Analysis of E-commerce in Kuwait
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Chapter one: Introduction
This chapter provides general background information regarding online purchasing behavior with an insight into the advantages and disadvantages of e-commerce in general and then specifically in Kuwait. The history of online shopping and internet retailers is presented to better understand e-purchasing behavior alongside a description of general theories of consumer online purchaser behavior and online shopping in Kuwait. The problem definition, research questions and methodology and limitations of the study are then presented, concluding with an outline of the thesis structure.
With advances in technology, specifically in the field of electronics and telecommunications, direct business and commerce with new retail approaches have emerged in recent decades to transform the business world. Due to the increase in the number of internet users and developing network technology, new forms of trade have grown from these advances particularly in Electronic Commerce (EC) a term introduced by Kalakota and Whinston in 1997. Electronic commerce has become one of the primary characteristics of the internet era and a significant method of doing business. According to Jelassi and Enders (2005) EC includes e-trading of digital and physical goods all trading steps: online marketing, online ordering, e-payment and distribution. Kalakota and Whinston (1997) pointed out that EC has two forms: business-to-consumer (B2C) and business-to-business (B2B). According to Molla and Licker (2001) B2C retailers offer their products and services to their customers. In the last decade, Khalifa and Liu (2003) stated that ‘we have witnessed a substantial growth of internet based on services, both from traditional companies and pure internet business that are developing online services'.
Despite apparent growth there are no reliable statistics concerning E-commerce in Kuwait. However there are indications that the volume of e-commerce in Kuwait is growing slowly as discussed by Al-Sabah (2009) Kuwait Financial Forum, the Central Bank Governor stating "We expect growth but so far we have not found a proper to be estimated for 2010, it depends on so many variables". In research shown in Economist Information in 2006 involving over 100 countries regarding availability of e-commerce, Kuwait came 50th. As the business world recognised the advantages of such socioeconomic changes, Kuwait began to take note of the advantages of electronic trading and commerce including the set up and development of measurements of electronic trading facilities and venues across the country (Al-Shati, 2009). As e-commerce is newly introduced in Kuwait, in order for Kuwaiti firms to reach world standards there needs to be research in different contexts of e-commerce such as online retailing to utilize opportunities and avoid risk. As observed by Lin (2003)
the key to success in e-commerce depends on knowing customers and studying a customer's viewpoint. The internet has singlehandedly created a concept shift away from more traditional methods of shopping. Studies by Joines et al. (2003) indicate the number of internet users is constantly increasing which signifies online purchasing is also increasing. Oppenheim and Ward (2006) agreed with Joines et al. (2003) explaining rapid increase was due to the growth of use of broadband technology combined with a change in consumer behaviour. Hollensen (2004) added that the internet has developed into a "new" distribution channel and evolution of this channel and e-commerce. Constantinides (2004) pointed out that in the influence of the consumer the first step was to identify certain impact aspects when purchasing online regarded as dimensions.
Numerous and varied studies have been conducted worldwide to identify the advantages and disadvantages of e-shopping. Bridges and Florsheim (2008) argue that online shopping has advantages for both consumers and retailers. From a consumer's point of view they found e-shopping allows a lower price, different alternatives of products/services, and customized products. Additionally they established retailers benefited from online shopping as it allowed them to reach a maximum number of customers, reduce communication costs and rapid transportation. However, e-shopping has also been criticized as online shopping may be considered non-trust worthy due to concerns of security of privacy (personal and financial information), lack of examination of the products, lack of human interaction and a concern the quality of the products will not reach customer expectation. From a retailer perspective the disadvantages of online shopping are providing high quality and creating special services can be very costly for the firm and may not be a good incentive to make consumers purchase (Kim and Forsythe (2009) and Lee et al. (2006).
Whether it is a traditional market or online market, Hollensen (2004) pointed out that the retailer should understand the online consumer purchasing behaviour and how individuals make decision and buying choices. Therefore, Kotler and Armstrong (2007) stated that the marketers have developed different theories that can explain why consumers interpret information provided by e-retailer in a certain way, and thereby understand certain behaviours. Several authors have set out different definitions of consumer behaviour. According to Dr. Perner “consumer behaviour is study of individuals, groups, or organizations and the processes they use select, secure, use, and dispose of products, services, experiences, or ideas to satisfy needs and the impacts that these processes have on the consumer and society”. Hollensen (2004) and Constantinides (2004) agreed that consumer online purchasing behaviour is a process of various factors and influences experienced by a consumer before finally purchasing products online.
Online consumer behaviour researchers have therefore examined the adoption of technology for e-purchasing in different aspects. There appears to be no constant model of online purchasing adoption and behaviour as it depends on the nature of adoption as influenced by characteristics or social issues; Theory of Diffusion of Innovation (DOI) Roger (1983). In order to investigate consumer online purchasing behaviour, Theory of Reasoned Action (TRA) and Theory of Planned Behaviour (TPB) are considered dominant theories to measure online purchase intention and attitude behaviour, with Decomposed Theory of Planned Behaviour (DTPB) (Taylor and Todd 1995) the extended TPB. On the other hand, one essential model for development technology usage perspective is the Theory of Technology Acceptance Model (TAM) Davis et al. (1989), which developed into the Online Shopping Acceptance Model (OSAM) (Zhou et al. (2007).
E-commerce researchers have measured different approaches for understanding online consumer behavior. Chen and Corkindale (2008) and Hernandez et al. (2009[a]) measured factors that influence consumer's online purchasing behavior from the perspective of innovation adoption and accepting technology. Moreover, other authors examined trait attributes, situational factors, web site quality, and individual factors and influences on attitude and intention of consumer purchasing online (Monsuwe et al. (2004); Liao and Shi (2009); and Vazquez and Xu (2009)). Chen and Crokindale (2008) agreed attitude and intention have a strong relationship with acceptance of technology and the decision of purchasing online. In addition, innovation characteristics were considered significant factors that influence of technology adoption and purchasing behavior (Rogers, 1983).
Therefore in order to understand online purchasing behavior it is important to measure different factors that may influence e-shoppers and determine online shopping based on insight from technology adoption innovation diffusion literature. This study will therefore present the Liu Model (2004) using it to identify factors that influence Kuwaiti consumer purchasing online. It will also measure the relationship between characteristics of internet retailers/consumers and characteristics of innovation, allowing the research to examine the impacts of these characteristics on consumer decision making and then purchasing behavior.
1.2 Online purchasing
1.2.1 History of Online Shopping
In the 1990s online shopping emerged as a technological breakthrough and novelty in the business arena. Strengthening year on year in 1994 the first of its kind, an online bank was opened and Pizza Hut offered pizza ordering on their web page. Netscape then presented Secure Sockets Layer (SSL) to secure transactions, an essential feature of e-shopping. In 1995 Bezos launched Amazon.com, one of the most successful online businesses worldwide, followed by ‘e-bay' an online auction site. By 1997 an estimated 41 million people were shopping online. With advances in technology in 1998, electronic postage stamps were introduced, whereby individuals could download and print stamps after paying a fee. In 1999, with the first online shop in the UK, The Virtual Mall was also launched, considered the first UK graphical internet shopping mall. The online shopping market developed rapidly from this point as the consumer gained in confidence and knowledge.
In 1991, Kuwait University connected all university campuses together with the internet using International Business Machine (IBM) then known as BITNET with the help of Ministry of Communication (MOC) university campuses together. This network was limited to e-mail and other minor services. The National Science Foundation (NSF) agreed to expand the internet services to Kuwait in 1992 (Hussain, 2003).
1.2.2 Kuwaiti Consumer Attitudes Towards Online Shopping
Online shopping is a relatively recent phenomenon that has gradually expanded worldwide reaching Kuwait. Compared to traditional stores, e-shopping is far from the target customer in efficacy and provides significant advantages in time saving and low costs. Although developing online shopping in Kuwait advances slowly, it is establishing a solid base as it incorporates a certain lifestyle, is a convenient option and its adventurous nature is attractive to Kuwaiti youths. With these factors increasingly dominant in daily routine, purchasing online has become a natural option in countries such as the USA and economic areas of the EU and the GCC ( Ma'arafy et al. 2007). Common products selling in Kuwait online are from the USA, the UAE and Asia. According to Forrester research (2008), "Global e-commerce spending in 2000 was 132 $ billion, and expected to spend more than 1 trillion by 2012".
In GCC capitals, the usage of online shopping behavior is different in the USA compared with and European and Asia Pacific cities. In Kuwait the online shopping concept is relatively in its early stages, however the adoption of online purchasing is expected to grow continually in coming years. With a high level of penetration in neighboring countries online such as Saudi Arabia and UAE, Kuwait will not be far from this diffusion of web shopping. Among the GCC, Kuwait lies 3rd with 10.7% in terms of e-commerce penetration, against 25.1% UAE and 14.3% in Saudi Arabia (Field, (2008)).
According to recent worldwide research, as shown in Table 1.1, Kuwait's internet user growth has jumped from 5.8% of the population in 2000 to over 34% in 2008 and five times more users in the same time period and with further growth expected.
Table1.1: Growth Internet Users in Kuwait
Source: world wide statistics.com
According to Al-Bahar (2009), Kuwait Consumer Adaptors online shopping distinguishes between local and international websites when purchasing online for many reasons. Kuwaiti consumer purchasing online and local websites are still in their infancy and under development. Thus, consumers are oriented to external websites they have established reputations, are trustworthy and provide an assurance of quality of their products. Express delivery firms such as Aramex and DHL compete to provide their services for delivery products in efficacy and effectiveness to encourage customers to e-purchase (Al-Abdullah, 2009).
However, according to Al-Awan, (2008) e-shopping in the Kuwait market is still in its development stage through lack of organization. In order to enlighten and educate consumers, huge effort needs to be made with responsibility on the retailer to reach their maximum number of potential customers in order to realize value. Recently online businesses have started to establish themselves as limited e-firms providing products and services for Kuwaiti customers.
1.3 Problem definition
- E-commerce penetration:
With the adoption of Kuwaiti consumer online purchasing low, the penetration of e-commerce in Kuwait remains relatively slow with a lack of studies relating to Kuwaiti e-shopping adoption.
- Consumer e-purchasing awareness:
Due to a lack of consumer awareness of online shopping it has not been used widely in Kuwait.
- E-retailer strategies:
As online selling is different to offline selling, it is necessary to fully understand consumer behavior in order to set up business strategies for the long term. In addition the rapid development of technology related to the internet enhances the shopping experience and encourages potential customers to purchase online. It is therefore critical for e-retailers to identify what factors influence the consumer when e-shopping.
1.4 Research objectives
The overall objective of this research is to gain a deeper understanding of online purchasing behavior in Kuwait and factors affecting their buying decision process. This study is therefore focusing on the following objectives:
- To investigate the key factors affecting online purchasing behavior of Kuwaiti consumers.
- To explore the impact of the decision making process on Kuwaiti consumers purchasing behavior.
- To determine the relationship between factors influencing purchasing behavior and the decision making process.
1.5 Research Questions
To fulfill the purpose of this research and reach the stated objectives related to consumer purchase online behavior the following research questions need to be addressed:
- What are the main factors influencing Kuwaiti customers online purchasing?
- How do these factors affect online purchasing behavior?
- What is the impact of the decision making process on consumer online purchasing behavior?
- What is the relationship between factors influencing behavior and the decision making process for e-shopping?
1.6 Research methodology
This study's approach is deductive, because it measures factors that affect online shopping to explain Kuwaiti consumer online behavior taken from previous studies in different countries. It is mainly explanatory, developing a deeper understanding of the online purchasing behavior of Kuwaiti consumers while investigating varied opinions related to local e-commerce, alongside which factors affect their purchasing behavior. To a certain extent it is exploratory because of a lack of previous research in the online purchase behavior in Kuwait and Gulf region. The study is also mildly descriptive due to previous research of online market phenomena conducted in different countries and extended to Kuwait.
Moreover, this research is quantitative in nature using primary data for the survey questionnaire as the main tool of data collection in order to discuss online Kuwaiti consumer purchase behavior. The questionnaire was randomly distributed either in person or through email. The total sample size 500 was distributed in Kuwaiti firms, ministries, universities and public places with 360 respondents. The data collected from the questionnaire is then used to identify relationships and connections between these variables to achieve the study's objectives.
In the course of this research a number of limitations were identified as follows:
- As the research examines consumer online shopping behavior without specifying the type of product exchanged whether tangible or intangible, it is limited in its scope.
- This study is limited to selection factors covering aspects of Kuwaiti consumer online purchase behavior disregarding other variables of satisfaction, trust, social aspects and situational factors.
- As with all research using survey data the sample may not be fully representative of the actual behavior in the population, as it is impossible to directly compare our data with data collected on the State of Kuwait level on online purchasing behavior due to time factors.
- Investigation focuses on online consumer behavior mainly from the customer's perspective rather than the retailer's perspective.
- This study evaluates only the online adoption purchasing behavior without evaluation of service quality offered by distinct websites.
- With a lack of previous research in this topic in Kuwait and the Gulf region, there is little, if any, comparative literature review or use as a framework.
1.8 Thesis structure
In the first chapter; an overview of the research area is given, introducing e-commerce in general, then in Kuwait. This is followed by a presentation of the country relevance, the problem definition, the research objectives and questions, the research methodology and the limitations of the study. Chapter Two provides a comprehensive review of relevant literature concerning the research to draw an understanding of dominant theories that explain online consumer behavior, followed by factors that influence consumer online purchase with an integrated consumer making decision process. Chapter Three covers the research design and methodology exploring the methodology of the strategy of collecting data and analysis of the survey questionnaire to achieve the objectives. In Chapter Four, data analysis presents the empirical data collected with analysis and a survey discussion of the results. Finally in Chapter Five conclusions drawn from the overall study are summarized with recommendations made for future research in the subject area.
Chapter Two: Literature Review
In this chapter an overview and examination of theories of adoption and online technology acceptance behavior from a global perspective is presented, with a comprehensive review of relevant studies conducted on consumer behavior purchasing online with the decision making process.
Interactivity is considered a primary principle for the World Wide Web (WWW) with Lee et al. (2006) arguing that “interactivity is the extent to which users can participate in modifying the form and content of a mediated environment in real time”. The WWW allows unprecedented access to information and markets which has impacted societies globally with people able to search for information and/or purchase product/service online. Factors influencing consumer online purchasing behavior have been explored between 2004/09. Ha and Stoel (2004), Lee et al. (2006) and Hernandez et al. (2009) [b] analyzed the online behavior from the perspective of innovation adoption and accepting technology by identifying the consumer acceptance of innovativeness and frequency of shopping online. Lin and Wang (2008) focused on the decision making process arguing that consumers depend on their experience with repeat shopping. Broekhuizen and Huizingh (2009) agreed adding experience will lead to a strong relationship between different variables (such as saving time/effort, enjoyment and price attractiveness) and intention to purchase. The research of Monsuwe et al. (2004) and Liao and Shi (2009) explored situational factors, trait attributes, individual factors and website quality and impact on attitude and intention of consumer purchasing online.
This review will therefore cover wide-ranging theories considering the features and benefits of numerous models proposed by such authors studying online consumer behavior.
2.2 Technology readiness and Self-Services Technologies
While customer innovation adoption behavior and diffusion of innovations have been investigated for decades, recent interest has turned toward Self-Service Technologies (SST's). SST's involves new service access provided via new channels to meet customer demand in an effective and efficient way. Many technological innovations face resistance from customers, due to a lack of experience and uncertainty. Therefore research involves varied measurements such as: innovation characteristics, service quality, individual differences, ease of use and usefulness. Liljander et al. (2006) agreed personal traits suggest influence on customer adoption of SSTs. A study by Parasurman (2000), presented the attitudinal measurement “Technology Readiness (TR), peoples propensity to embrace and use new technologies for accomplishing goals in home life and at work” stating TR is considered a factor influencing SST's. The same author explained an individual's positive or negative feeling toward technology is dominant identifying TR consists of multi-measurements of: Insecurity, Discomfort, Innovativeness and Optimism. The latter, Optimism refers to the positive view of technology and beliefs of control that enable users to increase convenience, efficiency and flexibility, while, Innovativeness is people's tendency to open up to technology. Discomfort is an individual's perceived lack of control of technology and has a strong negative influence on SST's. Insecurity refers to lack of trust in technology and its ability to work effectively. Notably, optimism and innovativeness are considered highly TR individual contributors, with discomfort and flexibility considered to have high level inhibitor attributes decreasing TR. Liljander et al. (2006) proved in their research a positive effect of TR on customer's attitude towards using SST's and their website evaluation, finding technology linked with convenience, freedom and control as vital when building positive attitudes towards using SST's.
2.3 Original theories of consumer online behavior
Having reviewed numerous forms of literature no singular constant model has been identified for innovation diffusion and adoption. Innovation technology depends on the nature of adoption influenced by social theory or characteristics of innovation such as the Technology Acceptance Model (TAM) devised by Davis et al. (1989).Therefore diffusion theory and other factors have been widely used to guide consumer behavior research.
Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) are dominant theories examining consumers online purchase intention and behavior. TAM is considered an initial model for technology usage development, as it is customized to understand the adoption of computer-based technology in the workplace and is used in many studies. Conversely other researchers criticized TAM, because it explores simply the technology side. TRA has evolved from TAM, determining individual attitude toward and behavioral intention to use this new technology. TPB is considered another update from TRA. Theory of Planned Behavior identifies the behavioral intention of purchase online influence with its attitude to technology. Rogers (1983) created a Diffusion of Innovation Theory (DOI) that illustrates adoption of innovation dominant over time in social systems. This theory depends on critical elements, the time of adoption and characteristics of innovation.
2.3.1 Technology acceptance model
By using Theory of Reasoned Action as a theoretical base Davis et al. (1989) created a Technology Acceptance Model. TAM is identified a viable paradigm for examining consumer adoption for the new technology and information technology. The genuine TAM determined the actual use of technology, attitude toward using this technology connected with beliefs to define behavioral intention to use new technology as explained by Liu (2004) and illustrated in 2.1. TAM focused on beliefs about the usefulness and ease of use to be a main role in technology adoption behavior. Perceived Usefulness (PU) refers to the degree of potential individual perception that use of new technology will enhance improving performance Davis et al. (1989). Perceived Ease of Use (PEOU) is identified as an individual perception of using technology not requiring extra effort. Perceived Enjoyment was added later by Davis et al. (1992) and considered “essential motivation in adoption of new technology, the extent to which the activity of using computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated”. In TAM, behavioral intention to new technology usage was determined by a person's attitude toward using this technology. In addition TAM evolved with an updated version proposed in 2000 by Venkatesh and Davis called TAM2. This new model was influenced by subjective norms, image and output quality.
Having examined PU, PEOU and enjoyment in different shopping experiences, Lee et al. (2006) and Bridges and Florsheim (2008) found that seeking hedonic benefit depends on perceived enjoyment through online experience. Hedonic elements may encourage internet use, but not necessarily online buying. Furthermore, an individual consumer may be oriented to seek experiential value through enjoyable browsing or shopping online or for their own fun experience. Seeking utilitarian benefits also relies on perceived ease of use and satisfactory outcomes, in addition to influencing the purchase directly. Utilitarian orientation defined by Bellenger and Korgaonker (1980), Babin et al. (1994) and To et al. (2007) observes orientation or motivation seeking instrumental value to minimize time and effort shopping and cost saving or seeking convenience. Acquired benefit depends on whether the mission of shopping is completed or not. The e-retailer's focus providing utilitarian benefits more than hedonic benefits will increase or be completed efficiently during the process of online buying and future intention.
2.3.2 online shopping acceptance model
Zhou et al. (2007) proposed an extension model of TAM called “Online Shopping Acceptance Model” (OSAM). This model considers a general view of online purchasing acceptance from the consumer's perspective. These authors also pointed out that in spite of TAM Davis et al. (1989) being broadly used to examine online purchasing environment, it does not analyze specific online shopping characteristics. Therefore OSAM integrated consumer factors in traditional markets and theories may be added to TAM factors to re-examine the issue in the context of online shopping as showed in 2.2. Moreover, OSAM have been developed to predict and explore consumer acceptance e-purchasing by incorporating the beliefs, intention, and attitude behavior relationship into the perspective of perceived usefulness which was replaced by perceived outcomes to cover potential benefits and e-shopping risks. Shopping orientation and motivation have been added from traditional market factors considered antecedents of online purchasing intention and online experience as factors that construct during navigation of e-shopping sites. Also, satisfaction as mediators between behavior and intention has been added. OSAM considers a strong predictor of continue intention to purchasing more than perceived usefulness. Furthermore, this model includes consumer demographics and normative beliefs with their influence on e-purchasing intention. Exploring the development of TAM by introducing OSAM will enhance our understanding of different factors that affect consumer behavior intention.
2.3.3 Theory of reasoned action
Fishbein and Ajzen (1975) formulated a “Theory of Reasoned Action” (TRA), which illustrates behaviors expressed by individual intention to perform a behavior from psychological social factors and aims to examine measurements of that behavior. Based on Marshall et al. (2009) and Lee and Park (2009), they pointed out correlations between beliefs, subjective norms and attitude affects on formation of behavioral individual intention. This intention is influenced by subjective norms referring to the individual's perception with outside influences to perform (or not) a specific behavior to purchase as illustrated in 2.3. While attitude refers to an individual attitude behavior, negative or positive, toward adoption of innovation and brand overall which creates their beliefs about the consequences of adopting and the brand's attributes (Jobber, 2004). Beliefs are defined by the person's subjective probability that performing a particular behavior will produce specific results. Four types of belief attitude towards to e-shopping were identified by Vijayasarathy (2002); shopping experience, product perception, customer services and customer risk. This model therefore suggests that external stimuli influence attitudes by modifying the structure of the person's beliefs (Ajzan and Fishbein, 1980 and Ajzen, 1991).
Further, TRA provides a strong theoretical basis for studying motivation related decision-making. Using this theory is expected to enhance our understanding toward attitudes and behavioral intention of online shoppers.
2.3.4 Theory of planned behavior
The Theory of Planned Behavior (TPB) can be appraised as an extension of TRA according to Ajzen (1985) used to predict buying behavior based on Bagozzi and Kimmel (1995) and De Cannière et al. (2009). A central element of this theory is the individual intention to perform a given behavior as shown in 2.4. Ajzen (1991) identified intention as ‘how individuals are willing to try and how much effort they are planning to exert, in order to perform the behavior'. The same author and Chen and Corkindale (2008) state this theory includes an additional element which is an individual perceived behavioral control (PBC). Compeau and Higgins (1995) cited by Dennis et al. (2009) defined it as a judgment of one's ability to use a computer. PBC is compatible with Bandura's (1977, 1982) concept of “perceived self-efficacy which is concerned with judgments of how well an individual can execute courses of action required to deal with prospective situations”. In PBC attitude and subjective norms factors can predict intention and behavior.
According to TPB, PBC together with intention can be used directly to predict behavioral achievement. This model proposes the intention impact and mediates among these factors: 1) intentions are the immediate antecedent of behavior, 2) fully mediate on impact of attitude towards behavior and 3) intentions partially mediate the impact of perceived behavioral control (Ajzen, 1985, 1991; Fishbein and Ajzen, 1975) as illustrated in 2.4. Furthermore, Ajzen stated that the relative importance of predictors in the TPB would be different among behaviors and situations. On the other hand, TPB components can be used according to De Cannière et al. (2009) to form the experience after purchasing.
2.3.5 Decomposed Theory of planned behavior
In 1995, Taylor and Todd demonstrated that better comprehension of the relationship between beliefs and antecedent of intention need to be combined as attitudinal beliefs as DTPB as shown in 2.5. They argued that DTPB is a strong model, more advanced and purer than the TRA and the TPB model. It was identified that, due to diffusion innovation theory, attitudinal beliefs contained three characteristics of an innovation that affect the adoption: relative advantage, complexity and compatibility (Rogers, 1983). This model contains the main elements of normative beliefs and Perceived Behavioral Control (PBC). PBC reflects behaviors and consists of two main elements: facilitating conditions (Traindis, 1980) and self efficacy (Ajzen, 1991) regards the comfort usage of innovations. As a result we can see DTPB integrated in most theories and models related to consumer behavior.
2.3.6 Triandis Model
Similar to TAM, TRA, and TPB, the Triandis Model (TM) was presented by Triandis in 1980. This author suggested a theoretical network relating to attitude and behavior to many variables such as and will biological and cultural factors. This model proposed the probability of performing behavior is determined by a number of measurements: habits, facilitating conditions, and intention as shown in 2.6. Furthermore, the behavioral intention is function of social factors (including norms, self concept and roles), affect and perceived consequences of acting the behavior, with facilitating conditions determining all necessary resources and sustaining performed a behavior, such as time, money and expertise. Chang et al. (2005) pointed out that due to the construct of the Triandis Model it created a lack of TAM, which believed that usage is preference not prevent an individual from using IS. As TM has been adopted widely in consumer behavior in recent years, it has been applied in technology adoption studies. Cheung et al. (2000) viewed implementing TM to explore the use of internet/WWW and understand user intention using online means for shopping and work.
2.3.7 Criticism of TAM, TRA and TPB theories
In 2009, Kim and Forsythe explained most of these theories tested master models only, which may increase the errors of generalizing results if implemented on different technology. Cheung et al. (2005) and Dennis et al. (2009) claimed TAM, TRA and TPB ignore other factors including: consumer characteristic, environmental influence, medium characteristics, situational factors and consumer traits. A theoretical model that includes TAM and other factors was proposed by Monsuwe et al. (2004) to consider the attitude of online shopping and the behavioral intention trying to implement TAM in the best way. Leder et al. (1999) and Chen and Crokindale (2008) determined that PEOU and PU in the TAM model was not always a significant key role. In addition Lee et al. (2006) explained the Theory of Acceptance Model presented PU and PEOU leading directly to the intention to use the technology without determining the attitude, either negative or positive, of using this system by the individual. In 2009, Taylor and Strutton argued that TAM no longer represents an appropriate study for online purchasing behavior, but PEOU and PU remain main key predictors of behavioral intention. Usage of this theory has decreased as it was not statistically significant in predicting size of the technology acceptance, despite in a relatively short time being widely accepted on the internet as a purchasing and a marketing channel.
2.4 Theory of diffusion innovation
2.4.1 Definition of diffusion innovation
Adoption and diffusion have been widely used in consumer behavior research (Liu, 2004). The innovation diffusion theory provides a set of innovation characteristics that may affect adoption decisions (Rogers, 1995). The difference between adopter and innovator must be determined in order to investigate and understand the Diffusion of Innovation (DOI). Innovators are individuals willing to use the technology without influences. Engell and Blackwell (1982) identified adoption as the “individual process for using technology mentally and behaviorally sequences that lead to acceptance and continue to use”. Early adopters are considered innovators by McDonald and Alpert (2007). These authors pointed out that early buyers for new products or services act under influence of word of mouth and other people's experience or include some adopting independently. Innovativeness arose, was defined and presented in the work of Midgley and Doweling (1978) as “is the degree to which an individual makes innovation decisions independently of the communicated experience of others”. Chen and Crokindale (2008) cited Williams et al. (1994) stating “DOI paradigm brings in a constant demand while innovation direction of how the recently new technology been introduced, communicated, evaluated, adopted or rejected and re-evaluated by consumer”.
Innovation classification investigated by Robertson (1967) was cited by Hand et al. (2009) and Hansen (2005) divided into three types based on consumer behavior toward technology: continues, dynamically continues and discontinues innovation. Hansen regarded discontinues innovation a crucial type, because it not only relates adoption of a new product/service, but also customizes the buyer behavior pattern more over online buying representing this type of innovation. Dynamically continues innovation considers product of new technology will not fundamentally change consumer behavior. Finally continues innovation views minor technology will not change consumer behavior. Lin (2008) agreed that innovation diffusion theory consists of perceived innovation characteristics influencing consumer usage of an innovation. This study also confirmed that direct and positive influence of innovativeness towards consumers' adoption behavior online shopping on future internet shopping intention and retailer should target more innovative users.
2.4.2 Innovation characteristics
According to Rogers (1983) innovation characteristics are considered important measures affecting adoption and consisted of 5 dimensions: 1) Relative Advantage, 2) Compatibility, 3) Complexity, 4) Divisibility or Triability and 5) Communicability.
As well as innovation diffusion integrating with adoption research and connecting with adoption decision and behaviors to a number of innovation characteristics, each type of innovation should be visualized as a foundation based on specific attributes in the adoption context. The innovation characteristics considered a potential influence of consumer adoption have been identified as: ease of use, compatibility, relative advantage, perceived risk and enjoyment.
184.108.40.206 Relevant advantage (RA): Rogers and Shoemaker (1971) and Rogers (2003) view RA as “the degree to which an innovation of technology is perceived as being better than the idea in current methods” compared to existing products affecting the speed of adoption. It also drives more value for consumer online shopping and consists of different elements that make online shopping unique leading eventually to consumers shopping online such as: shopping convenience (saving time), production information (different information for one product in one channel), merchandise (ease customization products, variety of products) and price reduction (Hansen, 2005).
220.127.116.11 Perceived Ease of Use (PEOU): Monsuwe et al. (2004) measure ease of use in the context of the internet as a shopping medium and identify certain elements that affect the consumer such as: experience, control (availability of knowledge and resource), computer playfulness (computer interaction) and computer anxiety. Zeithaml et al. (2006) strengthened these findings adding an additional dimension of the website playing a role in ease of use i.e.: search function, download speed and navigation. Perceived ease of use from the retailer enables consumers to feel comfortable and confident to participate in the shopping process.
18.104.22.168 Compatibility: Tronatzky and Klien (1982) cited by Kleijnen et al. (2009) concluded that compatibility refers in the online shopping context to the degree an individual who receives shopping through the internet is consistent with their current value, habit and past experience, needs and lifestyle. Findings by Jobber (2004) concurred and it is one of few factors of Rogers' theory related to adoption in the context of online shopping. Moreover, when conducting shopping over the internet is compatible with existing processes and systems, then customers employ less effort to deal with incompatibility. Slyke et al. (2007) argued perceived compatibility impacts on beliefs, as beliefs influence attitudes which subsequently impact on intention, therefore compatibility beliefs impact on attitude as well as intention.
22.214.171.124 Perceived Risk (PR): Taylor and Strutton (2009) referring to Bradach and Eccles (1989) identify PR as ‘a consumer's belief about the likelihood of gains and losses being associated with a given consumption decision'. Many consumer scholars including Hansen (2005) identified several types of risks such as social, privacy, performance and product. In the context of shopping online, Chang et al. (2005) analyzed perceived risks of online shopping into two approaches: general risk and specific risk. The general risks covered general risk perception of buying online goods; while specific risks were concerned with system security, privacy infringement, fraudulent merchant behavior product risk and credit card fault. The most common concern of consumer shopping online is perceived risk due to uncertainty of product value (product risk) and/or perceived financial risks (private and credit card information). Such research found that the key reason for consumers not shopping online is fear from invasion of personal information and theft of financial data. Subsequently, perceived financial risks have a significant influence on perception consumer adoption of purchasing online. Monsuwe et al. (2004) commented that “past experience decrease consumer perceived risk level associated with online shopping”. This agreed with the findings of Lee and Turban (2001) that connected perceived risk with trust, because online shopping converts the physical salesperson to buttons and privacy with security has an impact on trust, thus resulting in powerlessness for the consumer through online shopping. Ranganathan and Jha (2007) noted that combined security, privacy and offline delivery and return will influence and enhance consumer trust and increase purchase intention. Finally, Taylor and Strutton (2009) support the negative correlation between privacy concern and behavioral intention.
126.96.36.199 Perceived Enjoyment (PE): The concept of perceived enjoyment to stimulate consumer behavior and acceptance technology has been supported by numerous authors. By comparing and analyzing works of Liu (2004) and Monsuwe et al. (2004), PE has been divided into two approaches. In the “internet adoption” situation, perceived enjoyment is integrated with internet activities such as emails, browsing and downloading. Mounsuwe et al. (2004) forecast the dimension that constructs PE and influences the “consumer behavior” context as escapism, pleasure and arousal. In the same way, positive effects of PE increase the searching information behavior and experimental experience that eventually impacts shopping online behavior for an individual.
In conclusion innovation characteristics have a significant impact of consumer adoption to purchasing online, with RA and compatibility having the greatest influence on online shopping technology innovation and consumer adoption decisions. PEOU, PE and perceived risk are significant factors that influence consumer intention and attitude of purchasing online behavior with accepting the innovation technology. As stated by Liu (2004) the model of consumer online purchasing is integrated with the decision making process in addition to other variables: consumer, e-retailer variables and product moderating as illustrated in 2.7.
2.4.3 Criticism diffusion innovation theory
The Diffusion Innovation Theory (DOI) criticized by Vishwanath and Goldhaber (2003) and Chen and Crokindale (2008), provides inconsistent results and delineates insufficient relationship between the characteristics of innovation and adoption. Moreover, the same authors judged this theory as lacking in the marketplace, because of DOI inefficacious regard to performance prediction and control function. In 2009, Taylor and Strutton approved predicting online consumer behavior by using behavioral models such as TAM and diffusion of innovation theory may no longer adequately capture internet consumer behavior, due to online purchasing behavior and internet usage both reaching the post-adoption stage and peak level adoption.
2.4.4 Innovation decision process
Ha and Stoel (2004) used IDP as a foundation for their studies to evaluate the adoption of innovation for search information or purchase products online. Rogers, (1995) defined the decision process as an individual process to adopt innovation. The same author implemented this process for adopting apparel purchase online connected with characteristics of innovation as illustrated in 2.8. The aim of this process is to collect and seek information in order to obtain relevant information the consumer needs to evaluate attitude toward innovation to reduce their uncertainty of adoption innovation leading to make the eventual decision (purchase online).
2.5 Attitude, intention toward and actual buying behavior
Behavior purchasing online measurements have been conducted by numerous authors. Notable perspectives presented by Limayem et al. 2000 and Cao and Mokhtarian (2005), consider e-shopping intention in some studies dependent variables while other research considers actual online shopping, whereas in other studies, attitude to online purchasing are investigated as dependent variables as shown in Table 2.1. Attitude toward online shopping was identified as a specific action representing individual overall negative or positive beliefs and evaluation of the behavior. Intention to shop online was identified by Pavlou and Chai (2002) as customer intention to exchange, share information online and employ e-transaction. However both acknowledged that the stronger the positive intention behavior, the more likely individuals perform the behavior; highlighting actual online purchasing determinants consist of adoption of online buying, the amount spent online and frequency of using shopping.
Table 2.1: Determinants of Independent Variables of Online Shopping
Measurements of online purchasing intention
Measurements of actual purchasing behavior
Measurements of attitude toward online purchasing
Perceived behavioral control
Ease of use
Perceived behavioral control
Ease of Use
Source: Limayem et al. (2000)
Comparing works between Ha and Stoel (2004) and Kim and Park (2005), purchase behavior was determined by satisfying consumer uncertainty (perceived financial risk) and increasing convenience (price and products variety) factors. George (2004) measured consumer behavior of online purchasing by trust in the internet, attitude toward it and actual purchasing.
Recent contradictory research of Dennis et al. (2009) referred to Fishbein and Ajzen (1975) and Ajzen and Fishbein (1980) stating positive attitude drives online consumer behavioral intention and enhances other perception leading to actual purchase online, as shown in 2.9, which illustrates the difference between theoretical models and empirical studies. Some consumer behavior research explains that attitude and intention to purchase are dramatically changing due to the impact of adoption of the internet on consumer decisions.
2.6 the buyer decision process
2.6.1 General Definition of Consumer Decision Process
Researchers continue to measure consumer behavior purchase online with the assistance of the decision process. Blackwell, Miniard and Engel (2001) defined a series of interlinked multiple stages a consumer may pass through such as information collection, evaluation of alternatives, the purchase itself and post-purchase evaluation. 2.10 shows these stages, which form the consumer decision-making process. Many frameworks propose a consumer decision process in spite of several authors not seeing any fundamental difference between the traditional and the online buying process. Constantinides (2004) argues the process has one additional step which is building trust or confidence, whereas the repetitive loops increase the process of adopting the information which leads to updating the model (Zellwegger, 1997; Butler and Peppard, 1998).
2.6.2 Online consumer decision process
The decision making process can be conducted online and offline. The buying process starts with problem recognition. Jobber (2004) explained the degree to which the consumer intends to resolve the problem depends on two issues: the magnitude of the discrepancy between the desired and present situation and the relative importance of the problem. At this stage, the marketers need to determine the factors and situations that usually trigger consumer need recognition. In the online context, Lin and Wang, (2008) forecast the first step to making a decision is to recognize brand awareness and brand association, factors that help the consumer to identify certain brand products.
The second stage in the consumer decision process starts when problem recognition is sufficiently identified. Information search includes identification of alternative problem solutions. This includes internal search (relevant memory information and reference to personal experience) and external search a personal resource, commercial sources (advertisement) and personal experience. The objective of information search is building up the awareness set. Kulviwat et al. (2004) who referred to Wright (1975) explored motivation factors on searches such as: perceived benefit and cost, ability to search, buying strategies (website satisfaction), situational contingencies (time and pressure) and consumer characteristics (browsing enjoyment). Savolianen and Kari (2006) measured criteria for evaluation searching online either acceptance or rejection of the website or hyperlink as illustrated in 2.11.
The first step in evaluation, consumer use information reduces the awareness set and arrives at final brand choice and series consideration. They have been found to use one or more evaluation procedures that depend on them and their buying decision. Online evaluation is characterized by an evaluation website and services offered as Savolianen and Kari (2006) appraised, and then evaluating the information regarding the product attributes among other competitors, subsequently formulating a convenient feel toward decision purchasing. Further investigation for online consumer evaluation was conducted by Lin and Wang (2008) regarding product consequence, personal value and website trust.
Consumer ranks brands and forms purchase intentions as Kotler et al. (2006) determined, in the evaluation stage. Consumer's purchase decision influenced by two factors shown in 2.12 can come between purchase intention and purchase decision. Attitude from others and unexpected situation factors can influence purchase intention. Moreover, consumer decision is influenced heavily by perceived risks and the amount varies with the amount of purchase uncertainty, money at stake and consumer self-confidence.
After purchasing the products/services, the consumer will be satisfied or dissatisfied and will engage in post purchase behavior. This stage is crucial for the retailer because of the relationship between consumer's expectations and the product's perceived performance. The quality of products/ services is generally a key determinant as Jobber (2004) pointed out, and the role of retailer acting as problem solver can help customer satisfaction, then reduce cognitive dissonance (negative experience from purchase products or services). Also, the purchaser provides retailer's feedback website usage or /and quality of the product based on their experience which will develop in features of the relationship between the buyer and the website. Liu (2004) emphasized at this stage, the consumer experience involves several aspects: ‘decision confirmation, experience evaluation and future response intention'. Chaffey (2007) highlights the relative importance of instruments of the internet in supporting the purchaser through each of the stages of the buying process as shown in Table 2.2. We can see that the process of decision making is supported and connected by different aspects, and identify tools that can enhance achievement in each of the decision steps.
Table 2.2: Tactics to Support Customers through Different Stages of the Buying Process
1- Problem Recognition
Offline advertising and media mentions are important in generating awareness of digital channels as a means by which customers can find, evaluate and purchase products.
2- Information (or supplier) search
Identify different searching behaviors. Surfing website not searching is more important to groups with a high proportion of non-directed information-seekers.
Site content should communicate the features and benefits of the brand in what may be fleeting visits to the site or an in-depth analysis. Also, increase customer buying behaviors according to internet experience.
4- Decision making
Influencing decision making can be used to provide customers incentives to capture an e-mail or postal address and deliver detailed brochure by post, or provide a callback facility so the customer can be contacted to help decision.
Once the decision has been made to purchase, make the purchasing straightforward through the user -centered design.
Use e-mail and the web site to provide customer service and support. E-mail notifications of an order and dispatch can help reduce post purchase dissonance.
Source: Chaffey (2007) and DoubleClick (2003)
2.7 E-Service and adoption
2.7.1 General Definition of Adoption
The diffusion-adoption process determined by Chaffey (2007) and Kotler et al. (2006) referred to Rogers (1983) classifying those trailing new products as innovators, early adopters, early majority, late majority, or laggards. The adoption process is identified by them as ‘the mental process through which an individual passes from first learning about an innovation to final adoption', while adoption is defined as an individual decision to become a regular user of the product. Table 2.3 illustrates the description of each type of process. From definition of type of adopters in Table 2.3, e-retailers must target the innovators and early adopters, playing a key role in the success of innovation and adoption of online purchasing by spreading the word to other consumers who might be interested in the benefits of shopping online.
Table 2.3: Definition of Adoption Process
Type of adopters
Individual who are often adventurers, first who adopt a new idea or innovation and willing to take a chance at some risk.
Individual guided by opinion leaders and adopt new ideas early but carefully.
Individual usually deliberate and cautious in their approach to adopt ideas before the average person.
Individual characterized more cautious and skeptical, adopt innovation only after most people have tried it.
Are tradition-bound people, they are suspicious of changes and adopt the innovation only when it has become something of a tradition itself.
Source: Kotler et al. (2006) and Jobber (2004)
Adoption purchasing online is always integrated with completed steps of the decision making process. The adoption of the internet is classified into two types, full adoption which is a complete decision process and partial adoption that can be identified as the first of three steps of the decision making process (problem recognition, information search and alternatives evaluation).
Table 2.4: The Categories of Adopters of the Internet as a Shopping Medium
Current Adoption Behavior
Future Usage Intention
Will Continue to Visit
A consumer who uses the internet to search for product/service information and evaluate alternatives without intending to purchase through the internet
A consumer who has previously purchased through the internet but has no intention to continue to purchase products/service online
Will Continue to Purchase
A consumer who visits the internet for shopping purposes who has not purchased through the internet yet, but who intends to continue to visits the internet and will purchase products/ services online in the future
A consumer who has previously purchased through the internet and intends to continue to use the internet to purchase products/services online
Source: Liu (2004)
Liu (2004) identified in Table 2.4 four types of adopters in the context of shopping online with respect of the decision making process: steadfast, latent purchaser, dropout purchaser and steadfast purchaser.
By exploring types of adopters with respect of the decision making process in Table 2.3 non-adopters can be identified as shopper to visit website for comparing alternatives or getting information. While online purchaser adopters implement one or more steps of the decision making process.
2.7.2 E-Service Adoption
While internet delivered electronic-service is more and more available for the consumer, there are not sufficient studies to evaluate customer potential adoption (Dehbashi, 2007). It is crucial to differentiate between studying a basic buying transaction and adopting e-services. The e-service adoption decision is basically different from most standard e-commerce transactions. Therefore, the adoption decision is more difficult due to the establishment of the long term relationship between the consumer and e-retailer. According to Featherman and Pavalou (2003) when consumers buy via e-service they receive access to operations provided other than product buyers, where customers receive tangible goods. Furthermore, e-service is influenced by different factors. Despite e-commerce adoption being different to e-service adoption, there are some similar factors influenced such as risks and trust factors.
2.8 Consumer Variables
TAM, TRA and Diffusion Innovation Theory have been criticized because of focusing investigation on consumer behavior from a technological aspect only. Consumer purchases are influenced strongly by culture, social, personal and psychological characteristics. For the most part, marketers cannot control such factors, but they must take them into account. It is considered important to illustrate these characteristics for the case of a hypothetical customer (Kotler et al., 2006). Dennis et al. (2009) demonstrated factors influence consumer behavior in the social aspect including consumer traits. However, innovation characteristics, individual characteristics, demographics, social innovation and psychological variables also influence e-consumers making their individual choices.
2.8.1 Shopping Motivation
An understanding of motivation lies in the relationship between needs, drives and goals. Kotler et al. (2006) outline motive as a need that is sufficiently pressing to direct the person to seek satisfaction. Psychologists have developed theories of human motivation. Abraham Maslow sought to explain why people are driven by particular needs at particular times. In the context of online shopping, motivation according to Vazquez and Xu (2009) pointed to utilitarian motives and hedonic motives having a strong influence on online information search and online purchase intention. This contrasted with other author's buyer behaviors theory and argued that attitude toward online shopping influences on motivation of online shopping motivation.
In 1978, Midgley and Doweling presented innovativeness as the degree to which an individual receives new ideas and makes decisions independently without other experience, with innovativeness a good predictor for new product innovation. Innovativeness and demographics have the advantage of identifying innovators in early stages of adoption steps (Rogers, 1995). McDonald and Alpert (2007) argued that innovative is no longer related to behavior; instead it is defined in terms of time of adoption called “actualized innovativeness”. This is based on Midgley and Doweling (1978, 1993) whereby rather than types of information taken into account influenced by the actions and communications of others, the decision to adopt or not is identified as “ innate innovativeness”.
2.8.3 Internet Self-Efficacy
People's judgment of their capabilities to organize and execute courses of action required to attain designated types of performances' is recognized by (Bandura 1986 cited by Hernandez et al. 2009[b]). Ranganathan and Jha (2007) observed the consumer online orientation for shopping consists of self efficacy. In addition, Hernandez et al. (2009)[b] investigate found an individual is human aspects will influence their accumulate experience of adopting and acceptance of the IT. Self efficacy is therefore considered vital, because an individual should feel comfortable and confident in their capability to use online services for purchasing or browsing from the beginning. Moreover, self efficacy is divided into “computer self-efficacy” and “internet self efficacy”, whereby computer self-efficacy is experience, internet skills and knowledge of computer regarding the individual and technology.
2.9 Retailer variables
E-consumer research explores different aspects studying consumer purchase behavior including the decision process adopting internet, because consumers go through a process of choosing products and then acquiring them from the retailer by selecting alternative sales channels. Subsequently, consumer perceived characteristics of retailer influence online consumer purchasing behavior.
2.9.1 Different Models of Online Service Qualities
Varied research has tried to provide models to encourage e-retailers understanding of different impacts of the website quality on customer buying decisions. Zeithaml et al. (2000) developed a foundation of measuring online service quality known as “E-SERVQUAL”, including the following dimensions: ease of navigation, efficiency, access, reliability, responsiveness, privacy/security, assurance/trust, site aesthetics, and price knowledge. Wolfinbarger and Gilly (2003) presented critical determinants of consumer perception of e-purchasing experience “eTailQ”. Their model was also extended from “E-SERVQUAL” and suggested that the judgment of quality online site strongly related to website design and fulfillment/reliability factors, but privacy and security obtained from website design as shown in 2.14. Another model was presented by Santos (2003) consisting of two main determinants (incubative and active dimensions as illustrated in 2.13. Incubative dimension identifies the proper design of the website implementing new technology for easy access with comprehension interaction with web, while active dimension identifies as “the good, support, fast speed, and attentive maintenance that a website can provide to customers”.
The “e-TransQual” model by Bauer et al. (2006) was developed due to a lack of “eTailQ” and “Santos model” not covering all aspects of cons
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