In this chapter, a consumer purchase decision support system framework was built based on the following theories. First, consumer purchase decision-making process was studied to understand the decision-making stages. Second, social psychology was investigated to understand the characteristics a friend should have so as to be selected as a reference group member. Third, in order to identify the decision reference group social network analysis was used to analyse the members within social network. The complete theoretical foundation related to this research is shown in Figure 2.1.
Social Media Social Commerce
Information Technology Social Psychology
Consumer Behavior
Figure 2.1 Theoretical foundation
2.1 Consumer Purchase Decision Making
Human decision making process has been characterized as relatively sequential, and it becomes more complex with distributed source of information and the quantity of information available through networked sources. The way people make decisions varies considerably. Early research has focused on the way people are observed to make decisions and the way in which people should theoretically make decisions.
Depending on their methodological foundation, these models can be classified as:
descriptive, prescriptive or normative. A simple way of distinguishing between these modes of decision making is [26]:
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Descriptive: What people actually do;
Prescriptive: What people should and can do;
Normative: What people should do.
From a psychological perspective, it is necessary to examine individual decisions in the context of needs, preferences and values. From a normative perspective, the analysis of individual decisions is concerned with the logic of decision making and rationality. The rationality is ensured if the process of decision making is carried out systematically.
As purchasing decisions are often influenced by people who the consumer knows [44], this study focused on what consumers actually do when making purchasing decisions, that is, the descriptive mode was discussed. In consumer decision-making models, Utility theory proposes that consumers make decisions based on the expected outcomes of their decisions. However, in this model consumers are viewed as rational actors who were able to estimate the probabilistic outcomes [83]. As one might expect, consumers are typically not completely rational [69]. In contrast with this view, Simon was interested in the mechanics of the decision-making process [74], in that he considered how a decision maker evaluates all the consequences and compares them with each other. He proposed three principal phases:
Intelligence: think of the problem and find out what the alternatives to the given problem;
Design: determine all the possible consequences of these alternatives;
Choice: evaluate all the possible consequences.
In the consumer purchase decision-making process proposed by Kotler [45], the consumer passes through five stages: problem recognition, information search, evaluation and selection of alternatives, decision implementation, and post-purchase evaluation. This process is an extension of Simon’s model as three stages are included in Kotler’s model (see Figure 2.2).
Problem Recognition Information Search Evaluation of Alternatives Purchase Decision Post-purchase Behavior
Intelligence Design Choice
Kotler Simon
Figure 2.2 Mapping of Simon’s and Kotler’s decision process
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Once consumers perceive a need, they begin to search for information needed to make a purchase decision. The initial search effort often consists of an attempt to recall past experiences. If the internal search does not collect enough information, the external sources are consulted. Empirical study found that consumers relied more heavily on personal sources of information for decisions [29, 57]. After acquiring information during the information search stage, the consumer proceeds to alternative evaluation.
All the identified alternatives must be evaluated against some established criteria.
These criteria might base on past experiences or the comments of friends. At purchase decision stage, the consumer stops searching for and evaluating information, and make purchase decision. From a consumer-behavior perspective, the products that consumers select can be influenced by their reference groups [5, 17]. Reference groups are people to whom an individual looks as a basis for self-appraisal or as a source of personal standards, and they have important influence on the purchase behavior. As dual process theory suggests, reference groups can be divided into normative and informational [25].
The former one is based on the desire to conform to the expectations of others, and the later one is based on the acceptance of information from others [40]. Essentially, the personal source is individual’s online social network because it is constructed based on friends. Besides, online social network can be normative as well as reference group as friends can not only provide information but also influence each other.
2.2 Social Influence, Social Impact and Social Choice
In social network, social psychology, communication and information technology are essential in building meaningful relationships and influencing behavior. Today, the area of social commerce has been expanded to include the range of social network tools. Examples of these tools include consumer ratings and reviews, user recommendations and referrals, forums and communities, social network optimization and social applications. As the fast development of internet, together with the booming of online social network, it is much easier to collect information from personal sources.
Many consumers are getting used to make decisions based on comments collected from their own online social networks. While conventional decision support system has been extensively investigated, little specific mechanism on social commerce is developed.
For the purpose of helping consumer with making purchasing decision, it is desired to have proper social commerce support mechanism based on online social networks.
Moreover, as research suggests that consumers value and respect personal sources
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more than other sources [29, 57], it would be ideal to construct decision support groups from their online social networks.
In real-world decision-making process, human can experience emotional intensity and information overload that may affect their choices. Better decision support system should address these issues and assist human decision making by developing systems that integrate capabilities from human and computational intelligence. Social influence is the process by which individuals make real changes to their feelings and behavior as a result of interaction with others who are perceived to be similar, desirable, or expert [51, 68]. Social influence does not necessarily require face-to-face interaction, but is based on information about other people [70]. Social impact theory is widely cited in the research literature in social psychology, it provides a useful framework for understanding how a person is affected by social environment [61].
Social impact theory states that social influence is proportional to a multiplicative function of the strength, immediacy and number of sources [49]:
Strength: the importance of the reference group to the individual.
Immediacy: the closeness of the influencing group to the individual (in space and time) at the time of the influence attempt.
Number: how many people there are in the reference group.
Research on social influence demonstrates that one’s attitude and judgment tend to conform to those held by the majority of others [59]. Conformity can be due to either social pressure or one’s belief that the majority is likely to be correct [25]. When a large portion of a reference group holds a particular attitude, it is likely that the individual will adopt it as well [68]. Social choice theory is concerned with relationships between individuals' preferences and social choice [28, 73], and decision making and social choice theory are strong connected [4, 15]. The method of majority decisions has been widely discussed in the context of social choice theory. Voting-based procedures are entirely natural for some kinds of social choice problems [72]. Research on consumer decision involving multi-attribute options provides empirical evidence for use of the majority rule [71, 86]. A weighted voting system is one in which the preferences of some voters carry more weight than the preferences of other voters. However, in most of the social choice literature, all voters are treated equally. In fact, some voters are more important than others.
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2.3 Social Network Analysis
An online social network is a social structure made of people who are tied by one or more specific types of interdependency. Research on online social network has captured the effect of social influence on consumers’ purchase decisions across a variety of context [6, 38, 55]. Online social network analysis (SNA) refers to techniques used to analyse online social networks. Online social network can be analysed in node level and dyadic properties. The most popular metrics used are degree, betweenness and closeness centrality [31]. Degree centrality can be used to see if someone in an online social network is involved in large number of interactions.
Betweenness centrality is a metric to verify if an individual is an important node who lies on a high proportion of paths between others. A user with higher betweenness centrality is often considered as an opinion leader [31], and a higher closeness centrality indicates that a user is highly related to all others [64]. At the dyadic level the two properties are dyadic cohesion and equivalence [9, 10]. Dyadic cohesion describes to the social closeness of a pair of nodes. Equivalence refers to the extent to which pairs of nodes is similar.
Social impact theory suggests that social status, power and credibility can impact on decision [50]. Social status can be the proxy to estimate strength [62]. In-degree centrality, betweenness centrality [30, 31], and Bonacich power centrality can be used to measure social status [8, 30, 62]. Moreover, a member with high cognitive centrality would acquire pivotal power in a group and exert more influence on decision making [39]. In social impact theory, immediacy is used to describe group structure. Group structure can be treated as a pattern of immediacies between group members, and immediacies is the distances between individuals [61]. Furthermore, closeness may increase the power of social influence by making a source of influence more immediate [49, 61], hence closeness centrality can be used as the proxy of immediacy.
The studies of social network have examined a diverse set of properties, and these properties are classified as relational properties and structural properties [76].
Relational properties focus on the content of the relationship between network members and on the form of these relationships, while structural properties describe the way members fit together to form social networks. Human relationships are maintained, renewed, or deteriorate over time [77], but time factor is missing from the above properties.
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2.4 Multiple Regression Quadratic Assignment Procedure
Some data sets contain observations corresponding to pairs of entities (e.g., friends), and these data are not independent. The multiple regression quadratic assignment procedure (MRQAP) is commonly used in social network analysis. MRQAP is a nonparametric statistical algorithm regressing a dependent matrix on one or several independent matrixes. It is a standard technique to analyse social network data and to discover behavioral characteristics of friendship [85]. Therefore network regression measures are the most appropriate statistical method for testing them. However, these data are not independent and do not satisfy the assumptions of ordinary least squares regression, therefore requiring the use of the multiple regression quadratic assignment procedure (MRQAP) to test social network data [9].
MRQAP has been widely used in social network related research [27, 37, 48, 84].
However, in the development of social network applications, to my knowledge little effort has been devoted to test if the data collected from online social networks can be used to maintain online relationship. For example, some social network-based recommendation systems used interaction data such as comment, share, interests in common to measure online relationship [52, 53], but they are not empirically examined.
To make this research more solid, this method was introduced to test if the interaction data on online social network can really reflection social relation.
2.5 Design Rationale and Representation Schema
Due to the complexity of decision problem and communication process between decision-making group members, there is a strong need in formatting the solution design process to help members record, access and assess design rationale. Design rationale is used to provide information about why certain decisions were made. A clear design rationale provides a medium for communicating between decision group members. A design rationale is an important tool in arriving at the initial decision alternatives in the first place, and a representation is needed for capturing design rationale. A good representation schema is vital to enabling effective design and discuss. A representation schema explicitly documents the reasoning and argumentation occurring in design. It determines the methods used to capture and retrieve the design rationale.
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One design rationale representation schema known as Questions, Options and Criteria (QOC) developed by McLean et al. [54]. It focuses on three basic concepts indicated in its name. QOC represents the design space using three components:
Questions(Requirements): identify key issues for structuring the space of alternatives
Options(Alternatives): provide possible answers to the questions
Criteria: provide the bases for evaluating and choosing from among the options.
A design rationale presented by QOC schema is depicted in Figure 2.3.
Option
Link between Question and responding Option Link between Option and consequent Question Positive Assessment
Positive Assessment
Figure 2.3 QOC representation schema for design rationale
2.6 Fuzzy Analytic Hierarchy Process and Fuzzy Delphi
The Delphi method is a group decision making technique. Murry et al. integrated the concept of traditional Delphi method and fuzzy theory to improve the vagueness of the Delphi method [58]. Fuzzy Delphi is a good method for group decision to solve the fuzziness of common understanding of experts’ comments [60]. Analytic hierarchy process (AHP) is a structured, multi-criteria decision-making approach, and it is widely used for dealing with quantifiable and intangible criteria that can be applied to numerous areas such as decision theory [82]. Hsu and Chen [34] proposed a fuzzy similarity aggregation method, in which similarities between experts were collated and fuzzy numbers were assigned directly to each expert to determine the agreement degree between them. Fuzzy AHP (FAHP) translates the viewpoints of experts from definite values into fuzzy numbers and membership functions, and presents triangular fuzzy numbers in paired comparison of matrices to develop FAHP. Consequently, the
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comments of experts approach human thinking model, so as to achieve more reasonable evaluation criteria.
2.7 Chapter Summary
The primary objectives of this chapter were to provide a brief overview of related theories used in this study. By investigating Simon’s human decision process and Kotler’s consumer purchase decision-making process, their relationship was mapped.
Furthermore, the key role of reference group in purchasing behavior was also pinpointed. Social impact, social influence and social choice drawn from social psychology were reviewed to understand how people influence each other within social context. For the purpose of representing the relation between selection criteria and possible candidates, design rationale representation scheme was also covered in literature review. Last but not the least, information technologies used in this study were also discussed- namely, social network analysis, Fuzzy Delphi and Fuzzy AHP.
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