emer-gence of pure-play online stores.

6.5 Conclusion

The retail sector has been truly revolutionised by the advancement in infor-mation technology. Pure-play online retailers have emerged and traditional bricks-and-mortar shops have to alter their business models to keep pace with the trend. It was shown that there are still massive opportunities for organisations to seize higher market share through satisfying changing customer behaviours in online shopping. Hence we studied how the adoption of online shopping is affected by various constructs.

It was found that Hong Kong consumers’ actual online shopping behaviour was significantly influenced by their online shopping intention. We developed a robust regression model for predicting online shopping intention and attitude towards online shopping. Online shopping intention is highly affected by normative beliefs, attitude to-wards online shopping and the degree of consumers’ task orientation. We also devel-oped a strong regression model for predicting attitude towards online shopping, where the perceived benefits, past online shopping satisfaction and the degree of consumers’ experience orientation. This would help online retail practitioners formalise their business strategy in the competitive playing field.

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Appendix II – Regression Results

Online Shopping Behaviour as the dependent variable

SUMMARY OUTPUT - Online Shopping Behaviour

Regression Statistics

Multiple R 0.366836585

R Square 0.13456908

Adjusted R Square 0.126247629

Standard Error 1.242154909

Observations 106

ANOVA

df SS MS F Significance F

Regression 1 24.95156182 24.95156 16.17135 0.000109834

Residual 104 160.4666772 1.542949

Total 105 185.418239

CoefficientsStandard Error t Stat P-value

Intercept 2.596846379 0.613487364 4.232926 4.99E-05

Online Buying Intention 0.494028806 0.122851133 4.021361 0.00011

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Online Shopping Intention as the dependent variable

SUMMARY OUTPUT - Online Shopping Intention

Regression Statistics

Multiple R 0.840079

R Square 0.705732

Adjusted R Square 0.684713

Standard Error 0.554058

Observations 106

ANOVA

df SS MS F Significance F

Regression 7 72.14947 10.30707 33.57572 2.19E-23

Residual 98 30.08402 0.30698

Total 105 102.2335

CoefficientsStandard Error t Stat P-value

Intercept -0.23025 0.388269 -0.59302 0.554532

Online Experience 0.012555 0.110709 0.113407 0.90994

Innovativeness 0.049136 0.058467 0.840395 0.402733

Attitude Towards Online Shopping 0.624919 0.094617 6.604714 2.07E-09

Normative Beliefs 0.220412 0.092295 2.388129 0.018851

Past Online Shopping Satisfaction 0.095233 0.101561 0.937693 0.350707

Task Orientation 0.214546 0.071168 3.014627 0.003276

Experience Orientation -0.07913 0.070751 -1.11839 0.266133

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Online Shopping Intention as the dependent variable with insignificant independent vari-ables removed

SUMMARY OUTPUT - Online Shopping Intention

Regression Statistics

Multiple R 0.835488

R Square 0.698041

Adjusted R Square 0.68916

Standard Error 0.550137

Observations 106

ANOVA

df SS MS F Significance F

Regression 3 71.36315 23.78772 78.598 2.04E-26

Residual 102 30.87034 0.30265

Total 105 102.2335

CoefficientsStandard Error t Stat P-value

Intercept -0.03973 0.330873 -0.12007 0.904663

Task Orientation 0.215116 0.064692 3.325217 0.001229

Attitude Towards Online Shopping 0.649738 0.078079 8.32157 4.08E-13

Normative Beliefs 0.232813 0.088402 2.633578 0.009764

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Attitude Towards Online Shopping as the dependent variable

SUMMARY OUTPUT – Attitude Towards Online Shopping

Regression Statistics

Multiple R 0.798454

R Square 0.63753

Adjusted R Square 0.611639

Standard Error 0.561787

Observations 106

ANOVA

df SS MS F Significance F

Regression 7 54.39985 7.771407 24.62384 4.68E-19

Residual 98 30.92929 0.315605

Total 105 85.32914

CoefficientsStandard Error t Stat P-value

Intercept -0.35554 0.516928 -0.68779 0.49321

Perceived Benefit 0.285998 0.088081 3.246995 0.001597

Perceived Risks 0.030157 0.074669 0.403869 0.687189

Online Experience 0.153384 0.110505 1.388026 0.168277

Innovativeness 0.109422 0.058655 1.865497 0.065103

Past Online Shopping Satisfaction 0.238685 0.102366 2.331677 0.021765

Task Orientation 0.106793 0.072293 1.477228 0.142821

Experience Orientation 0.20456 0.071615 2.856379 0.005232

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Attitude Towards Online Shopping as the dependent variable with insignificant indepen-dent variables removed

SUMMARY OUTPUT – Attitude Towards Online Shopping

Regression Statistics

Multiple R 0.774532

R Square 0.5999

Adjusted R Square 0.588132

Standard Error 0.578539

Observations 106

ANOVA

df SS MS F Significance F

Regression 3 51.18894 17.06298 50.97873 3.25E-20

Residual 102 34.1402 0.334708

Total 105 85.32914

CoefficientsStandard Error t Stat P-value

Intercept -0.05022 0.429687 -0.11688 0.907188

Perceived Benefit 0.387163 0.081072 4.775573 6.01E-06

Past Online Shopping Satisfaction 0.300538 0.089682 3.351139 0.00113

Experience Orientation 0.355797 0.05435 6.546439 2.41E-09

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Perceived Benefits as the dependent variable

SUMMARY OUTPUT – Perceived Benefits

Regression Statistics

Multiple R 0.510004

R Square 0.260105

Adjusted R Square 0.25299

Standard Error 0.700083

Observations 106

ANOVA

df SS MS F Significance F

Regression 1 17.91884 17.91884 36.56039 2.35E-08

Residual 104 50.9721 0.490116

Total 105 68.89094

CoefficientsStandard Error t Stat P-value

Intercept 2.701211 0.424053 6.369988 5.24E-09

Past Online Shopping Satisfaction 0.541744 0.089596 6.046519 2.35E-08

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Perceived Risks as the dependent variable

SUMMARY OUTPUT – Perceived Risks

Regression Statistics

Multiple R 0.013582

R Square 0.000184

Adjusted R Square -0.00943

Standard Error 0.781392

Observations 106

ANOVA

df SS MS F Significance F

Regression 1 0.011716 0.011716 0.019188 0.890097

Residual 104 63.49963 0.610573

Total 105 63.51135

CoefficientsStandard Error t Stat P-value

Intercept 4.521371 0.473303 9.552803 6.72E-16

Past Online Shopping Satisfaction 0.013852 0.100002 0.138521 0.890097