Engaging of companies in e-commerce to expand their retail channels

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1.1 INTRODUCTION

As the world of the internet rapidly grows, and online technologies improve drastically, more companies are engaging in e-commerce to expand their retail channels, and attain better reach to potential customers. Based on the previous literature, providing multi-channel shopping capability will allow retailers to target a wider customer base and improve their competitive advantage in the market, thus increasing their potential for success (Dickson, 2000; Khakimdjanova & Park, 2005; and Nitse, Parker, Krumwiede & Ottaway, 2004). However, despite the obvious advantages of e-commerce, and the fact that the Internet affords great opportunities to businesses and is the fastest growing retail channel, it still is not the most common method for shopping used by consumers (Brashear, Kashyap, Musante, & Donthu, 2009).

This chapter explores the theoretical framework of this study, explaining the purpose of the research and the model used by the researcher to analyze the subject understudy. The chapter includes a preliminary explanation of the deductive model proposed by the researcher, the variables used, and their relationships, in addition to the formulation of the major and minor questions into hypotheses, the data collection instrument employed and the methodology of data analysis.

From the researcher's literature review it is apparent that there are factors that clearly affect consumer online shopping preference. The review of literature clearly classifies influential factors of retail channel preference into Functional and Hedonic perceived value - related factors (Babin & Attaway, 2000. The researcher aims to determine which of the factors suggested to have an effect on Functional value perceived by consumers, namely Ease of Use, Usefulness (Childers, Carr, Peck & Carson, 2001), Convenience, Informativeness (Bagdoniene & Zemblyte, 2009), Wider Selection, Cost Reduction (Delafrooz, Paim & Khatibi, 2009) and Perceived Control (Koufaris, 2002), are most influential on online shopping preference in Egypt. Similarly, the researcher will attempt to discover which of the factors suggested to have an effect on Hedonic value perceived by consumers, namely Adventure Shopping, Social Shopping, Gratification Shopping, Idea Shopping, Role Shopping and Value Shopping (Kim, 2006), are most influential on online shopping preference in Egypt.

The literature further states that factors such as product type (Girard, Korgaonkar, & Silverblatt, 2003), retailer reputation (Lee & Turban, 2001; and Wright, 1975) and payment method (Cases, 2002; and Forsythe & Shi, 2003) are most apparently associated with the different Intentions to shop online exhibited by consumers.

From the review it is also evident that in Egypt, the e-commerce business has a lot of potential but is still yet under achieved. This study will attempt to provide Egyptian retailers with data that will help them realize their full potential and serve a larger market.

Through attempts to portrait the multi channel shopping habits of the Egyptian consumers, the researcher aims to uncover more appropriate manners by which retailers can optimize their business performance and achieve most success.

1.2 PROBLEM DEFINITION

It is evident from the previous review of literature that the global widespread use of the internet as a means of commerce is apparent across many industries. However, Retailers are still unsure of the factors that motivate/ influence customers to switch from the traditional retail channel (Physical stores) to the online channel (Online stores).

This research attempts to explore and confirm from pervious researches, and identify the key factors that impact the choice of consumers for online shopping as the preferred channel of purchase in Egypt. The researcher will try to discover the moderating effect of different product classes (Search, Experience and Credence), trust in retailers (International & local retailers) and payment methods (Cash on Delivery & Credit Card Payment) on the relationship between Online Shopping Preference and Online Purchase Intention

As a conclusion, the researcher intends to develop a general portrait of the multi/cross shopping habits of the Egyptian consumers, with the aim of providing retailers with information that might enable them to capitalize on their opportunities and highlight the factors that might be key to their success in the e-business world.

1.3 RESEARCH OBJECTIVE

The purpose of this quantitative study will be to measure the influence of the different motivators of hedonic and utilitarian values of the online shopping experience as perceived by consumers. The study will also measure consumers' preference for the online shopping channel, as well as the effect of product class, trust in retailers, and payment methods on intention to purchase via the internet.

The researcher will attempt to demonstrate the relationship between the influential factors and Online Shopping Preference, and that between Online Shopping Preference and Online Purchase Intention, as moderated by Product Types, Retailer Trust and Payment Methods. As a conclusive outcome to the study, the researcher will attempt to portrait the multi-channel shopping classification of the Egyptian consumer. It is assumed that this understanding will allow retailers to capitalize on opportunities and to realize the factors that need to be focused on in order to succeed in the Egyptian market.

1.4 THEORETICAL FRAMEWORK

The model used in this research has been developed as an adapted combination of models previously tested in the literature by researchers Sahney, Shrivastava, & Bhimalingam (2008); Koufaris (2002); Childers, Carr, Peck, & Carson (2001); Kim (2006), Chen & Lee (2008) and Zhang (2006). The model attempts to identify the most influential factors of the hedonic and functional value of the shopping experience as perceived by consumers on consumers' choice of the internet as their preferred retail channel.

It is also utilized to suggest the relationship between consumer preference to online shopping and their intention to purchase form the internet. Furthermore, the model is aimed to uncover the effect of different product classes, perceived trust in retailers on consumers' intention to purchase from the internet.

1.4.1 Research Variables

This research examines the relationships among the following dependent, independent and moderating variables:

1.4.1.1 Dependent Variables

* DV 1: Online Shopping Preference (OSP)

In the very basic sense, Preference is defined as the choice of something over another (www.thefreedictionary.com). Online Shopping preference in this study refers to choice of consumers to the online shopping method as their preferred retail channel (versus shopping from traditional physical stores)

It is suggested that Online Shopping Preference (OSP) will be positively influenced by the factors perceived by consumers as online shopping functional values: Ease of use (EU), Convenience (CO), Informativeness(IN), Wider selection (WS), Cost Reduction (CR), Perceived Control (PC) and Usefulness (US); as well as factors considered as online shopping perceived hedonic values: Adventure (AD), Social (SO), Gratification (GR), Idea (ID), Role (RO) and Value (VA) shopping.

It is also suggested that the relationship between Online Shopping Preference (OSP) and Online Shopping Intention (OPI) will be moderated by Product Type (PT); Retailer Type (RT); and Payment Method (PM).

* DV2: Online Purchase Intention (OPI)

In its most primitive meaning, Purchase intention is as a plan to purchase a certain good or service in the future (www.budinessdictionary.com). It is of particular interest to marketers as they are continuously attempting to establish a purchase intention in the minds of consumers for the products their companies are offering.

In this study it is suggested that the relationship between Online Purchase Intention (OPI) and Online Shopping Preference (OSP) will be moderated by Product Type (PT); Retailer Type (RT); and

1.4.1.2 Independent Variables

Variables Related to Online shopping Utilitarian Value as perceived by consumers

· IV1: Ease Of Use/ Efficiency/ Usability (EU)

Ease of use is identified as the ease of online shopping and refers to time and effort savings through efficient search functions, fast download speed, good overall design and easy ordering procedures (Swaminathan, Lepkowska-White & Rao, 1999). It is believed to be perceived by consumers as a functional value of online Shopping. Childers, Carr, Peck & Carson's (2001) and Koufaris (2002) indicate that ease of use positively influence consumers' choice of the internet as their preferred retail channel. In this study, it is suggested that Ease of use (EU) will have a positive effect on Online Shopping Preference (OSP).

· IV2: Convenience (CO)

Convenience is defined as ease of shopping via home based 24/7 availability (Jang & Burns, 2004; Park, Lennon & Stoel, 2005; and Rogers, Negash & Suk, 2005). It is the second factor perceived by consumers as a functional value of online Shopping. Girard, Korgaonkar & Silverblatt (2003); and Rohm & Swaminathan (2004) established that convenience was one of the most prominent attractions to shopping online. In this study, it is suggested that Convenience (CO) will have a positive effect on Online Shopping Preference (OSP).

· IV3: Informative-ness (IN)

A third factor thought to be perceived as a functional value of online shopping is Informativeness, defined as the extent to which a channel is perceived to provide relevant and in-depth information for decision making (Chen & Dubinsky, 2003). . Girard, Korgaonkar & Silverblatt (2003) and Park, Lennon & Stoel (2005) indicate that a positive relationship exists between Informativeness and consumers' choice of the internet as their preferred retail channel.

In this study, it is suggested that Informativeness (IN) will have a positive effect on Online Shopping Preference (OSP).

· IV4: Perceived Control (PC)

Perceived Control is defined as the need to demonstrate one's competence, superiority and mastery over the environment (White, 1959), and human desire to feel dominant and influential (Ward & Barnes, 2001). It has a significant role in explaining consumer behavior and research suggests that online shoppers search for freedom and control, where the emphasis is on goal accomplishment rather than on having a compelling experience Wolfinbarger & Gilly (2001). Online shoppers take pleasure in the lack of commitment (Mick & Fournier, 1998) because it increases their efficiency, helps them minimize the effort of making a purchase, thus increasing their sense of control. It is the fourth factor perceived by consumers as a major functional value of the online shopping experience. According to Koufaris, 2002, Perceived control leads to less risk perceived by consumers, hence serves as a motivator for consumers to engage in online shopping. In this study, it is suggested that Perceived Control (PC) will have a positive effect on Online Shopping Preference (OSP)

· IV5: Cost reduction (CR)

The fifth factor thought to have significant effect on online shopping functional value perceived by consumers is the possibility of cost reduction, which has been identified as by Mathieson (1991), who stated that the possibility of price comparison of products and better prices compared to traditional shopping would serve as a motivator for online shopping preference. (Rosen & Howard, 2000 establish a positive relationship between reduced cost as a value of online shopping and preference of consumers to online shopping. In this study, it is suggested that Cost Reduction (CR) will have a positive effect on Online Shopping Preference (OSP)

· IV6: Wider Selection (WS)

The sixth factor, Wider Selection, was identified by Gilly & Wolfinbarger (2000); Rosen & Howard (2000); and Wolfinbarger & Gilly (2001) as a functional value of the online shopping experience. Wider selection offers consumers the capacity to make more efficient price comparisons (Donthu & Garcia 1999), and through utilizing the advantage of online price transparency and low search costs, purchase decisions are easier, more reliable and show significant cost reduction. Bagdoniene & Zemblyte (2009) suggested a positive relationship between wider product selection and online shopping preference. In this study, it is suggested that Wider Selection (WS) will have an influence on Online Shopping Preference (OSP)

· IV7: Usefulness/ Achievement (US)

The seventh and final factor, Usefulness, is defined as a shopping orientation related to goal accomplishment, where success in finding specific products that were planned for at the beginning of the shopping experience is important (Kim, 2004 in Kim, 2006. Childers, Carr, Peck & Carson, 2001 define usefulness as the degree to which the use of the selected medium will improve user performance, mostly referring to the outcome of a shopping experience; furthermore, they indicate that usefulness serves as a motivator for preference of the internet as a retail channel. In this study, it is suggested that Usefulness (US) will have a positive effect on Online Shopping Preference (OSP) Variables Related to Online shopping Utilitarian Value as perceived by consumers.

· IV8: Adventure Shopping (AS)

The first factor thought to be perceived by consumers as a hedonic value of online shopping is adventure shopping, described as shopping for excitement, adventure, and stimulation through diverse environments (Arnold & Reynolds, 2003). Koufaris (2002) established a positive relationship between the perceived hedonic value of online shopping and preference to the internet as a retail channel. Therefore, Adventure shopping (AS) will be proposed in this study as a positive influencer of Online Shopping Preference (OSP).

· IV9: Social Shopping (SS)

The second identified factor is social shopping, defined as shopping with a focus on the social benefits of shopping with friends and family (Arnold & Reynolds, 2003). Social shopping (SS) will be proposed in this study as a positive influencer of Online Shopping Preference (OSP).

· IV10: Gratification Shopping (GS)

Gratification Shopping is described as shopping as a means to generate a positive feeling, and make one feel better. (Arnold & Reynolds, 2003) It relates to fun, excitement and is the third variable identified as factor of consumer perceived hedonic value. Gratification shopping (GS) will be proposed in this study as a positive influencer of Online Shopping Preference (OSP).

· IV11: Idea Shopping (IS)

The fourth factor perceived as a hedonic value of online shopping by consumers is idea shopping, described as shopping to gather information about new trends, fashions, and products (Arnold & Reynolds, 2003). Idea shopping (IS) will be proposed in this study as a positive influencer of Online Shopping Preference (OSP).

· IV12: Role Shopping (RS)

Role shopping is described in the literature as shopping for the pleasure of making purchases for others and finding the perfect gift (Arnold & Reynolds, 2003) and is the fifth factor considered to be perceived by consumers as a hedonic value of online shopping. Role shopping (RS) will be proposed in this study as a positive influencer of Online Shopping Preference (OSP).

· IV13: Value Shopping (VS)

The final and sixth variable identified is Value Shopping, described as shopping for the joy of hunting for bargains, finding discounts, and seeking sales (Arnold & Reynolds, 2003). Value shopping (VS) will be proposed in this study as a positive influencer of Online Shopping Preference (OSP).

1.4.1.3 Moderating Variables

The researcher will examine the role of the following moderating variables in this research,

* Product Type (PT)

Three broad product categories are identified in this research:

(1) Search Product

Defined as one that a consumer can make a decision about by inspection prior to purchase & when full information for dominant product attributes can be known prior to purchase (Nelson, 1970, 1974 cited in Girard, Korgaonkar, & Silverblatt (2003). Examples of search products, as defined in the literature are 'Books and Personal Computers' (Girard, Korgaonkar & Silverblatt, 2003: 111).

Based on the researchers' previous empirical findings, the Search product selected for testing in this study will be 'Books'.

(2) Credence product

Defined as a product for which the average consumer, even after use, cannot verify the level of quality or even their level of need for (Darhy & Karni, 1973 in Girard, Korgaonkar, & Silverblatt (2003). Credence qualities are primarily found in professional contexts, such as medical services and pension plans, because consumers usually lack the knowledge to evaluate them (Asch, 2001).

The Credence Product selected for testing in this research will be 'Vacation'. This was supported through previous literature explanation of credence products and equivalence of the meaning to this particular product.

(3) Experience product

Defined as one whose qualities a consumer cannot determine prior to purchase but rather requires direct experience, or that for which information search for dominant attributes is more costly/difficult than direct product experience.

It is further classified into (a) experience nondurable (experience 1) goods, which are high frequency of purchase goods, such as clothing and perfume and (b) experience durable (experience 2) goods, which are low frequency of purchase goods, such as cellular phones and televisions (Kline 1998: 199).

Durable and Non Durable Experience products selected for testing in this research will be 'Mobile Phone' and 'Clothes', respectively, as supported by the results of the empirical study conducted by Girard, Korgaonkar & Silverblatt (2003).

Based on the findings of Girard, Korgaonkar, & Silverblatt (2003), the researcher suggests that the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI) will be moderated by Product Type (PT). It is also suggested that online shopping intention will be greatest for Search products and Credence products, followed by Durable experience (experience-1) products and Non durable experience (experience-2) products, respectively.

· Retailer Trust (RT)

It is believed that the amount of trust towards a certain e-retailer affects how the customer perceives the risks associated with an online purchase from that e-retailer (Comegys, Hannula & Vaisanen, 2009). Trust has been proven to affect purchase intentions (Jarvenpaa, Tractinsky & Vitale, 2000; Schlosser, White & Lloyd, 2005; Shankar, Urban & Sultan, 2002; Yoon, 2002 & Buttner and Goritz, 2008). Hoffman, Novak & Peralta (1999) stated that lack of consumers' trust is one of the main hindering factors of e-transactions. Castelfranchi & Tan (2002) added that online shoppers will not get involved in online transactions unless the perceived level of trust exceeds the minimum level acceptable to the shopper.

Trust will be tested in this study by assessing consumer intention to purchase from (1) International Reliable Website, (2) Local Online Retailer. This assumption was made based on the opinions of experts gathered during the structured interviews conducted in the research process. The researcher suggests that the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI) will be moderated by Retailer trust (RT).

· Payment Method (PM)

The payment methods utilized in this study are (1) Cash on delivery, (2) Online credit card payment. The researcher believes that these two methods will be a good measure of the 'Payment Risk' associated with online shopping, defined in the previous literature as risk that there might be financial consequences of online payment (Credit Card number)(Cases, 2002; and Forsythe & Shi, 2003).

The researcher suggests that the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI) will be moderated by Payment Method (PM).

1.4.2 Research Assumptions

The following are the assumptions the researcher used in this research:

* A1: The researcher assumes that the three products tested, 'Books', 'Clothes' & 'Mobile phones', are representative of three product categories identified in the literature, namely Search, Non Durable Experience and Durable Experience Products, respectively. This assumption was supported by previous findings by Girard, Korgaonkar & Silverblatt (2003).

* A2: The researcher assumes a 'Vacation' will be a sufficient representative of a Credence product. This assumption was supported through previous literature explanation of credence products and equivalence of the meaning to this particular product.

* A2: The researcher assumes that Trust in the Retailer (TR) is best measured by the selected items, 'International Reliable Website' & 'Local Online Retailer'. This assumption was supported by the opinions of experts gathered during the structured interviews.

* A3: The researcher assumes that the payment methods to be tested 'Cash on delivery' & 'Online Credit Card payment' are sufficient to establish the difference in purchase intention in payment methods exhibited by consumers. This assumption was supported by the opinions of experts gathered during the structured interviews.

* A4: The researcher assumes that a relationship exists between the model's variables, as proven by previous researchers Sahney, Shrivastava, & Bhimalingam (2008); Koufaris (2002); Childers, Carr, Peck, & Carson (2001); Kim (2006), Chen & Lee (2008) and Zhang (2006).

1.4.3 Research Limitations

The following are the limitations the researcher found relevant to this research:

* L1: This research is limited to internet users, as it is believed that only internet users are eligible for online shopping.

* L3: This research is limited to the Egyptian population living in Cairo, as due to time constraints the researcher will not be able to travel outside of Cairo.

* L4: This research is limited to non-probability sampling, but the researcher believes that it yielded a fair representation.

* L5: this research does not take into account the effect of other moderating and independent variables that may have an effect on Online Shopping preference and Online Purchase Intention such as Personality factors, Risk perception and Cultural factors.

1.5 RESEARCH QUESTIONS

1.5.1 Major Research Questions

(MjRQ1) What are the factors that mostly influence Egyptian consumers' choice of the internet as their prefered retail channel?

(MjRQ2) What are the factors that moderate the Online Purchase Intention of Egyptian consumers?

By answering these questions, the researcher should be able to provide retailers with direction and help them optimize their marketing strategy and capitalize on available opportunities. It will be a means for those retailers to know what they need to focus on in order to succeed in the Egyptian market.

1.5.2 Minor Research Questions

The minor questions test the relationships that are in the form of hypotheses developed by the researcher in this research model diagram displaying the factors influencing and moderating consumer's online shopping preference and intention. The questions address the relationships between the model variables that were established by previous researchers Sahney, Shrivastava, & Bhimalingam (2008); Koufaris (2002); Childers, Carr, Peck, & Carson (2001); Kim (2006), Chen & Lee (2008) and Zhang (2006).

(MnRQ1) To what extent does the Ease of use (EU) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ2) To what extent does the Convenience (CO) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ3) To what extent does the Informativeness (IF) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ4) To what extent does the Wider Selection (WS) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ5) To what extent does the Perceived Control (PC) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ6) To what extent does the Cost Reduction (CR) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ7) To what extent does the Usefulness (US) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ8) To what extent does the Adventure Shopping aspect (AD) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ9) To what extent does the Social Shopping aspect (SO) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ10) To what extent does the Gratification Shopping aspect (GR) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ11) To what extent does the Idea Shopping aspect (ID) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ12) To what extent does the Role Shopping aspect (RO) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ13) To what extent does the Value Shopping aspect (VA) associated with online shopping as a retail channel positively affect Online Shopping Preference (OSP)?

(MnRQ14) To what extent does Product Type (PT) moderate the relationship between Online Shopping preference (OSP) and Online Purchase Intention (OPI)?

(MnRQ15) To what extent Retailer Trust (RT) moderate the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI)?

(MnRQ16) To what extent does Payment Method (PM) moderate the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI)?

Research Hypotheses

The research null and alternative hypotheses are formulated to test the relationships among variables as follows:

Table 1.1: Formulation of the Null and Alternative hypothesis of the research

Acronym

Null Hypothesis

Alternative Hypothesis

H1a

Ease of Use (EU) associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

Ease of Use (EU) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H1b

Convenience (CO) associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

Convenience (CO) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H1c

Informativeness (IN) associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

Informativeness (IN) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H1d

Wider Selection (WS) associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

Wider Selection (WS) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H1e

Perceived Control (PC) associated with Online Shopping does not positively affect Online Shopping preference (OSP).

Perceived Control (PC) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H1f

Cost Reduction (CR) associated with Online Shopping does not positively affect Online Shopping Preference (OSP)

Cost Reduction (CR) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H1g

Usefulness (US) associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

Usefulness (US) associated with Online Shopping positively affects Online Shopping Preference (OSP).

H2a

The Adventure Shopping (AD) aspect associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

The Adventure Shopping (AD) aspect associated with Online Shopping positively affects Online Shopping Preference (OSP).

H2b

The Social Shopping (SO) aspect associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

The Social Shopping (SO) aspect associated with Online Shopping positively affects Online Shopping Preference (OSP).

H2c

The Gratification Shopping (GR) aspect associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

The Gratification Shopping (GR) aspect associated with Online Shopping positively affects Online Shopping Preference (OSP).

H2d

The Idea Shopping (ID) aspect associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

The Idea Shopping (ID) aspect associated with Online Shopping positively affects Online Shopping Preference (OSP).

H2e

The Role Shopping (RO) aspect associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

The Role Shopping (RO) aspect associated with Online Shopping positively affects Online Shopping Preference (OSP).

H2f

The Value Shopping (VA) aspect associated with Online Shopping does not positively affect Online Shopping Preference (OSP).

The Value Shopping (VA) aspect associated with Online Shopping positively affects Online Shopping Preference (OSP).

H3

Product Type (PT) does not moderate the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI).

Product Type (PT) moderates the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI).

H4

Retailer Type (RT) does not moderate the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI).

Retailer Type (RT) moderates the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI).

H5

Payment Method (PM) does not moderate the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI).

Payment Method (PM) moderates the relationship between Online Shopping Preference (OSP) and Online Purchase Intention (OPI).

1.6 RESEARCH METHODOLOGY

1.6.1 Research Type

This research is of an analytical nature as it is aimed to define the factors that mostly influence Online Shopping Preference and Online Purchase Intention. The research lies in the quantitative paradigm, it will produce quantitative data, using a large sample due to the large size of the population under study, and is concerned with the hypothesis testing in its application. The research is deductive, as the models it has been adapted from were tested before in different parts of the world including United States, England, New Zealand, China, Brazil, Bulgaria and India and the researcher intends to test it locally amongst the Egyptian consumers. Finally, the research is basic, as it is done mainly to enhance the understanding of certain problems that commonly occur in organizations; however the researcher believes it can easily be converted into the applied type and used by applying its findings to solve a specific, existing problem demanding a timely solution in the world of e-commerce.

The researcher uses a cross functional questionnaire to take a snapshot of the current situation in the Egyptian market and use the data gathered to measure the factors that influence the Online Shopping Preference and Online Purchase Intention of Egyptian consumers. Data Collection methods

The data collection in this study consists of two steps as follows:

* STEP 1

Eight structured interviews with experts in the field of e-commerce were conducted (Appendix A). Their areas of expertise range from pure service providers, such as internet banking (HSBC), academic research database providers (ENSTINET), e-booking and reservations (Thomas Cook) and Cell phone service providers (undisclosed source); an internet based fast food ordering service provider (otlob.com); Facebook based internet apparel & cosmetic retailers (Bath & Body Works); a local Candle manufacturer & retailer (Relax Candle Store) and an internet based Grocery store pioneer (elsou2.com); The aim of these interviews was to collect firsthand information from experts with a reliable and strong understanding of the Egyptian consumers and of the disciplines understudy. The experts validated the selected influential factors as well as the determined product types, retailer types and payment methods used. It also provided depth about the subject understudy (Appendix A).

* STEP 2

The outcome of the unstructured interviews was then filtered to produce a close-ended-questions questionnaire that would be completed by convenience and snowball sampling techniques (Appendix B). This is a structured, conclusive phase that provided quantitative data, to test the specific hypotheses and examine the relationships amongst the outlined variables.

The researcher adopted most of the survey instrument's questions and scales from previous studies conducted by previous researchers to ensure validity and reliability. Four previously used questions were eliminated from the survey due to irrelevance. The scale used in the questionnaire was unified to the 5-point-Likert Scale as used in most previous studies. The researcher added one multiple Choice question for conclusive purposes in the study. Table 3.1 provides a summery of the sources from which questionnaire was adapted.

Table 1.2: Sources and Scales of the questionnaire

Variable

Source

Scale

Ease of use

Childers, Carr, Peck & Carson (2001)

7 point Likert

Convenience

Bagdoniene & Zemblyte (2009)

5 point Likert

Control

Koufaris, 2002

7 point Likert

Informativeness

Bagdoniene & Zemblyte (2009)

5 point Likert

Wider selection

Delafrooz, Paim & Khatibi (2009)

5 point Likert

Cost reduction

Delafrooz, Paim & Khatibi (2009)

5 point Likert

Usefulness

Childers, Carr, Peck & Carson (2001)

7 point Likert

Adventure

Kim (2006)

5 point Likert

Gratification

Kim (2006)

5 point Likert

Social

Kim (2006)

5 point Likert

Role

Kim (2006)

5 point Likert

Idea

Kim (2006)

5 point Likert

Value

Kim (2006)

5 point Likert

Preference

Joseph, Grewal, Mangleburg, Park, Chon, Claiborne, Johar & Berkman (1997)

5 point Likert

Intention

Putrevu & Lord (1992)

7 point Likert

The seven variables related to Perceived utilitarian value represented a section in the questionnaire, as did the six variables related to Perceived hedonic value and the Online Shopping Preference variable. The researcher then proceeded with questions related to the three moderating variables in the following sections. The first section of the survey referred to internet usage habits of the respondents, and the last section referred to their demographics. Almost all the questions were graded on a 5-point-Likert scale, where 1 indicates Strongly Disagree and 5 indicates Strongly Agree).

The questionnaire was made available in English and was posted on the internet for two weeks. It was circulated via e-mail and Facebook to colleagues, friends and family members for completion. At the end of the two weeks days 315 respondents had completed the survey. Table 3.3 provides a summary of the Mapping of Research Questions, Hypotheses, Dimensions and Items in Questionnaire.

Table 3.3: Mapping of Research Questions, Hypotheses, Dimensions and Items in Questionnaire

Minor Research Questions

Alternative Hypothesis

Theoretical

Framework

Variables

Operationalization

Items in Questionnaire

MnRQ1: To what extent does EU positively affect OSP?

Ha1a: EU positively affects OSP

* Ease of Use (EU)

* Online Shopping Preference (OSP)

Relationship between EU and OSP

MnRQ2: To what extent does CO positively affect OSP?

Ha1b: CO positively affects OSP

* Convenience (CO)

* Online Shopping Preference (OSP)

Relationship between CO and OSP

MnRQ3: To what extent does IN positively affect OSP?

Ha1c: IN positively affects OSP

* Informativeness (IN)

* Online Shopping Preference (OSP)

Relationship between IN and OSP

MnRQ4: To what extent does WS positively affect OSP?

Ha1d: WS positively affects OSP

* Wider Selection (WS)

* Online Shopping Preference (OSP)

Relationship between WS and OSP

MnRQ5: To what extent does the PC positively affect OSP?

Ha1e: PC positively affects OSP

* Perceived Control (PC)

* Online Shopping Preference (OSP)

Relationship between PC and OSP

MnRQ6: To what extent does CR positively affect OSP?

Ha1f: CR positively affects OSP

* Cost Reduction (CR)

* Online Shopping Preference (OSP)

Relationship between CR and OSP

MnRQ7: To what extent does US positively affect OSP?

Ha1g: US positively affects OSP

* Usefulness (US)

* Online Shopping Preference (OSP)

Relationship between US and OSP

MnRQ8: To what extent does AD positively affect OSP?

Ha2a: AD positively affects OSP

* Adventure Shopping (AD)

* Online Shopping Preference (OSP)

Relationship between AD and OSP

MnRQ9: To what extent does SO positively affect OSP?

Ha2b: SO positively affects OSP

* Social Shopping (SO)

* Online Shopping Preference (OSP)

Relationship between SO and OSP

MnRQ10: To what extent does GR positively affect OSP?

Ha2c: GR positively affects OSP

* Gratification Shopping (GR)

* Online Shopping Preference (OSP)

Relationship between GR and OSP

MnRQ11: To what extent does ID positively affect OSP?

Ha2d: ID positively affects OSP

* Idea Shopping (ID)

* Online Shopping Preference (OSP)

Relationship between ID and OSP

MnRQ12: To what extent does RO positively affect OSP?

Ha2e: RO positively affects OSP

* Role Shopping (RO)

* Online Shopping Preference (OSP)

Relationship between RO and OSP

MnRQ13: To what extent does VA positively affect OSP?

Ha2f: VA positively affects OSP.

* Value Shopping (VA)

* Online Shopping Preference (OSP)

Relationship between VA and OSP

MnRQ14: To what extent does PT moderate the relationship between OSP and OPI?

Ha3: PT moderates the relationship between OSP and OPI

* Product Type (PT)

* Online Shopping Preference (OSP)

* Online Purchase Intention (OPI)

Moderating effect of PT on relationship between OSP and OPI

MnRQ15: To what extent does RT moderate the relationship between OSP and OPI?

Ha4: RT moderates the relationship between OSP and OPI

* Retailer Trust (RT)

* Online Shopping Preference (OSP)

* Online Purchase Intention (OPI)

Moderating effect of RT on relationship between OSP and OPI

MnRQ16: To what extent does PM moderate the relationship between OSP and OPI?

Ha5: PM moderates the relationship between OSP and OPI

* Payment Method (PM)

* Online Shopping Preference (OSP)

* Online Purchase Intention (OPI)

Moderating effect of PM on relationship between OSP and OPI

1.6.2 Sampling Criteria

The population size of internet users in Egypt is approximately 13.5 millions (Ministry of Communications and Information Technology). The sample size should be 384 customers according to Krejcie & Morgan (1970) in Sekaran (2003). The issue of sample size is one of several where there is no consensus. The researcher considered the above as the guideline for the sample size.

The sample chosen was based on prior knowledge of internet usage and was conducted through a non-probability convenience snowball sampling design since the sample is a group of respondents that were selected by the researcher, and the subsequent respondents were selected based on referrals by the initial respondents. The researcher managed to receive 315 respondents. Moreover, a non-probability judgmental sampling approach was also used for the first phase that includes experts in the field of e-commerce. The exploratory research criterion is used in this phase because the researcher needs firsthand information from the experts in the field to validate the variables identified and to obtain more insight and understanding on the subject matter. The size of the sample that the researcher was able to contact is eight experts.

1.6.3 Data Analysis Methods

The results of the questionnaire are analyzed using various analysis methods and an interpretation of the data collected from the closed ended questionnaire will follow in the next chapters. The analysis of the data is divided into four parts, 1) Descriptive analysis, 2) Reliability testing, 3) Inferential data analysis, and 4) Managerial analysis.

1) Descriptive Analysis:

The researcher described the questionnaire data collected through frequency tables, measures of central tendency (mean and standard deviation) and graphical presentations (Pie charts and bars).

2) Reliability testing:

Reliability test was used to provide information about the relationships between individual items in the scale. Cronbach's Alpha is the model of reliability tests that was used to indicate the consistency of internal data, based on the average inter-item correlation. The closer the coefficient to 1, the higher the internal consistency of the scale; and most professionals agree that a Cronbach's Alpha value neighboring 0.70 is considered reliable (Sekaran, 2003). The researcher considered all variables with Cronbach's Alpha larger than 0.70 as reliable in this study.

1) Inferential data analysis:

The researcher analyzed each dependent variable separately. Relationship between the factors perceived as values of shopping and Online Shopping Preference (OSP) was analyzed through multiple Regression Analyses across the overall data, then across each component of the different demographic groups identified in the study: Gender (Male & Female); Age Group (≤35 yrs and >35 yrs); Income level (≤ 5000 LE/month and >5000 LE/month) and Educational Level (Graduates/Pre-graduates and Post Gradates). A total of nine Regression Analysis tests were run on these variables. The conclusion of this section of analysis was the identification of the factors that influence Online Shopping Preference.

The second variable, Online Purchase Intention (OPI) was measured in sixteen different scenarios offering a combination of the moderating variables used in this study, namely, the four Product Types (PT): Search, Durable and Nondurable Experience Products and Credence Products; as well as the two retailer types signifying Retailer Trust (RT), International and Local Retailers and finally the two Payment Methods (PM), Cash and Credit Card Payment. Multiple Regression Analyses were performed for each moderating variable and the Online Purchase Intention (OPI) was measured across four different situations for Product Type, and across eight different situations in the cases of Retailer Trust (RT) and Payment Method (PM).

Regression Analysis was used because it is considered the best method of analysis when the focus is on the relationship between a dependent variable and one or more independent variables.

4) Managerial analysis:

For this section of data analysis, the researcher focused on the variables of the model separately. Analyses of the factors perceived as functional and hedonic values of online shopping were performed; as well as analysis of Online Shopping Preference (OSP) and Online Purchase Intention (OPI). The researcher then conducted a more specific analysis on Product Types, Retailer Trust (RT) and Payment Method (PM) variables, identifying their overall variances as well as across demographic and internet usage habits groups. Detailed analysis of the differences in results observed across demographic groups and Internet usage habits groups was also executed. Finally, the researcher analyzed the cross shopping pattern of the respondents in relation to other variables in an attempt to come up with trends or habits that would be of managerial benefit. For the managerial analysis section of this research, the user executed One Sample T-Tests, Paired Samples T-Tests, Independent Samples T-Tests, One-way ANOVA, Chi-Squares, Frequency Tests and Means Analysis.

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