The quick growth of the number of consumer products’ offered on the Internet has exposed modern users to the need of making multiple choices. The most problematic aspect of such a situation is that every single consumer has to make a rational decision in the process of product choice. It is essential to realize that with an increase in the number of online merchants, the use of Online Decision Aids has gradually replaced the cognitive effort of the selection of online products. However, there are seemingly no studies that explored and synthesised various ODA tools’ properties and might provide scholarly recommendations for Virgin media in this respect. The current study addresses the paucity of research on the above-mentioned topic by employing quantitative techniques. The latter allow collecting data in order to address the problem that consumers are facing nowadays with regard to product’s choice while shopping online. This paper employs various theoretical frameworks to explain how Online Decision Aids (ODA) has been able to enhance decision-making of online shoppers. The case study of the companies like Senso Solutions and Apple is used to support the findings. This paper employs quantitative method for data collection and data analysis. The findings from survey are presented in tabular and graphical forms to enhance their understanding. According to the graphical illustrations, Comparison matrix is the best ODA tool that this study can recommended for Virgin media. This result has been based on the literature review analysis and comparison of graphic results of five ODA.
This study has provided several contributions to the topic of ODA use for online shopping. First, the paper bridges the gap created by the lack of research on a most suitable ODA for Virgin Media. Second, the study provides greater understanding of the help that online merchants present to customers for them to make rational choice of products with ODA tools. Finally, the dissertation enriches the knowledge of businesses, individual, and the general community on the effectiveness of ODA tools.
Keywords: Online Decision Aids, Virgin Media, online shoppers, decision making, rational decision
Background of the study
The explosive growth of internet has accelerated the growth of electronic commerce where computer-mediated environment conditions the increase of necessity of rationale consumer purchasing decisions. Increase in the availability of online products has made many consumers realise that web based environment offers immense opportunities for them to chose the products and services they desire. Despite the increase in the number of online stores, many Internet shoppers cannot find products that meet their needs. The main reason is that the web-based environment is characterised by varieties of similar and different products. Thus, consumers face the need to make overwhelming multiple decisions because of the mentioned products variety (Punj & Rapp, 2004).
Additionally, consumers often encounter problems in evaluating products in the online environment. This affects their ability to make effective decisions regarding the products they intend to purchase (Haubl and Trifts, 2000). The technological advancement has also made the selection of products more complex within the online environment. Accordingly, the technological advancement has made consumers’ demands more sophisticated, while the consumers have become more intelligent and mature in product selection products. Thus, consumers need information about products before making purchasing decisions (Pereira, 1999). With a bid to attract customers in the electronic commerce environment, many businesses are employing sophisticated electronic aids to help customers make decisions on the products they intend to purchase (Sharkey, Acton, and Conboy, 2009).
However, the rapid advancement in Information Technology (IT) has provided solution to this issue. Numerous online merchants have started implementing sophisticated software in the form of Online Decision Aids (ODA) or Computerised Aided Decision to provide intelligent interfaces for consumers’ decision-making. Designed for aiding online shoppers in making rational decisions during products’ selection, the ODA system has been noted to facilitate sales promotion and enhance quality support among consumers (Pereira, 1999). Thus, ODA has become an extra advantage for online merchants to attract customers, because the former make use of Online Decision Aids to assist customers in order to sell varieties of products. Despite the explosive growth in electronic shopping, very little is known about the ways in which online merchants make use of ODA to help consumers in decision-making.
With little understanding in this area of research, the current dissertation attempts to provide detailed academic study of Online Decision Aids (ODA). To understand how online merchants enhance customer’s satisfaction through the adoption of ODA, this study uses Virgin Media as a case study. The paper reviews various academic sources to explore different types of ODA and suggest a suitable one for Virgin Media to aid customers in products’ selection in the online environment. The selection of Virgin Media enhances greater understanding of the study because this company’s online environment provides a broad research field for deployment of a new ODA tool. For example, the company web page presents detailed specifications of a particular product, but the common ODA tool to aid customers in product’s selection is lacking. For clearer understanding of this research area, research aims and objectives are provided below.
Aims and Objectives
The goals of this dissertation are to explore the importance of ODA. The paper gains insight into numerous academic sources in order to identify and explore different types of ODA. Additionally, this paper recommends the most suitable Online Decision Aids for Virgin Media. To achieve the goals of the dissertation, the paper develops the following aims and objectives:
- To identify the commonly used Online Decision Aids that many organisations employ to influence their customers’ decision-making;
- To demonstrate the usefulness of Online Decision Aids in affecting customers’ decisions in the online shopping environment;
- To identify the advantages and disadvantages of ODA that online merchants use to help customers in the online decision-making;
- To identify factors that affect human decision-making with respect to product’s selection in the online environment.
To achieve the research aims and objectives, the paper addresses the following research problem.
Prior scholars have attempted to find out how online merchants make use of Online Decision Aids to help customers in making rational decisions during product selection. Becwati and Xia (2003) argue that customers’ preferences have shifted from cognitive decision making to the electronic decision-making process. It essential to realise that with the increase of online merchants’ numbers, the role of Online Decision Aids has also increased (Peter & Izak, 1994). However, such brief mentionings of the topic of ODA in the modern business and its online environment cannot be regarded as a full and comprehensive analysis of the whole topic. Although scholars have tried to explore how online merchants have been able to influence customers’ decision making through ODA, seemingly no studies explored various ODA types and provided scholarly recommendations of a best ODA for an online merchant. In addition, there is no study that has recommended a suitable ODA tool for Virgin Media. The paucity in this area of research generates research problem, which the current study attempts to address.
In particular, the significance of the current research can be expressed in terms of four major points of interest. They include the obvious lack of scholarly attention to the increasing importance of ODA in the modern business world; the seemingly insufficient amount of research papers considering the effects of ODA use upon the functioning of businesses in the online environment; the fact that no scholars used the Pearson Correlation model to measure the relation between two or more ODA tools; and the absence of scholarly recommendations designed specifically for Virgin Media in respect of the ODA tools it can use at the highest level of efficiency. Each of these points has crucial importance for the overall development of knowledge concerning the use of modern information systems in the permanently updating online business environment.
Accordingly, the significance of the current study lies, first of all, in the fact that it can provide the modern science with at least some data (only some data, because it is obviously impossible for one study to cover all the aspect of such a wide topic and to analyze it in all possible contexts) regarding the use of Online Decision Aids in online business relations. Respectively, these data might also be essential in recommending the best ODA tools that a specific representative of the modern online commerce, i. e. Virgin Media, can implement to increase its business efficiency.
In more detail, the literature review presented in Chapter 2 reveals that the topic of ODA in business has been underestimated by scholars and requires additional in-depth research. Further on, the fact that it turned out to be quite a difficult task to collect scholarly sources regarding ODA is another proof of the point that this topic lacks specific and focused research. It is seemingly obvious that if a topic acquires such importance as ODA in the online environment has, it is necessary to research the topic and provide scholarly ideas regarding its current conditions and developmental perspectives. Accordingly, if the topic lacks such an in-depth research, any well-documented and properly designed attempt to eliminate this lack is of crucial importance for the development of the knowledge on this topic on the whole.
Further on, the previous scholarly works show that no scholars used the Pearson Correlation model in their analyses of ODA tools. So, the current paper’s significance is also in the fact that it attempts at considering the topic of ODA and customer online decision making through the perspective of this model. The Pearson Correlation model uses variables X and Y to mark the discussed phenomena and implements the coefficient from +1 to -1 inclusively to characterize their correlation. So, this study will attempt at considering the topic from this point of view and will use the Correlometer (see Appendices section) to ensure data objectivity.
Finally, Virgin Media has probably been not the primary focus area for scholarly research, and it is a challenging task to recommend the best ODA tools for the company. Drawing from this, the lack of prior research on the topic also proves the significance of the current paper, which not only studies the roles played by ODA tools in customers’ decision making, but also projects its findings on a specific setting, i. e. Virgin Media, and derives implications of its findings for this very company. Accordingly, the following research questions are designed to facilitate the development of such an obviously significant study.
The dissertation formulates the following research questions to gain insight into how online merchants enhance the customer’s satisfaction through ODA.
- What are the familiarity levels of the respondents with the further discussed 5 ODAs?
- Which extent of the perceived ease of use is attributed to each of the selected 5 ODAs?
- How useful are the selected 5 ODAs as perceived by the survey respondents?
- What are the effectiveness levels of the selected 5 ODAs for shopping decisions as perceived by the survey respondents?
To answer the research questions, the paper collects data through survey method and the specifically designed questionnaire.
Overview of the study
This section provides the methods via which the dissertation is arranged. So, Chapter 1 provides the introduction, which includes the research aims and objectives. This chapter also formulates the research problem and presents the research questions. Chapter 2 develops the literature review and summarizes the findings by previous scholars. Chapter 3 is the account on the research methodology. This chapter examines the methods of data collection, research design, and data analysis. The paper uses Virgin Media, and other online merchants as case studies to examine the effectiveness of ODA tool that these online merchants employ to enhance decision making during product selection.
Chapter 4 provides the results of the data analysis. The findings help the study solve the research problem and answer the research questions. Additionally, the findings add new knowledge regarding ODAs within the business environment and academic community. Chapter 5 explains the design and the tools used for the study. The focus of Chapter 6 is the evaluation of the research design, while Chapter 7 summarizes the whole study and presents conclusion and discussion.
This chapter presents the background of the study together with the research aims and objectives. The chapter also formulates the research problem that the study addresses. In addition, research questions and the overview of the study are presented.
This chapter reviews previous scholarly studies related to the dissertation topic. In particular, the chapter examines the theoretical framework related to the study. Numerous scholarly articles are reviewed to explore five ODA tools that online merchants employ to aid customers in making rational choice of products. It is essential to realise that no literature has been able to synthesize up to five ODA tools and recommend a suitable ODA for online merchants. It is expected that with the analysis of different sources relevant to the topic a suitable ODA tool for online merchants can be defined and recommended. The paper further compares five different types of ODA tools. Through synthesizing the ODA tools discussed by various scholars, the paper recommends the most appropriate one for Virgin Media.
Proposed theoretical framework
There are several theories that enhance the understanding of Online Decision Aids. For example, Miller (2005) develops Information Process Theory to argue that human cognition has limited capacity to process large volumes of information. With the development of computer information systems, the chance emerges to gather and process large amounts of information for use by human beings. Michael and Qimei (2006) also consider the limitation of human cognition in making rational choices when facing many similar products. The authors employ rational choice theory to explain how online shopper makes rational decision with Online Decision Aids. According to the Rational Choice Theory (RCT), the global explosive development of online business and the growth of online products’ assortment made consumers develop the above mentioned cognitive limitations in making rational choice.
In this respect, it is also essential to realise that the technological advancement and globalisation of online market environment have led to shorter products’ lifecycles. Thus, consumers have faced challenges of making rational decisions during products’ selection. The situation is complicated by the fact that consumers often lack time to consider the alternatives and have insufficient information to ground their choices. Thus, the Rational Choice Theory argues that the consumer needs information-rich environment before making rational choices. Here, it should be noted that a decision, or a choice, is considered rational when its effect is enough to increase and, later, maximize, the consumer’s welfare. Accordingly, to enhance consumer’s rational decision making, RCT explains that online merchant needs to use sophisticated decision support tools to help consumers reduce the human cognitive stress during products’ selection.
However, there is debate among scholars whether RCT has been able to provide rational decision for online consumers. For example, Hansson (2005) counters RCT by proposing the Decision Theory. The author argues that the Decision Theory explains how an online merchant helps customers to choose the best products in an online market environment. For example, an organisation might employ various variables, such as price, taste, and combination of these, to help consumers to choose between two or more alternatives. For example, an organization might argue that:
- Product A is better than product B
- Product B is better than product C
- Product A is better than product C
With such an approach, it is easy for a consumer to choose an alternative that has the highest utility. However, the weakness of the Decision Theory in this context is that it is not applicable for enhancing the choices between completely equal products (Hansson, 2005). However, the Economic Theory provides solution to the weakness of the Decision theory by arguing that the rule of maximisation can be applied to the decision making process. More specifically, if a consumer faces several alternative products that have the same utility, he/she should choose one of these products because the economic theory is based on the maximisation of individual holding, which is relatively measured in monetary value (Stewart and DeMarco, 2005).
Luis (2007) provides similar argument by proposing the Bayesian Decision-Theory which consumers employ to maximise utility. Typically, information is crucial for decision-making, and a person with positive information about a product is more likely to make rational decision than a person possessing negative information about the same products. Luis (2007) further argues that a consumer armed with an Information Processing Technology tool is more likely to make rational decisions in product selection than a consumer using other methods.
All the above considered theories are employed to understand different Online Decision Aids that the online merchants use to help their customers in making rational decision during product selection in the online environment.
Overview of Online Decision Aids
This section is an overview of different types of Online Decision Aids. The above literature review discusses the basic theories surrounding ODA, while this section attempts at considering the potential usefulness of ODA tools for easing consumer choices in the online environment. Defining ODAs is also a task for this section.
So, different scholars give different definitions of ODA. According to Punj and Rapp (2004), ODA is a mechanism by which online merchants help consumers in making the rational product selection in the online business environment. Typically, online merchants employ interactive computer programs to help consumers in the decision making process. It is essential to realise that with Online Decision Aids, there is an interactive mechanism between e-retailers and online consumers that helps the latter in decision making. However, Merz & Chen (2006) argue that the ODA is not only the internet tool as some online merchant makes use of short films, pictures, and sounds to help customers in making rational decisions. Merz & Chen (2006) define ODA as a means for an online merchant to combine online and offline information processes to maximise consumer’s welfare in the selection of the best products. Garnett (2004), thus, defines ODA as the intelligent decision aid that makes selection of products easier for consumers. To provide greater understanding of ODA, it is essential to explore their different types.
Different types of ODA
In this section, the study identifies five different types of ODA, which are Query-Based Electronic Decision Aid (QBDA), Comparison Matrix (CM), Recommendation Agent (RA), Maxim, and Consumer Review making references to the relevant literature. The rationale behind choosing these ODA tools is that scholars like Pereira (2001), Wan, Menon and Ramaprasad (2009), and Lee, and Kwon (2008) consider them to be the most effective ODAs in making consumer choices more informed.
Thus, Pereira (2001) argues that Query-Based Electronic Decision Aid (QBDA) is a set of ODA tools used for search and decision making on the Internet. Pereira (2001) claims that the QBDA is useful to assist consumers in refining products according to their preferences. Typically, the QBDA aids consumers by examining the attributes of different products and recommending the most fitting ones to consumers. It is essential to realise that QBDA has the same potential of computer decision aids that empower consumers to make informed decisions.
Further on, Wan, Menon, and Ramaprasad (2009) suggest two more types of electronic decision aids, including the Comparison Matrix (CM), and the Recommendation Agent (RA). The authors argue that CM facilitates the process of comparing products side-by-side, and then provides data regarding the attributes of the products to consumers. Another type of the Online Decision Aid is what Maes (1994) refers to as Maxim, i. e. a tool using memory-based reasoning to relate a consumer’s previous decisions to his/her further choices.
Moreover, Wietsma and Ricci (2005) also identify Consumer Review as a type of ODA that allows customers to review the products based on experiences and information available about them. This strategy helps a consumer to rate the available products; a consumer is thus able to use this tool to make a high quality choice. However, these types of the computer assisted decision aids have both benefits and shortcomings. The next section provides the critical analysis of these ODA tools based on data from previous research.
Scholarly opinions about QBDA
Some scholars have researched advantages and disadvantages associated with QBDA use. According to Pereira (1999), QBDA’s crucial benefit is the provision of adequate information about several products, which allows consumers to process information and use it for their individual needs. In addition, the use of QDBA allows consumers to to manage information flow (Pereira, 1999). A unique benefit of QDBA is that dynamic information is provided to customers to maximise their individual benefit. Typically, a consumer will also have ability to control information flow at a time when the information is available.
Sudweek and Romm (1999) also point out that the benefits of QDBA allow consumers to efficiently screen information and enhance their ability to match the alternatives based on their preferences. The information available to consumers helps them to make rational purchasing decisions and save large amounts of search costs. The search’s cost saving is related to opportunity costs, where a consumer is able to choose the best product from the range of alternatives available.
Payne et al. (1993) also argue that QDBA is helpful to enhance the quality of a decision made by a consumer. QDBA can reduce the consumer’s cognitive effort made in searching for products, and increase the customer’s level of accuracy in product’s decision-making. Typically, the behavioural decision theorists have demonstrated in various experiments that consumers have been able to establish high levels of decision accuracy using QDBA.
Despite the pros of QDBA, scholars argue that it has certain disadvantages. Thus, Bichler, Kalagnanam, and Lee (2002) argue that QDBA requires a consumer to process lots of data, and an inexperienced online shopper might get confused facing large information amounts.
Review of works regarding Comparison Matrix (CM)
Further on, scholars have considered other ODA tools, and in particular they have identified some advantages associated CM. Wan, Menon and Ramaprasad (2009), for instance, point out that the CM enables an online consumer to make quality shopping decisions, which eliminates searching costs and increases the capability of information processing. The other advantage of CM is that a consumer can get timely help in product selection. Haubl and Trifts (2000) identify CM as a tool to make efficient selection of products using available information. For instance, the CM allows online shoppers to make in-depth comparisons on the range of several products. Haubl and Trifts (2000) point out that the CM can display the format of product’s information in an alternative row by employing the attributes of row and columns in the Matrix, which allows comparing products effectively.
At the same time, scholars identify some loopholes associated with the use of CM for online decision-making. For example, Alba et al. (1997) argue that CM would be efficient only if it is used rationally. For instance, comparing a Rolls-Royce with a cheap car will be meaningless because these two cars do not command the same price and value. Moreover, there is the need to measure the marginal cost of using CM before consumers can benefit from it.
Advantages and disadvantages of Recommendation Agent (RA)
One of the advantages identified by scholars in the Recommendation Agent (RA) is that this type of ODA can assist customers in screening alternatives products available in online stores. Haubl and Trifts (2000) argue that RA can recommend sets of products that can satisfy customers’ tastes. Typically, with recommended list of alternatives described in the brands of the products, the consumers are able to measure the attributes of the products based on their specifications. It should be noted that RA is effective in the evaluation of the products with automation systems, which allows online shoppers to limit the number of products available in the alternative lists. The automated system of RA enables online shoppers to reduce their cognitive effort during the search. In addition, the RA improves the decision quality and allows online shoppers to choose products with highest accuracy. However, the RA has some disadvantages because the ability of consumer to screen information depends on the availability of the latter, which is not always observed.
Scholarly views on Maxim
Maxim is another ODA tool discussed by scholars. As identified by Maes (1994), Maxim as an online decision aid that helps consumers to remember previous decisions made while shopping online. Typically, Maxim helps consumers to make rational decisions by mimicking their experiences in products’ selection. Thus, Maes (1994) argues tha Maxim helps online shoppers to gain competence by employing repeated use of this tactics. It allows consumers to reduce the search costs and minimise the time employed to process information. However, a disadvantage of Maxim, according to Maes (1994), is that a consumer may not be able to make a best rational choice by mimicking his/her experience in the selection of a product. With rapid development of Information Communication Technology and the use of Computer Aided Design to design new ideas, many products become obsolete within a short time. Thus, the customer’s selection of product by mimicking past information on may lead to errors in this process.
Advantages and disadvantages of Consumer Review
Consumer Review is another ODA tool widely discussed by scholars. Kerner (2004), for instance, argues that Consumer Review is an explanatory tool that enhances customers’ knowledge through product’s reviewing. In addition, the Consumer Review helps to better formulate product’s query, and this can simplify human-computer interaction. However, a disadvantage of consumer review, according to Kerner (2004), is that this ODA tool cannot ensure that consumers will make rational product’s choices based solely on their review of products in the online environment. So, with the advantages and disadvantages of the different ODA tools considered, the paper explores various factors that affect human decision-making with respect to product’s selection in the online environment.
Factor influencing human decision-making during product selection in the online environment
According to scholarly opinions, several factors influence consumers’ decisions in respect to product selection in the online environment. Degeratu et al. (2000), for instance, argue that the price information is one of the major factors that influence the product selection in the online environment. Typically, many consumers focus their search strategies on price differences when making decision about products. With the use of ODA, many consumers explore and compare different prices on products before making decisions. Although some consumers are price sensitive and others are ready to pay higher price for a product, the bulk of them prefer products at lower prices.
However, Haubl and Trifts (2000) do not agree that it is only price that influences consumer’s decision-making during product’s selection. The authors argue that the quality of a product is essential when an online shopper makes a decision. Many consumers intend to maximise their holding by selecting a product with the highest quality. The use of ODA tools may help a consumer to reduce cognitive effort and select only high quality products.
On the other hand, Brynjolfsson (2001) argues that the trust that consumers have towards e-retailers influences their rational decisions. It is essential to realise that with increase in computer fraud rates, the trust is very important before making decision in the product’s selection from an online merchant. However, Stávková, Stejskal, and Toufarová (2008) believe that product’s brand is the main influence for the customers’ rational choices. So, one can see that there are different scholarly views regarding the main factors conditioning rational choices of consumers in the online environment.
The literature review presented above reveals that there is no balance regarding the scholarly works in the theoretical and practical aspects of ODA in the modern online environment. On the one hand, scholars propose a rather borad theoretical framework for ODA study and consider various ODA types through that theoretical framework. On the other hand, the presented accounts by scholars regarding the five selected ODA tools obviously lack experimental support. So, as far as seemingly no works have been dedicated to the empirical research of ODA tools and their effectiveness in different contexts, the current research will aim at filling in this gap by using the above discussed theoretical views to ground and justify its empirical findings.
The chapter discusses the selected methods of data collection. Additionally, the research design and the basics of the selected sampling strategy are justified in this chapter. Obviously, the research methodology is essential for any kind of research, and this study is not an exception as here the methods are implemented to ensure that only the objective and reliable data are obtained (Farlex 2008). Moreover, the selected methodology enables the current paper to generate the research findings and to achieve the above listed research aims and objectives (see Chapter 1, Section 1.2). Finally, the selected research methodology allows answering the above specified research questions (see Chapter 1, Section 1.4).
The research design of the current paper is revealed in its subsequent sections. First, the topic is overviewed and areas for research are determined. Second, the methodology for data collection, i. e. the quantitative method and the survey research, are established. Data are collected from both primary and secondary sources. The primary research is the survey one, while the secondary research includes literature review and the case study of Senso Solutions and Apple companies. The next element of the research design is the data analysis methodology that should ensure the validity and reliability of the study findings. Finally, the paper presents the conclusions from the whole research and applies them to the specific setting of the Virgin Media. This step makes it possible to recommend the best ODA for the company on the basis of both the characteristics of the selected ODA tool and the needs of the Virgin Media.
Methods: Survey Research
The study employs quantitative method for data collection. The literature on research methodology reveals that the survey research is the best tool to implement the quantitative methodology in the above discussed research design (Bender et al., 1998; Dillman, 2000; Lakshman, Sinha, Biswas, et al., 2000). The paper employs both primary and secondary research for data collection. The survey research method is the technique selected for conducting the primary research. This method of primary research presupposes the distribution of the specifically designed questionnaires among all the research participants, collecting their answers, and analyzing them with regard to the research topic. The primary objective of the survey questionnaire is to examine the ODA influence on decision making in the process of online shopping.
To ensure that the survey questions reach the target population, the researcher will inform the participants about the importance of their help and seek the official consent of every single person to answer the questionnaire items. To help the participants understand the requirements of the questionnaires, the researcher will provide participants with images of the ODA tools presented for their analysis (See Appendices section, Fig. 1, 2, 3, 4, 5, and 6). To ensure the timely data collection, the researcher will employ the Tailored Designed Method (Dillman, 2000) to inform the respondents to return their survey. Dillman (2000) advises that the researcher can call the participants on the telephone to stress one again how important the survey is and to request the timely questionnaire returning. For effective data collection through survey method, the following survey instrument has been developed.
Since the paper uses the survey research to collect the primary data, it is necessary to examine the developed survey instrument. There are four questions in the questionnaire structured to make the survey as comprehensible and clear for the participants as possible. The special feature of the discussed questionnaire is that it presupposes that the answers are chosenf rom the given list. This limits the choice of the paritcipants but ensures that the answers will provide the needed data to the fullest extent (Lakshman, Sinha, Biswas, et al., 2000). Thus, the survey instrument first of all instructs the participants in respect of the procedure of ansswring the survey question (for more detail on the questionnaire, see Appendices section):
Instructions in filling the survey
“The objective of this survey is to find out the methods you use in making rational decision in product selection during online shopping. In this survey, there is no ‘right‘ or ‘wrong‘ answer, and you can provide any answer based on your opinion. This is NOT a test; it is just a survey to sample your opinion.
For identification purposes, you provide your name that will further be coded to avoid leaks of private data. All the information you supply will be kept strictly confidential and will not be revealed to any third party. Please, before choosing an option in the survey, read the survey question very carefully, and choose the best option to reflect your opinion. In all questions, you should shade your choice.”
Such an approach to structuring the work with the survey participants is expected to provide objectivity of data, as there will be no pressure on the participants, and they will be free to select the option they prefer due to their own reasons.
Since the survey project involves human participants, it carefully considers any ethical issues. To avoid legal disputes, the survey research fully conforms to The Data Protection Act 1998, various ethical codes of conducting research, and associated legislation. As stated in the previous section, all participants are informed beforehand about the aims and objectives of the study. The researcher works for the participants to be fully satisfied with the outcome. The participants prove their official consent for cooperation by signing the questionnaires and placing the date of completion on their last pages. The findings of this study are made readily available for all participants for verification to guarantee that the data collected are only used for research purposes.
Apart from primary research, the study also employs the means of data collection from secondary sources. The purpose of this step is to review the previous studies in order to enhance greater understanding of different types of ODA tools that merchants use to enhance decision-making process for online shoppers. The secondary research also highlights advantages and disadvantages of different types of ODA. Collection of data from secondary sources is carried out through electronic databases that contain large numbers of journal articles, e-books, newspaper articles, and reports.
The electronic databases such as Science Direct, EBSCO’s databases, Emerald database, research articles from universities libraries, and database of Social Science Resources Network (SSRN) are used for the secondary research purposes. To ensure the relevance of secondary data, the research employs specific keywords to search the journal articles from database. The keywords used are Online Decision Aid, online merchant, information processing, Virgin Media, Electronic Decision Aids, and online shoppers. Scholars attribute several advantages to secondary research as an inexpensive data collection tool:
“The primary advantage of secondary research is its potential for resource savings and cost- effectiveness. Often, the database provides the investigator with access to information that took months or even longer to collect. Hiring and paying personnel to collect data are eliminated, and with adequate computer storage and memory, the investigator can reduce the number of additional support persons required. Secondary research also circumvents other data collection challenges such as finding appropriate participants and obtaining large enough sample sizes to yield convincing results” (Castle, 2003, p. 3)
Thus, the importance of secondary research is emphasized, and the current research also makes use of such an obviously relevant research method. The following case study is the third data collection method. It can also be referred to as a secondary research tool, as it presents information on the use of ODA by the two reputable companies, Senso Solutions and Apple. Such a case study is expected to serve as a practical illustration of the effects ODA tools have upon the companies’ functioning. Accordingly, the following case study might ground the selection of the recommended ODA for the Virgin Media.
The sample population is the students who enjoyed online shopping. The number of people in the sample is 80, among whom 50% are males and 50% are females. The two major goals of the researcher in regard to this population were to select a small sample and to distribute the questionnaires among all its participants. The essential purpose of selecting the small sample population is to have control over survey data collection.
It is essential to notice that one of the major objectives of this study is explore different type of ODA and recommend the best ODA tool for Virgin Media. To achieve this objective, the paper employs a case study to collect data about two other reputable companies, Senso Solutions and Apple. Such a case study helps analyzing the experiences of these companies in ODA use, which should be helpful in determining the best ODA solution to improve Virgin Media customers’ online decision making process.
Senso Solutions is a Finland-based company that deals in numerous countries around the world and has its branches in Europe, North and South Americas, and Asia. The major area of activity of Senso Solutions is the development and sale of the most updated computerized solutions for the businesses of all directions. In particular, Senso Solutions offers a wide range of ideas, solutions, and products for the modernization of stores and shops (Senso, 2010). The company develops at the fast pace, and the great role in this development is played by the notion of Online Decision Aids. Obviously, the company that specializes in online computerized technology attributes much importance to its online environment and takes care of its online consumers with the help of ODA tools (Senso, 2010).
In particular, the web site of Senso Solutions offers a wide range of options for the customers and potential online shoppers. If considered from the point of view of the five ODA tools selected for the analysis in this paper, the ODA used by Senso Solutions can be characterized as the combination of Recommendation Agent and Consumer Review. The former allows consumers to get the most updated information about every product offered by Senso accompanied by recommendations regarding potential pros and cons of this product for every particular context. The Consumer Review helps every online shopper to learn all the specifications of the product he/she plans to select. Such ODA tools improved the Senso website, as consumers have now a wider set of options and can learn literally everything about every single product.
Apple, the worldwide giant of the modern technology and computerized products, also makes use of ODA tools at its website. Understanding that the modern media market is permanently updated, Apple resorts to the more sophisticated combinations of ODA tools it offers to its online consumers (Apple, 2010). In terms applicable to this paper’s topic, the website of Apple helps the company’s customers to be oriented in the variety of Apple’s products using the combination of Comparative Matrix, Recommendation Agent, and Consumer Review (Apple, 2010).
First of all, the Apple’s website offers comprehensive matrices with images and prices of its various goods categorized according to their functions and specifications. So, a consumer might select a category and than face a matrix where similar products in this category are compared according to their functions and prices. Second, every consumer at the website of Apple can obtain recommendations regarding the products fitting his/her needs the most. Finally, for the convenience of all consumers, every single suggestion at this website is accompanied by a brief review that allows shoppers to ground their choices on primary data about Apple products (Apple, 2010).
So, it is obvious that both Senso Solutions and Apple make use of ODA tools at their website, and those tools improve the quality of the consumer services at their web pages. As the experience of these two reputable companies shows, ODA can present considerable developmental impacts to any modern business. Drawing from this, the need to recommend the most suitable and effective ODA tool for Virgin Media is fully grounded and relevant.
This chapter analyzes the methodology of data collection, as well as the basics of the current research design and ethical considerations associated with the survey research. The chapter also identifies the sample population of the study and provides the case study potentially helpful for achieving the main objective of the whole research. The results of the use of the above discussed data collection methods are presented in the next chapter.
Data Analysis and Findings
The purpose of this chapter is to analyze the data collected via the survey research. The data analysis is essential in order to ensure the validity and reliability of the research. In addition, the analysis is essential to check data accuracy and to rectify all errors that might have been observed in the questionnaires. Finally, this chapter presents the findings of the research, relates them to the previous literature, and makes recommendations of the best ODA tool for Virgin Media.
Data analysis procedure
This section describes the procedure of gathering the answered questionnaires and synthesizing the information from the in order to achieve research objectives and answer the research questions. As discussed in Chapter 3, the target population includes university students experienced in online shopping. 80 survey questionnaires were distributed among the research participants. After the distribution, the participants had seven days to answer all the questions rationally and properly. Using the ideas by Trochim and Donnelly (2007), the researcher tried to eliminate any issues regarding survey answering and made sure that the participants had enough time to think over the answers and provide objective data.
To ensure that this study adheres to Trochim and Donnelly (2007) criteria, the researcher started collecting the questionnaires to check them for any mistakes regarding the answering procedure. However, among the questionnaires received from students, only 31 students filled all the survey without errors. Errors were observed in 38 questionnaires, and 11 students did not return their questionnaires due to certain reasons. Out of 38 questionnaires with errors, 14 students did not complete all the survey questions, 9 students shaded options twice in the same survey question, while the remaining 15 students did not provide their names.
So, to ensure that all questionnaires are properly filled, the researcher returned them to the students and explained the importance of the work once again. Within 24 hours, the researcher collected the remaining 38 questionnaires, among which only 19 were properly filled. From the rest of 18 questionnaires, in 10 of them biased answers were observed, and in 8 not all questions were filled. Thus, the total number of questionnaires collected was 50.The screening of all the data collected provide the data accuracy and the enhance reliability, and data validity. After the data were available, the research moved on to the stage of the pilot study.
The researcher used pilot study to test the micro results of this survey research. The researcher chose 10% of sample population to check on what to expect in the overall findings. After testing the questionnaire with pilot study, it was obvious that there is the need to design questionnaires that would compare the effectiveness of different types of ODA tools identified in the study. Thus, after editing the questionnaire, the researcher was able to achieve the results similar to the ones of the pilot study in the overall research.
This section discusses the findings of the current research, mainly retrieved from the analysis of answers of survey research participants. Most importantly, the findings allow answering the above discussed research questions (see Chapter 1, Section 1.4) and make recommendations to Virgin Media regarding the ODA tools it should use.
Findings categorized by research question
The answers derived from the collected survey questionnaires allowed the researcher to answer the above mentioned research questions in the following manner.
What are the familiarity levels of the respondents with the further discussed 5 ODAs?
The questionnaire aimed at answering this research question by collecting answers on the question-statement “From the pictorial description, one or more of the listed decision-aids are familiar to you”. The analyzed findings prove that the 100% of the survey participants were familiar with one or more of the suggested ODA tools. At the same time, the levels of familiarity and the percentage of participants familiar with this or that tool were different. The Pearson Correlation tool allowed the researcher to better analyze the relations of every single point in the questionnaire to all others.
Accordingly, the results from the Pearson Correlation tool (for more detail, see Appendices section) reveal the fact that, at least for the studied population, the largest percentage of the participants strongly agree that they are familiar with the Comparison Matrix as an ODA tool (56%), while the QBDA is the least familiar tool as only 20% of the participants strongly agree that they are familiar with it. As for the correlation values, the Consumer Review correlates positively as moderate with both Recommendation Agents and QBDA (the values are.575 and.514 respectively). This means that these three ODA tools are compatible with each other and can be implemented as ODA tools on a merchant’s website together. The correlation of Consumer Review with Recommendation Agents is stronger as it is closer in its meaning to the moderately strong on the Pearson scale (for more detail, see Appendices section). However, the Comparison Matrix, having the highest percentage of “strongly agree answers” correlates positively as moderate with both Consumer Review and Recommendation Agents, which evidences that either two of these tools, or all three, can be used by Virgin Media website. This fact also evidences that the use of either of these tools will improve public image of the company. Interestingly, neither of the tools is negatively correlated to others, which evidences that the participants did not have negative experience with them as far as mere familiarity is concerned.
Which extent of the perceived ease of use is attributed to each of the selected 5 ODAs?
To answer the second research question, the specifically designed questionnaire implemented the question-statement “While choosing mobile phones online, it is easy using one or more of the following ODA’s”. Interestingly, among the 50 participants who answers were analyzed, there was no such an ODA, the ease of use of which was totally rejected by the participants. At the same time, the percentage of the answers fluctuated from 8% who strongly agreed on the ease of use of Maxim to 50% of those who agreed that it was easy using the Comparison Matrix while making online product choices.
The results from the Pearson Correlation model for this question were not as uniform and positive as for the first question. In particular, the table of the correlation values reveals that there are both positively and negatively correlated ODA tools. Even more, almost all the tools correlate negatively as weak with each other. Such a correlation reveals the fact that the survey participants do not see the possibility of using those tools together on the website. Only Consumer Review and QBDA correlate positively as moderate, which is represented by the values of.415, while Comparison Matrix and Consumer Review correlate positively as weak (.1), which also proves that they are compatible, although to a smaller extent than CR and OBDA.
At the same time, the fact that the positively correlated ODA tools include Comparison Matrix, Consumer Review, and QBDA, similarly to the correlations regarding the issue of familiarity, is one of the first pieces of evidence to support the idea that these tools are the most effective ones in the online environment. This fact also allows the researcher to start developing the recommendation for Virgin Media. Thus, two of the four questionnaire points reveal that Comparison Matrix, Consumer Review, and QBDA correlate positively in most case, and the preliminary recommendation for Virgin Media is to use one of those tools or combine them all. However, further consideration of the survey research findings is essential for stronger recommendations.
How useful are the selected 5 ODAs as perceived by the survey respondents?
For answering the third research question, the following question-statement was developed by the researcher and placed into the questionnaire “From your experience, you regard the listed decision-aids as very useful/ informative while selecting mobile phones online”. The similar scale of possible answers was presented after the question, and every respondent had to mark the only answer he/she considered the most adequate based on his/her personal experiences and perceptions. As far as the percentage of answers is concerned, the largest number of “strongly agree” points was observed in the section of the Comparison Matrix (46%), while the smallest number of such answers was obtained from the analysis of Recommendations Agents (16%).
At the same time, the correlations derived again using the Pearson Correlation model reveal the findings considerably similar to the ones of the first two questions. In particular, there were several weak negative correlations, but the three tools correlated positively were Comparison Matrix, Consumer Review, and QBDA. The first two of them were correlated positively as moderately weak (.292), while Consumer Review and QBDA again displayed positive correlation as moderately weak (.413), which is another piece of evidence to the idea that one of these three ODA tools, or all, are the most acceptable alternatives for improving the online decision making of Virgin Media customers.
As well, this positive correlation allows assuming that the points that consumers appreciate in online decision aids are the access to the full consumer-targeted information about the product; the instant access to all product specification immediately after the query is submitted; and the option to compare goods in relation to functions and prices. Moreover, if the survey participants consider these points to be crucial, they obviously lack them in their online shopping activities. So, apart from improving customer services, Virgin Media might gain competitive advantages if it properly adopts the three recommended ODA tools, i. e. Comparison Matrix, Consumer Review, and QBDA.
What are the effectiveness levels of the selected 5 ODAs for shopping decisions as perceived by the survey respondents?
The fourth research question is presented above. To answer it, the questionnaire used the following question-statement “One or more of the ODA tool is/are more effective in influencing your decision to shop for mobile phone online”. Again, as far as the percentage relations are concerned, the largest amount of people that strongly agreed that this or that ODA was effective in their shopping decisions was observed in the frequency table for the Comparison Matrix as 54% of the respondents stated that this tool facilitated their decision to shop online. This fact provides another proof of the idea that the full information about the product and the possibility of its comparison with other suggestion (in both functioning specifications and price) are the most essential features that consumers look for in the Online Decision Aids at the moment.
At the same time, the Pearson Correlation table provides the data that one could have expected after analyzing the first three questions. So, according to the table (for more detail on it, see Appendices section), Consumer Review was the ODA tool that correlated positively as moderately weak with both Comparison Matrix and QBDA. Their correlation values equaled.374 and.259 respectively. The positive correlations of these three tools prove that they are compatible and potentially effective for online shopping websites. Moreover, this is another piece of evidence that the modern online shoppers lack these qualities in the Online Decision Aids they have available.
Further on, if the findings from the case study regarding the ODA tools used by Senso and Apple are integrated into the analysis of all the four questionnaire points, it is possible to see that Comparison Matrix and Consumer Review are the two most popular and effective tools used nowadays. If accompanied by the QBDA, which customers suppose to be efficient, these three ODA tools can form a set of powerful improvement means for Virgin Media in its attempts to provide better online decision aids to its online consumers.
Accordingly, the major outcome of the questionnaire findings analysis for all the four questions is that the Comparison Matrix, Consumer Review, and QBDA are the three ODA tools that can be recommended to Virgin Media. The company thus might consider an option of implementing all the three ODA tools in its online environment for the better results. However, if only one alternative of three should be identified, it is the Comparison Matrix that should be that alternative. There are four major reasons for such a choice:
- The survey findings analysis proves that the Comparison Matrix is the ODA tool in respect of which the largest percentage of “strongly agree” answers were given for all the four questions;
- Comparison Matrix obviously provides customers with all the necessary information they require for rational decision making during online shopping;
- The experience of such companies as Senso Solutions and Apple proves the efficiency of Comparison Matrix for the purposes of online decision making improvements;
- Scholars like Kerner (2004), Bustos (2008), and Pereira (2001) consider Comparison Matrix to the most effective and, most importantly, the most customer-oriented ODA tool of all being used nowadays.
This chapter presents the findings of the whole research, including responses from the survey, the case study, and analysis of the scholarly opinions to support tool selection for Virgin Media. The findings reveal that there are three acceptable tools, but the Comparison Matrix is the most appropriate among them, although it is compatible with CR and QBDA. Such a decision is made on the basis of the above research findings and can be supported by the correlations that various tools displayed according to the Pearson Correlations model. The latter is considered to be an effective correlation measuring means but is underused in scholarly sources studying ODA tools for online shopping (Pfeiffer, Riedl, and Rothlauf, 2008; Bichler, Kalagnanam, and Lee, 2002; Lee, and Kwon, 2002). Therefore, the current research fills in this gap and, most importantly, its findings retrieved from other sources correspond to the data from the Pearson Correlations model. According to all source, Comparison Matrix is the best alternative ODA tool that can be recommended to Virgin Media.
The chapter explains how the recommendations to Virgin Media were arrived to using the special decision support software.
Decision Support Tool
The dissertation makes use of SPSS software for the design and for the support of the decision tool for Virgin Media. In particular, the SPSS package is used to perform necessary calculation in this dissertation and to present the graphs reflecting the survey findings. Several tactics are used to process the information from the survey. For better clarity and visualisation, SPSS presents outcomes of the survey in percentages, tables, and graphs form.
Virgin Media decision tool
Another part of the decision making tool for Virgin Media comes from the survey questions outcomes, and the results are presented in percentages. Typically, the percentages of the responses from the Strongly Agree to Agree that exceed 60% are considered valid to make decision support (for more detail, see Appendices section). Thus, in every questionnaire point there are such answers that can be validly used to make decisions and that affect the option to recommend Comparison Matrix for Virgin Media.
The design for the decision support tool has been provided in this chapter, together with the description of how it has been able to enhance decision making process for Virgin Media. The following chapter is the comprehensive account on the evaluation procedures for the selected tool.
The chapter provides the evaluation of decision support tool developed, with special emphasis put on its noticeable strengths and weakness.
A major method to evaluate the tool is to test it with the end user, which is Virgin Media. Accordingly, this tool could only be tested only with the end user and with no other organisation due to the confidentiality of the documents involved.
The willingness of the students to take part in the survey is very important. The success of the whole project depends on this willingness as well as on the readiness of respondents to provide actual and reliable information. Although using a small group of participants, the study seemingly did not suffer lack of information. Since the survey questions were standardised and structured to allow the participants to choose an answer supplied by the researcher, it was almost impossible to identify any point in the survey that the participants might have misinterpreted.
The study has used the questions with provided sets of possible answers. Although open-ended questions can generate large amounts of data (Lee and Kwon, 2008), this study did not make use of such questions because it would take lots of time to analyse and process the answers. To overcome similar problem, the researcher avoided asking too many questions and limited the questionnaire to the most important ones. To help the participants understand what the ODA was and to help them in answering the survey questions, the study provided samples a pictorial view of ODA (See Appendices section).
Decision to choose Comparison Matrix for Virgin Media
The decision to suggest Comparison Matrix as the best ODA tool for Virgin Media is based on the survey results and analysis of literature. However, other ODA tools, like QBDA and CR are also good for Virgin Media. At the same time, the results of this research can provide only a recommendation for Virgin Media, while the actual decision completely depends on the consideration of the company’s executive management.
The chapter evaluates the survey carried out, as well as the suggestion for Virgin Media to implement the Comparison Matrix ODA tool in the company’s web environment. However, further research is needed to improve the accuracy of this decision-making and, probably, provide new insights in this topic.
Conclusion and Discussion
Summary of the dissertation
This section provides the summary of the dissertation. Chapter 1 presents the introduction to the dissertation, and includes the research aims and objectives. This chapter also formulates the research problems that the study attempts to address. Finally, the chapter provides the research questions, and the design of the dissertation.
Chapter 2 reviews previous literature related to the study. It is essential to examine the contribution of the previous scholars, and to fill in the gaps observed in scholarly research regarding ODA. The paper reviews several academic studies to identify different types of ODA and their advantages and disadvantages. It also reviews the factors that affect human decision-making with respect to products’ selection in the online environment.
Chapter 3 provides research methodology. This chapter examines the methods of data collection, research design, and data analysis. The quantitative technique is used as the research method. Finally, this chapter discussed the case study used for the research. The paper used Senso Solutions and Apple as case studies to examine the effectiveness of ODA tool that the online merchants use to enhance decision making during products’ selection.
Chapter 4 includes the data analysis and the survey findings. The analysis is carried out for the reliability and validity of the study. There is the analysis of the primary and secondary data. The findings answer the research questions and make it possible to recommend a most suitable ODA tool for Virgin Media. The paper uses Virgin Media as its main focus, but case studies of Senso Solutions and Apple are used to examine the effectiveness of ODA tools that the online merchants use to enhance decision making during products’ selection by their customers.
Chapter 5 provides analysis and findings for the paper regarding the study’s contribution of new knowledge to the academic community. Chapter 5 also discusses the design tool used in the dissertation. Chapter 6 evaluates the survey research as method of data collection and identifies the need for further research.
Meanwhile, the study provides several contributions to the community. It is essential to realise that the study enhances the body of knowledge regarding the effectiveness of ODA tools in several ways. First, the research bridges the gap created by lack of research on the selection of the most suitable ODA for online merchants. The paper also provides the greater understanding on how online merchants make use of Online Decision Aids to enhance customers’ decision-making on the products they intend to purchase in the online environment. In addition, the study explores different types of ODA and recommends the most suitable ODA tool for Virgin Media to be used to enhance customers’ decision-making. The data collected through the case study enhance a greater understanding of how some online merchants like Senso Solutions and Apple, make use of effective ODA tools.
Furthermore, the paper provides greater understanding for the academic community regarding the usefulness of ODA to enhance customer’s decision-making. Finally, the dissertation enriches the knowledge of individuals, business organisations, and the general community on the effectiveness of ODA in enhancing customer’s decision-making. For this purposes, Pearson Correlation Model is used, which has never been done before by scholars in this particular topic. This model is used because it provides considerably accurate data regarding the potential compatibility of the studied ODA tools for use on merchants’ websites. At the same time, Pearson Correlation Model has its limitations on the whole and in regard to this study in particular. First, this model gives data on potential compatibility and cannot provide guarantee of their actual joint work efficiency. Second, the small sample of the survey does not allow generalizing data on larger scopes, so this means that additional research is needed.
Future research and development
There is also the need for future research to develop sophisticated online decision aids for online shoppers in making rational decisions. In addition, it is necessary to design an ODA tool that is equipped with design tools. Typically, with present ODA tools, it has been noted that consumers may be overloaded with information that can prevent them from making rational choices of products, and there is the need to design an ODA tool to prevent this. Future research is also needed on how ODA tools attract online shoppers, as far as typically a friendly aid tool will retain the existing customers (Pfeiffer, Riedl, and Rothlauf, 2008; Bichler, Kalagnanam, and Lee, 2002; Lee, and Kwon, 2002).
This dissertation enhances my understanding of Online Decision Aids. Although, before exploring this study, I have had some idea on ODA tools, after finishing this dissertation, I am familiar with several other aspects of ODA I did not understand before. Thus, completion of this study has enriched my knowledge of several aspects of ODA tool I did not know before starting this study. Additionally, with the new knowledge I have acquired from Online Decision Tools study, I am able to develop new knowledge in this area. Finally, this dissertation helped me to learn how to overcome various academic difficulties. In particular, I have faced the moment when I could not cope with the whole task according to the deadline, but the new schedule I used allowed me to avoid this pitfall of dissertation writing. So, I can conclude that the work on this dissertation paper has helped me improve not only my academic knowledge, but also the overall ability to cope with various difficulties a person might face while working on a certain task.
List of References
Alba, J, Lynch, B. Weitz, C. et al (1997). ‘Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in electronic Marketplaces’. Journal of Marketing. 61 (3): 38-53.
Apple. (2010) Apple Stores. Apple Company. [online] Web.
Becwati, N.N. & Xia, L. (2003). ‘Do Computers Sweat? The Impact of Perceived Effort of Online Decision Aids on Consumers’ Satisfaction With the Decision Process’. Journal of Consumer Psychology 13. (1-2):139-148).
Bender et al (1998), ‘Integrated Use of Qualitative and Quantitative Methods to Elicit Women’s Differential Knowledge of Breastfeeding and Lactational Amenorrhea in Peri-Urban Bolivia’. Journal of Health & Population in developing countries. 1(1): 68-84.
Bichler, M. Kalagnanam,J.R. & Lee, S.L. (2002). ‘ECCO – Automated Evaluation of Configurable Offerings’. IBM Research Report. RC22288 (W0201-004).
Brynjolfsson, E. & Smith, M. (2001), ‘Consumer Decision-Making at a Internet Shopbot: Brand Still Matters.’ Journal of Industrial Economics. 49 (4):41-558.
Bustos, L. (2008).’ Product Selection and Discovery: What You Can Learn From the Telco Industry’.Get Elatic.UK.
Castle, J.E.(2003). ‘Maximizing research opportunities: secondary data analysis (Research Corner)’. Journal of Neuroscience Nursing. 35(5):287-290.
Degeratu, A.Rangaswamy,A.& Wu, J.(2000), ‘Consumer Choice Behavior in Online and Traditional Supermarkets: The Effects of Brand Name, Price, and Other Search Attributes’. International Journal of Research in Marketing. 17: 55-78.
Dillman, D. (2000). ‘Mail Internet Surveys Tailored Design Method’. Wiley.UK.
Farlex (2008). ‘Research, Free Medical dictionary’, Web.
Garnett, R. (2004). ‘Integrating a Intelligent Decision Aid into a Case-based Learning Environment for Improving Dynamic Decision Making. In L. Cantoni & C.
Haubl, G. & Trifts, V. (2000). ‘Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids.’ Marketing Science INFORMS. 19(1): 4–21.
Hansson, S.O. (2005). Decision Theory A Brief Introduction, Department of Philosophy and the History of Technology Royal Institute of Technology (KTH) Stockholm.
Kerner, S. (2004). ‘Americans weigh in online rating: Rating is a popular pastime’. ClickZ Demographic Statiscs.
Lakshman, M. Sinha.L. & Biswas, M.et al (2000). ‘Quantitative Vs qualitative research methods’. Indian Journal of Pediatrics. 67(5): 369-377.
Lee, K.C. & Kwon, S. (2008). ‘Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: A causal map approach Kun Chang Lee, Soonjae Kwon’. Expert Systems with Applications 35: 1567–1574.
Luis, S. (2007). ‘Asymmetries in information processing in a decision theory Framework’. MPRA Munich Personal RePEc Archive. Portugal.
Maes, P. (1994).’Agents that Reduce Work and Information Overload,” Association for Computing Machinery?’ Communcations of the ACM. 37 (7): 30-40.
McLoughlin’ (Eds.). Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2004: pp. 3867-3872. Chesapeake, VA: AACE.
Merz, M.& Chen, Q. (2006). ‘Consumers’ Internet and Internet Consumers: Exploring Internet-based Electronic Decision Aids’. Advances in Consumer Research 33: 301.
Michael, M.& Qimei, C. (2006) ‘Electronic Decision Aids,Advances in Consumer Research’. 33(1): 301-302.
Miller, G.A. (2005). ‘Information Processing Theory’. York University, UK.
Payne, J.W. Bettman, J.R. Johnson, E.J. (1993). ‘The adaptive Decision Maker’. Cambridge University Press. Cambridge England.
Pereira, R.E.(1999). ‘Factors influencing consumer perceptions of Web-based decision support systems’. MCB UP Ltd, ) : 157 – 181.
Pereira, R. (2001), ‘Optimizing Human-Computer Interaction for the Electronic Commerce Environment’. Working Paper.
Peter, T. & Izak, B. (1994). The Influence of Decision Aids on Choice Strategies: An Experimental Analysis of the Role of Cognitive Effort, Organizational Behavior and Human Decision Processes. 60(1): 36-74
Pfeiffer,J. Riedl,R. & Rothlauf, F. (2008). ‘On the Relationship between Interactive Decision Aids and Decision Strategies: A Theoretical Analysis’. Working Papers 3 in Information Systems and Business Administration.
Punj, G. & Rapp, A.(2004). ‘INFLUENCE OF ELECTRONIC DECISION AIDS ON CONSUMER SHOPPING IN ONLINE STORES’. University of Connecticut,USA.
Senso. (2010) Senso Overview. Senso Solutions. [online] Web.
Sharkey, U. Acton, T.& Conboy,K. 2009). ‘Modelling the Effects of Decision Tools in Online Shopping’. AIS Electronic Library. USA.
Stávková, J. Stejskal,L. Toufarová, Z. (2008). ‘Factors influencing consumer Behaviour’. Agric. Econ. – Czech. 54 (6): 276–284.
Stewart, D.O & DeMarco, J.P (2005). ‘An economic theory of patient decision-Making’. Journal of Bioethical Inquiry, 2, ( 3): 153-164.
Sudweek, F. & Romm, C.T. (1999). ‘Doing business on the Internet: opportunities and pitfalls’. Springer. USA.
Trochim, W. Donnelly, J.P.(2007). ‘The Research Methods Knowledge Base (3rd ed)’. Atomic Dog Publishing, USA.
Wan, Y. Menon, S. & Ramaprasad, A. (2009). ‘The Paradoxical Nature of Electronic Decision Aids on Comparison-Shopping: The Experiments and Analysis’. Journal of Theoretical and Applied Electronic Commerce Research. 4(3):80-96.
Wietsma, R.T.A. Ricci, F. (2005). ‘Product Reviews in Mobile Decision Aid Systems’. eCommerce and Tourism Research Laboratory ITCirst. Italy.
Appendix 1: Survey Questions
Online Decision Aid Effectiveness on a Mobile Phone Web Environment Survey
Instructions in filling the survey
The objective of this survey is to find out the methods you use in making rational decision when making products’ selection in an online shopping.
In this survey, there is no ‘right‘ or ‘wrong‘ answer, and your chosen answer is based on your opinion. This survey is not a test; it is just a survey to sample your opinion.
All the information you supply will be kept strictly confidential and your information will not be shown to anyone or a third party. Please, before choosing an option in the survey, read the survey question very carefully, and provide an option that most reflect your opinion.
In all questions, you should shade a best choice that fits your opinion.
Section 1: General Question on Online Decision Aids
Instruction: Please mark “” in the box that best represent your view E.g. If you either strongly agree, agree, neither agree or disagree, disagree or strongly disagree with the questions please mark as seen below in the sample question.
|Strongly Disagree||Disagree||Neither agree or disagree||Agree||Strongly Agree|
|From your experience, you regard the listed decision-aids as the best for selecting mobile phones.||Comparison Matrix|||
|Strongly Disagree||Disagree||Neither agree or disagree||Agree||Strongly Agree|
|From the pictorial description, one or more of the listed decision-aids are familiar to you.||Comparison Matrix|
|While choosing mobile phones online, it is easy using one or more of the following ODA’s||Comparison Matrix|
|Strongly Disagree||Disagree||Neither agree or disagree||Agree||Strongly Agree|
|From your experience, you regard the listed decision-aids as very useful/ informative while selecting mobile phones online.||Comparison Matrix|
|One or more of the ODA tool is/are more effective in influencing your decision to shop for mobile phone online.||Comparison Matrix|
Section 2: Demographics
Instruction: Please mark “” in the box that best represent your view.
| ||What is your gender?||Male Female|
| ||How old are you?||18-30 31-40 41+|
| ||Occupation||Student Instructor|
| ||How long have you been shopping online?||1yr 2yrs 3yrs 4yrs +4yrs|
Thank you, for your contribution to this study.
Appendix 2: ODA Pictures
QBDA help consumers to check the attributes of products.
Consumer matrix allows consumers to compare different products before making a selection.
This ODA recommends a suitable product that satisfy consumer’s taste. Source: (Bustos, 2008).
This helps consumers to review different products before making a selection.
Consumer able to select products based on their past experiences.
Appendix 3: Pearson Correlometer
- ODAs that fall between this region, possesses positive Association, meaning the two variables involved can work together on a merchant web environment because of the strength and direction of the relationship between the two variables. Note that the higher the value, the stronger the association
- Although variables that falls under this region are regarded to be negative they can still work together, but not as strong as those on the positive region.
- From Point A and B it can be said that variables X and Y that fall within this region can work together, but as mentioned already, the more moves towards the positive side form the left to the right, the higher and better the association
- No Association is involved in this region
Appendix 4: Pearson Correlations Models
Fig. 1: Familiarity
|Comparison Matrix||Consumer Reviews||QBDA||Recommendation Agents||Maxim|
|Comparison Matrix||Pearson Correlation||1||.464(**)||.288(*)||.359(*)||.087|
|Consumer Reviews||Pearson Correlation||.464(**)||1||.514(**)||.575(**)||.255|
|Recommendation Agents||Pearson Correlation||.359(*)||.575(**)||.480(**)||1||.205|
Fig. 2: Ease of Use
|Comparison Matrix||Consumer Reviews||QBDA||Recommendation Agents||Maxim|
|Comparison Matrix||Pearson Correlation||1||-.100||-.028||-.248||-.300(*)|
|Consumer Reviews||Pearson Correlation||-.100||1||.415(**)||-.084||-.087|
|Recommendation Agents||Pearson Correlation||-.248||-.084||.099||1||.232|
Fig. 3: Usefulness
|Comparison Matrix||Consumer Reviews||QBDA||Recommendation Agents||Maxim|
|Comparison Matrix||Pearson Correlation||1||.292(*)||.177||-.002||-.336(*)|
|Consumer Reviews||Pearson Correlation||.292(*)||1||.413(**)||-.202||.114|
|Recommendation Agents||Pearson Correlation||-.002||-.202||-.062||1||.100|
Fig. 4: Effectiveness
|Comparison Matrix||Consumer Reviews||QBDA||Recommendation Agents||Maxim|
|Comparison Matrix||Pearson Correlation||1|
|Consumer Reviews||Pearson Correlation||.374(**)||1|
|Recommendation Agents||Pearson Correlation||.071||-.120||.137||1|