CHAPTER 3 RESEARCH METHODOLOGY - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/6106/8/08_chapter 3.pdf · CHAPTER 3 RESEARCH METHODOLOGY ... 2 Suitability for word of mouth

Research Methodology

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CHAPTER 3

RESEARCH METHODOLOGY

3.1. Introduction

In this chapter the theoretical framework and methodology adopted in the study has been

discussed. It outlines the various dimensions of the study and research objectives and the set of

methodologies adapted to accomplish those objectives. It explains in detail the pilot study

conducted for the identification of an appropriate online tool for the study after a comparative

analysis of three online tools. Netnography, which is a new qualitative, interpretive research

methodology, that uses internet optimized ethnographic research techniques to study the online

communities, has been applied, for the formulation of the research instrument. Further the

procedures followed for the collection of data and selection of the sample of online community

consumers and online community managers have been outlined. The tools and techniques

followed for analyzing the data for the study are also dealt in this section.

Using the concept of response modelling, four specific models have been developed during the

entire research study. These are-

(v) Consumer Trustworthiness Regression model using Netnography (CTR)

(vi) Co-creation model using INV based on Metcalf Law (C-INV)

(vii) Consumer Price Sensitivity model using K-means cluster analysis (CPS)

(viii) Business Online Community Credibility model (BOCC) using Linear

Programming

This chapter describes in detail the sample size, sampling techniques, tools of data collection and

tools of data analysis for the complete study.

3.1.1. Research Design

The research design is a framework for conducting the research. It involves the various steps

ranging from the definition of the information needed, specifying the measurement and scaling

procedures, constructing and pretesting the questionnaire, specifying the sampling process and

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size to developing a plan for data analysis. The research design for my study is primarily

exploratory and descriptive in nature. It is exploratory because at the first stage it involved the

provision of insights into the research topic and comprehension of the problem situation. It led

me to formulate the research problem, develop the objectives of the study, isolate the key

parameters of the study and plan the future course of action. The descriptive research is a type of

conclusive research. It attempts to describe systematically a situation, problem, phenomenon,

service or programme; it also describes the characteristics of the respondents and the degree of

association or relationship between the variables being studied. It helps to make specific

predictions. These two research designs were apt for the present study.

3.2. Conducting pilot studies for selection of an online tool and a comparative analysis of

three tools for CRM and CEM.

3.2.1. Pilot Study

A pilot study was carried out as part of my exploratory research. This was conducted with a

focus group of 30 participants well versed with traversing the internet. This focus group

comprised a set of practitioners from the industry, who were already using these online web

spaces for consumer engagement and participation. They were asked to map the tools of the

collaborative web, namely, Blogs, Wikis and Online communities with regard to their ability to

create and deliver organisation, brand and product related value to the customer.

A focus group is an interview conducted by a trained moderator in a nonstructured and natural

manner with a small group of respondents. The group is homogeneous in terms of demographic

and socio economic characteristics and the respondents are pre-screened. The participants must

have had adequate experience with the object or issue being discussed. The moderator leads the

discussion. The value of this technique lies in the unexpected findings often obtained from a free

flowing group discussion.

3.2.1.1. Evaluation Grid 1: Selection of an online tool

The focus group was asked to rate the above stated tools across a set of 12 parameters (Table

3.1) by using a Rating scale (1-3) with 1 representing the best tool delivering consumer value


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across a particular parameter. The Evaluation Grid 1 developed for the purpose is listed in

Annexure I.

A composite score was calculated for each respondent, for each tool. This was done by summing

up the scores for each participant across the entire parameters (Table 3.2). A mean consumer

perceived value score was calculated using SPSS 17.0 (Table 3.3).

Online communities were chosen because they create value for all their stakeholders including

the host members and any third parties such as advertisers. Increasing the perceived value of the

customers is the defining criteria in this respect. They can be used for value exploration in the

consumer cognitive space and enhancement of the relational equity of the firm. A community

can further serve as a value delivery mechanism to enhance the perceived value to the customers

by product promotions and enhancing customer cross selling and up selling, while also

stimulating greater content contribution and participation.

This study proceeds to outline individual features of online communities ranging from co-

presence, reciprocity and conviviality-these have well defined roles to play in building

relationships with customers and furthering the CRM goals of the organisation. Thus online

communities can become vital for organisations driving towards better profitability and

enhanced productivity by leveraging their internal and external customers.

Table 3.1: Construct of Consumer Perceived Value S.No. Parameter Source

1 Increasing customer perception of value in

organisation

(Zeithaml V., 1988)

2 Suitability for word of mouth marketing (Woerdl M. et al. 2008)

3 Enhancing customer participation (Alexander Ardichvili, Vaughn Page, Tim

Wentling, 2003)

4 Ease of navigation (Preece, J., 2001)

5 Ease of registration (Macaulay A. Linda ., 2007)

6 Ease of accessibility (Teo Hai H. et al. 2003)

7 Increasing customer knowledge of product (Sawhney M. et al. 2005)

8 Garnering traffic (Wayne G. Lutters, Mark S. Ackerman,

2003)

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9 Facilitating communication ( Rovai P.A., 2007)

10 Congeniality of environment (Johnson M.C., 2001)

11 Customer centricity (Wagner C. and Majchrzak A., 2007)

12 Help build a community of customers (Wiertz C. and Ruyter K., 2007)

Table 3.2: Composite Respondent scores across all parameters for three Online Tools

Respondent Blog Online

community

Wiki

1 21 29 22

2 22 26 24

3 24 30 18

4 24 26 22

5 22 31 19

6 22 31 19

7 24 26 21

8 24 26 22

9 23 28 21

10 26 27 19

11 22 28 22

12 24 29 19

13 22 28 22

14 22 28 22

15 22 24 26

16 22 28 22

17 23 27 22

18 21 19 32

19 26 26 20

20 22 27 23

21 24 28 20

22 29 26 17

23 23 24 25


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24 24 26 22

25 23 28 21

26 25 26 21

27 25 25 22

28 21 27 24

29 22 32 20

Table 3.3: Mean scores for Web 2.0 tools. Statistics

Blog Online

Community

Wiki

N Valid 29 29 29

Missing 0 0 0

Mean 23.2414 27.1034 21.6897

Online communities depicted the highest mean score amongst the three tools. They, hence,

appeared to be the tool of the collaborative web, best able to enhance a consumer’s perception of

value with regard to an organisation or brand, by virtue of ease of navigation and being the most

accessible to the consumer. The results of this study were further interpreted by a detailed study

of the features of online communities, through a literature review, to explore areas where they

could contribute to the CRM goals in organisations.

3.2.1.2. Evaluation Grid 2: Comparative analysis of three tools for CRM and CEM.

For the purpose, an evaluation grid was set up as a research instrument. Participants were asked

to rate the ability of the three online tools across each of the given parameters on a likert scale

(1-3).Thus the three touch points viz. Online Communities, Blogs, Wikis were rated with regard

to their ability to contribute to the value delivery and enhancement mechanism, which is the

backbone of Customer Experience Management. The Evaluation Grid 2 developed for the

purpose is listed in Annexure I.

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A comparative study of Blogs, Online Communities and Wikis was undertaken across the above

discussed parameters. A focus group of consumers was identified by using the online-intercept

technique. The criteria for participation revolved around the internet usage of the participants

across 5 online consumer communities. These five online consumer communities were selected

through random stratified sampling conducted on an online directory of consumer communities.

Age and membership of the online community were used as stratification variables.

Stratified sampling is a probability sampling technique that uses a two-step process to partition

the population into subpopulations or strata. The strata should be mutually exclusive and

collectively exhaustive. Elements are selected from each stratum by a random procedure. The

variables used to partition the population into strata are referred to as stratification variables.

Figure 3.1: CEM using Collaborative Web tools

The above stated online tools are excellent customer feedback data streams for companies to

monitor perceptions and trends. Based on the need to create, deliver and enhance customer

perceived value, we proceeded to study the ability of three customer touch points viz. Blogs,

Wikis and Online communities with regard to their ability to contribute to the value delivery and

enhancement mechanism, which is the backbone of Customer Experience Management.


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Figure 3.2: Customer Experience Management

3.2.2. Best practices in Customer Experience Management

3.2.2.1. Building Emotional Connection with Customers: Companies can improve

relationships with customers by participating in Online Communities. The literature review

shows in many ways that maintaining such relationships leads to loyalty. Customers who

participate in online discussions are motivated to repeat their purchases from the same company.

Customers will be loyal to these companies because they share the same ideology and views.

When companies take part in online forums, they are able to recognize the customers who want

some information, and who ask specific questions about their industry. Customers will

demonstrate loyalty to the company which has answered their specific questions. The

expectations of the customers have to be met consistently. Other uses include delighting

customers by meeting un-met needs, innovating new products, services, features and functions.

The result is that customers recommend the company and its products to their friends and family.

Customer Experience Management is effectively achieved when the customers step up to defend

the company and its product when they hear a negative comment about the brand. This can be

achieved only by connecting with the customer on an emotional level, showing you care and

appreciate them. Thus to achieve high degree of Customer Experience Management, the

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companies should review their customer loyalty programs and strive to meet the basic needs and

un-met needs and make their customers feel valued.

Figure 3.3: Building Emotional connection with consumers

3.2.2.2. Listening to Voice of the customer and engineering influence: The bidirectional

communication can be used to practice listening to the customers. The bidirectional

communication is an important function of an online community. It further enhances customer

involvement and Customer Experience Management. The online web space is becoming a very

common place where people are connecting with other people in an emotional way. The

organisations have been listening to their customers for many years via surveys, via user tests for

interaction through their website. The online web space is an opportunity to engage and

communicate with customers, listening to them, showing them how the organisations are acting

on their feedback, and giving them feedback on what the organisation is doing. Customers want

to be listened to, they do not want to be passive receptors of the company’s sales pitch. The

online consumer communication tools can be blended with other voice of the customer sources

to create a holistic view of customer priorities.

Figure 3.4: Suitability for Voice of Customer


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3.2.2.3. Building consumer participation: Online communities allow employees to share their

skills with other employees and consumers of a product; they are best in building a community

of customers. Continuous knowledge flow and capture due to steady inflow of quality data will

maximize the company’s intellectual resources. Intellectual resources will help organisations in

retaining old partners as well as capture new markets and clients. Consumer participation

enhances knowledge base for better knowledge acquisition from the explicit knowledge base.

The posts and reviews help in increasing the organisational brand equity as the consumer

perception for a product varies and depends a lot on the discussion on these communities. The

online communities have more discussions about the competitor’s products also and about new

features which increase the perceived value by stakeholders which can be utilized for growth of

the company.

Figure 3.5: Enhancing Customer Participation

3.2.2.4. Increasing customer knowledge and clear priorities for acting on customer

feedback: The organisations should establish a process internally where customer feedback from

online communities can be used as a basis for continual improvement of customer experience.

The importance of Customer Experience Management should be emphasized across all functions

of the organisation. A vital function of Customer Experience Management is to focus on

strategically significant customers. It is hence important to learn what these customers value and

what are the most important attributes driving customer loyalty and intent to purchase. A key

component of a branded customer experience is being differentiated in a way that is valuable to

targeted customers. This implies having a detailed understanding of the customer experience and

being intentional about designing it to deliver value at the key touch-points.

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Figure 3.6: Increasing Customer Product knowledge

3.2.2.5. Increasing customer centricity: Greater customer centricity can be achieved by

nurturing a congenial environment, which aims at increasing customer value by increasing

customer satisfaction, increasing customer involvement and attempting to delight the customer.

Organisational objectives comprise nurturing brand advocates by connecting emotionally.

Figure 3.7: Customer centricity

3.2.2.6. Building a Community: This facilitates peer to peer consumer connect. There is a

concept of empathy and trust prevalent in Online Communities as it is said that greater

similarities amongst people forge better understanding. Furthermore when people discover they

have similar problems, requirements, opinions or experiences they may feel closer, more trusting

and be prepared to reveal even more. This has a “snowball effect” in that the more people

discover that they are similar to each other, the more they tend to like each other and the more

they will disclose about themselves. This is known as “self disclosure reciprocity” and it is

powerful online.

Figure 3.8: Help build a community of customers


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Results were tabulated (Table 3.4). An individual score across each parameter was calculated for

each of the three tools. This was done by dividing the score across each parameter by the sum

total of the scores for each tool respectively.

Table 3.4: Individual Score across each parameter for three tools S.No. Parameter Blog Online

commun

ity

Wiki Blog-

Score

Online

community

-Score

Wiki-Score

1 Build Emotional

Connection with

consumers

59 54 51 0.087537

0.068878 0.09729

2 Suitability for Voice of

Customer

60 61 49 0.089021

0.077806 0.07815

3 Enhancing

customer

participation

65 70 39 0.096439

0.089286 0.0622

3.1 Ease of navigation 50 57 67 0.074184

0.072704 0.10686

3.2 Ease of registration 53 74 47 0.078635

0.094388 0.07496

3.3 Ease of accessibility 43 62 69 0.063798

0.079082 0.11005

3.4 Garnering traffic 55 71 48 0.081602

0.090561 0.07656

4 Increasing

customer

knowledge

of product

62 55 56 0.091988

0.070153 0.08931

5 Customer centricity 54 68 54 0.080119

0.086735 0.08612

5.1 Facilitating communication 55 67 52 0.081602

0.085459 0.08293

5.2 Congeniality of environment 63 63 48 0.093472

0.080357 0.07656

6 Help build a community of

customers

55 82 37 0.081602

0.104592 0.05901

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Figure 3.9: Customer Experience Management using Online Communities

Online communities emerge as the best collaborative tools from the Customer Experience

Management perspective as they score highest with reference to building a community of

customers. They have greater accessibility which results in greater consumer participation,

hence building a group of loyal customers. People believe that they are breeding grounds for

new ideas and product improvements.

The outcomes of the above two pilot studies, hence formed the basis for my research thesis.

1. Online communities provide information that is updated frequently based on current

discussions and as it is online, it can also be consumed by people at diverse geographic

locations, thereby increasing knowledge share and exchange.

2. They provide better platform for communication which helps in increasing explicit

knowledge base of an organisation. They also help in knowledge transfer from their

database to the company’s database for analysis.

3. The posts made on the forums help an organisation in delivering good value to the

customer, based on their experiences and knowledge. In spite of blogs providing easy

search, people prefer these communities as a better substitute of word of mouth

marketing. Thus they provide continuous knowledge transmission back for better people

management and change.


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4. Online Communities also provide a platform to both customers and employees to share

their observations, thus increasing socialization and interaction among the employees and

customers. Due to socialization, there is continuous knowledge reuse which helps

organisations for innovation on new products and developing an interaction friendly

environment for increasing product as well as brand equity.

5. Content delivery is better on wikis as they display the complete details of the products in

an organized manner. Wikis are the best alternative form of media as they provide

detailed knowledge of products, as compared to online communities or blogs, but blogs

provide the best data organisation as search is easy.

3.3. Proposed Conceptual Model

The conceptual model focuses on studying the ability of online communities, as a channel to

serve as an interface between organisation and consumer and aid the organisation in achieving its

CRM goals. The functions of the online communities which aid the process are listed in the

conceptual model below.

CRM and CEM using Business Online Communities

CRMCEMCLV

Co CreationEconomics of CRM

Operational, Analytical and

Collaborative CRMConsumer

Segmentation and Profiling

Customer Portfolio Analysis

Research Methods and Tools

Research InstrumentsRI1:-Applying Metcalf Law for INV & CNV-Identifying consumers with high relationship and profit potentialRI2: -Community Manager’s perspective and the OC

-Pilot Study-Netnography for identifying consumer trustworthiness

Online Community Dynamics

P¡ DOP¡ EA¡ OT¡ C¡ ML¡ AS¡ PAN¡ CP¡

Figure 3.10: Achieving CRM Goals by Using Online Communities

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3.4. Research Objectives

This research thesis focuses on studying the ability of online communities, as a channel to serve

as an interface between organisation and consumer and aid the organisation in achieving its

CRM goals. This is accomplished through the following research objectives-

1. Develop a model to analyze the usage of online consumer communities for identifying the

components that build consumer trustworthiness for an organisation.

2. Create a framework for calculating the value of an online community based on its customers.

2. a. Identification of determinants of Individual Network Value (INV) and Community Network

Value (CNV) and creation of a framework to use INV as a basis for identifying consumer co-

creators.

3.Creation of a framework for selection of consumers demonstrating high future profit or

relationship potential and devise strategies to impact consumer price sensitivity for expensive,

medium and low cost products for organisations.

4. Creation of a model for identifying the credibility of a business online community from a

community manager’s perspective.

To accomplish Research Objective 1, Netnography was conducted on 40 online product

communities of Apple. Research objectives 2 and 3 were accomplished through self designed

Research Instruments (RI-1 and RI-2), (Annexure II) Research objective 4 was also

accomplished through self designed Research Instrument (RI-3), (Annexure II.).

3.5. Research Methodology for formulation of Research Instrument

I have applied the research technique of Netnography, which is very specific to the online

domain for formulation of two sets of research instruments. Experience is something singular

that happens to an individual and researchers cannot directly access (Caru, A. and Cova, B.,

2008). Therefore researchers only interpret what their subjects have expressed orally, in writing

or through their behaviour. Experience becomes more and more important to marketing,


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however, the methodologies typically used to research experiences, such as interviews and focus

groups, have a number of drawbacks such as respondent inhibition (Elliott, R. and Jankel Elliot,

N., 2003). Verbatim comments instead, are important for understanding the private nature of the

experience to be studied.

3.5.1. Netnography

It is a new qualitative, interpretive research methodology that uses internet optimized

ethnographic research techniques to study the online communities. With the help of

Netnography, Online Community research can be done by either actively integrating the

members of the community or passively monitoring the community and integrating the gathered

information, knowledge and ideas into the new product development process, (Kozinets, Robert

V., 2002).

As a method, “Netnography” is faster, simpler, and less expensive than ethnography, and more

naturalistic and unobtrusive than focus groups or interviews. It provides information on the

symbolism, meanings, and consumption patterns of online consumer groups.

As a marketing research technique, “Netnography” uses the information publicly available in

online forums to identify and understand the needs and decision influences of relevant online

consumer groups. Compared to traditional and market oriented ethnography, “Netnography” is

far less-time consuming and elaborate. Another contrast with traditional and market-oriented

ethnography is that “Netnography” is capable of being conducted in a manner that is entirely

unobtrusive. Compared to focus groups and personal interviews, “Netnography” is far less

obtrusive, conducted using observations of consumers in a context that is not fabricated by the

marketing researcher. It also can provide information in a manner that is less costly and timelier

than focus groups and personal interviews. “Netnography” provides marketing researchers with a

window into naturally occurring behaviours, such as searches for information by, and communal

word-of-mouth discussions between, consumers. Because it is both naturalistic and unobtrusive,

an unprecedented unique combination not found in any other marketing research method-

“Netnography” allows continuing access to informants in a particular online situation. This

access may provide important opportunities for consumer-researcher and consumer-marketer

relationships.

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Figure 3.11: Netnography as a Research Technique

3.5.2. Significance of Netnography

One of the main benefits of this methodology is the possibility to access unfiltered unbiased

information from very experienced and highly involved users, due to the huge amount of

conversations and the vivid online dialogue regarding consumer products marketing and

innovation. Managers are able to obtain deep insights into the everyday problems experienced by

consumers and their solutions to those problems. One of the main expectations of this new

technique of research methodology is to utilize a huge number of consumer statements for

qualitative analysis, to get unobtrusive and unbiased original consumer statements and to get

access to specialized user groups.

3.5.3. Procedure in Netnography

The following steps and procedures are included in a typical Netnography Research, (M. Bartl,

Steffen Huck, Stephan Ruppert., 2009).

3.5.3.1. Definition of Research field: It includes the definition of the field of innovation as well

as the systemization of topics, trends, markets and products which are of major interest. The

operating result of the first step is an extensive mind map that contains a classification and


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structured set of topics which are used as starting point to define search strategies for the

identification of adequate online sources.

3.5.3.2. Identification and selection of Online Communities: The aim of the second step of

Netnography is to identify communities and internet sources where users exchange relevant

information on the defined research area. For this purpose, general online search engines, meta

search engines and specific online search engines that focus on blogs, groups, communities are

used. Having identified and sighted often, a couple of hundred relevant online sources for

Netnography, the researcher has now to select the communities which can be probed in for

further in-depth analysis. There exist a number of appropriate and well proven qualitative and

quantitative criteria which support the researcher in the selection procedure. Qualitative criteria

include e.g. “topic focus”, “data quality”, “language type”, “interaction type”, “profile editing”.

Quantitative criteria include criteria such as “number of messages”, “frequency of usage”,

“member activity”, “data quantity” or “interaction level”.

3.5.3.3. Community Observation and Data Collection: In this step, the selected online

communities are observed by the researcher who immerses in the community. This is

accomplished by extensive reading with focus on conversations which are recent, extensively

corresponded to, referenced and frequently viewed from the community members. While

before the emergence of the internet, it was necessary for the researcher to participate in the

considered group, nowadays Netnography enables observation and analysis of the consumer

communication without active participation. Hence the approach is a way to unobtrusively

study the nature and behaviour of online consumer groups. The analysis is conducted in the

natural context of the community and thus is free from the bias which may arise through the

involvement of the researcher or experimental research setting.

3.5.3.4. Data Analysis and Aggregation of consumer insights: The “thinking” about the

“noticed” and “collected” online consumer statements is part of the fourth step of

Netnography. In this step the aim is to look for patterns and relationships within and across the

collections of consumer statements and to make general discoveries about the subject matter of

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research. Therefore, the researcher compares and contrasts the collected consumer records in

order to discover similarities and differences, build typologies, or find sequences.

3.5.3.5. Translation of Community insights into product and service solutions: The

Netnography process typically does not end with the generation of insights. A major challenge

is to transfer the obtained insights into innovative product and service solutions. The

implications of the results could be for product, brand, target group as well for the process of

communication e.g. source for product innovations and product modifications and

development of consumer oriented communication strategies.

3.5.4. Strengths of Netnography Revelatory depth of online communication

Ability to provide interesting and useful conclusions from small number of messages

Useful for contextualizing the data

Can be used as a standalone method for tracking the marketing related behaviours of

members

Is based primarily on observation of textual discourse

Utilizing carefully chosen message threads is akin to “purposive sampling” in market

research

3.5.5. Benefits of Netnography to Online Communities Greater consumer engagement and participation

Enhance their value perception

Co-creation and consumer evangelism

Relationship building, value creation and commitment

Refine marketing actions, while reducing the cost of routine sales

Identification of strategically significant community members


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3.5.6. Limitations of Netnography

The limitations of “Netnography” draw from its more narrow focus on online communities, the

need for researcher interpretive skill, and the lack of informant identifiers present in the online

context that leads to difficulty generalizing results to groups outside the online community

sample. Marketing researchers wishing to generalize the findings of a “Netnography” of a

particular online group to other groups, must therefore apply careful evaluations of similarity and

employ multiple methods for triangulation.

3.5.7. Netnography Methods Adopted

The following Netnography methods were used for the formulation of the two research

instruments-

Gaining entree into the community or group I wanted to investigate

Gathering and analyzing data

Ensuring trustworthiness of data interpretation

Conducting ethical research

Member checking, or getting feedback from participants (Hammersley, M. and Atkinson, P.,

1995;, Lincoln, Y. and Guba., E 1985; Wolcott, H.F., 1994)

The entree involved identifying the online communities most relevant to my research as well as

learning as much as possible about the communities that are identified. The following features

were preferred -

A more focused, relevant segment, topic, or group with large number of questions

Higher traffic of postings

Larger numbers of discrete message posters

More detailed or descriptively rich data

Higher Interactivity between members

Netnography in the form of both participatory observation as well as non participatory

observation was conducted for the formulation of the constructs of the three research

instruments. For the formulation of the third research instrument for community managers, the

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specific behaviour of managers and moderators was observed. For the participant observation, I

participated in the community and asked some specific questions to the members who depicted

very high status level in the community, high level of participation and provided maximum

number of correct answers which further depicted a higher consumer brand or product

knowledge.

Netnography in the form of participatory observation was performed for the business online

communities. Some examples of communities where I conducted participant observation are

Apple, Dell, HUL, Microsoft, CSC, Cisco and Shiksha.com.

Netnography in the form of non-participatory observation was performed for some other

business online communities wherein the customer reviews published on the internet contained

detailed information about their experiences with the products and services of the firm. The

reason for choosing a combination of participatory and non participatory observation is the

undesirable influence of the outsider to the group. The learning derived out of this was used to

study individual behaviour and subsequently formed the basis to develop specific constructs to

study consumer learning.

3.6. Consumer Trustworthiness Regression model using Netnography (CTR)

Research Objective 1: Develop a model to analyze the usage of online consumer communities for

identifying the components that build consumer trustworthiness for organisations.

In order to accomplish the first research objective the online marketing research technique of

Netnography has been applied and an analysis of 40 different online product communities of

Apple and one online community of Dell namely “Ideastorm” has been done.

3.6.1. Netnography of 40 online product communities of Apple

This study aimed to achieve the following:

To study Electronic Customer Relationship Management in Organisations (E-CRM)

IT Enabled Relationship Management between organisation and consumer

Increase consumer engagement, participation, and trustworthiness of participants


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Value creation and consumer commitment

Before I outline the entire study in detail, the following variables which appear on the Apple’s

online product communities have been defined as they form the genesis for the further study.

These variables help us to measure the trustworthiness of participants.

1. No. of Points: It represents the status level of a member in the forum category or at the

main community level. These points are earned by replying to another members’ question

topic. This is considered as a measure of Reciprocity and correctness of replies made by

a forum member.

2. No. of Views: It represents the no. of times a participant post is viewed by other

members. This is indicative of trustworthiness of the participant.

3. No. of Days: It represents the total number of days spent by a consumer in the online

community of Apple from the date of registration till 1st Dec 2010. This is indicative of

longevity of community presence.

4. No. of Posts: It represents the volume of messages created by the community members.

This is considered as a measure of Participation.

A previous study (Alavi, S., Ahuja V., and Medury Y 2010) “Building Participation, Reciprocity

and Trust – A Netnography of an Online Community of Apple-Using regression analysis for

prediction” had already explored the implications of all the above variables. The number of posts

and number of days appeared to have no direct relationship with trust, hence the same were not

explored further, in this study.

3.6.1.1. Content Organisation

Content organisation on the site, is initiated to enable consumers to view relevant content in

order to induce greater consumer participation and for creating and maintaining value laden

relationships with current and potential customers. The typology of content that attracts greater

consumer interest and generates subsequent engagement by soliciting participation and

involvement through comments needs to be identified to enable organisations to post content in

accordance with consumer receptivity. The online community of Apple - Apple Discussions

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118 Ph.D. Thesis

(http://discussions.apple.com/category.jspa?categoryID=204) was used for our study and

secondary data for about 400 consumers was collected for the study and further regression

analysis was done.

The content in Apple Discussions is organized as follows:

1. Forum Categories—Categories represent a collection of topical forums as well as other

categories, and are used to organize forums. Most categories are generally defined by a product

name, such as "iPod," "iMac," or "Mac OS X v10.4 Tiger." The Apple Discussions Forum has

40 product categories.

2. Forums—Forums are the areas where individual discussions take place. The discussions are

displayed as a list of topics. For example, if a consumer is looking for conversations about

searching their Mac with Spotlight, they can click the Mac OS X v10.4 Tiger link on the

Discussions homepage, and then click the Spotlight link in the resulting Tiger page to visit the

Spotlight forum.

3. Topics—Topics refer to the actual topics of discussion, each of which consists of messages

displayed as a conversation.

4. Messages—Messages are the individual posts made by community members. If a consumer

clicks a topic to view a discussion, they will see messages posted by other members.

5. Replies—Replies are posts made in response to other messages and are organized in a flat or

threaded manner. For example, if someone posts a question in a topic, other members may post a

reply to that question.

The procedure has been carried out for 40 product communities of Apple.

3.6.1.2. Participation and Reciprocity in the Apple’s Online Community

When a community member posts a question as a topic starter, other members can post an

answer in reply. An answer can be just some hints or helpful information to help the poster solve


119 Ph.D. Thesis

an issue. The originator of the topic can mark such a reply as a "Helpful" post. A small yellow

star appears next to that reply and the person who posted the reply is awarded 5 points. If a

community member posts a specific answer, that provides a solution to the original poster's

issue, the originator marks this reply as a "Solved" post. A large green star appears next to that

reply and the person who posted the reply will be awarded 10 points. Only the original topic

poster has the option to mark replies as either Helpful or Solved or to not mark a reply at all. The

originator can also end the discussion by marking the topic as "answered," which displays a

green star at the top of the topic page to let everyone know that the topic contains valid helpful

information. If a participant replies to another member's question topic, they are eligible to

receive points from that member, though this is at his or her sole discretion. The originator has

the option of marking a reply as either Helpful or Solved, which will add points to the

respondent's account. These points, in turn, increase a member's ranking (status level) in the

community over time. A member receives 10 points for each reply that a member marks as

"Solved" and 5 points for each reply that a member has marked as "Helpful." The reward system

helps to increase community participation. When a community member gives a reward to

another member for providing helpful advice or a solution to his or her question, the recipient's

points will help increase his or her status level within the community. Members can see their

status level by Forum, Category, or at the main Community level.

Table 3.5: Depiction of status level for Apple’s communities Status Level Point Range

5 50,000+

4 8,000 - 49,999

3 1,000 - 7,999

2 150 – 999

1 30 – 149

Under this model, the studies are conducted on a set of online communities of Apple and the

correlation and regression model is applied for forty product categories. The results of a regression

model are used to analyze factors contributing to the growth of trust in an online community and for

finding out the contribution of the independent variable, that is, number of points to the dependent

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120 Ph.D. Thesis

variable, that is, number of views. The information is subsequently applied for prediction by analyzing

how far the dependent variable depends on the independent variable.

The results and findings of this study of Netnography have been detailed in Section 4.1.1. of

Chapter 4 on Results and Findings.

3.6.2. Netnography of Dell Ideastorm

The online community of Dell named “Ideastorm” bypasses the middlemen and sells directly to

the customers. It aims to integrate the experiences of the customers with the process of co-

creation. It leverages user collaboration to act as a multiplier force for better collaborative CRM.

Dell wants to see what all products and services users want Dell to develop. IdeaStorm gives a

direct voice to the customers. In almost three years, Idea Storm has crossed the 10,000 idea mark

and implemented nearly 400 ideas. The customers can interact with each other as well as Dell.

Customers can voice their concerns as well as complaints. Though these complaints may not be

regarding Dell units that these customers possess (handled by the support section), it is the user’s

feedback about the product in general. Managers can judge needs and respond quickly. A posted

idea grants Dell a royalty free license to use and implement it without compensation to the

originator. Marketers already have an idea of product perception amongst the consumer base.

The managers have increased clarity regarding what customers want and such products are more

likely to have better sales.

The following variables formed the basis of the study-

1. No. of Posts: Users can post their experiences and ideas

2. No. of Comments: Users can comment on others ideas

3. No. of Votes: Users can promote and demote others

4. Voted up: No. of vote ups on user’s ideas

5. Voted down: No. of vote downs on user’s ideas

6. No. of ideas :The number of ideas submitted

The data for users in the online community of Dell is collected and compiled and further

regression analysis was done (Section 4.1.2).


121 Ph.D. Thesis

3.7. Research Instruments

Three sets of self designed questionnaires have been drafted, based on Netnography conducted

and extensive literature review, in order to study and analyze the factors that play a pivotal role

for an online business community to be successful in achieving the Customer Relationship

Management and Customer Experience Management goals of an organisation, both from the

perspective of an online community consumer and a community manager. All research

instruments are detailed in Annexure II.

3.7.1. Questionnaire for Online Community Consumers (RI-1)

This research instrument (RI-1) was divided into two sections. The first section i.e. Section

gathered information about the demographics of the respondents which included their name, age

and gender. The second section i.e. Section B measures Consumer participation, Degree of

participation, Emotional Attachment, Online Trust, Usability of the features of an online

community, Commitment, Member Loyalty, Attitude towards switching and Period of

association. The individual items of each of the questions are highlighted in the table below

(Table 3.6).

Table 3.6: Individual items of RI-1

S.No. Constructs Items 1. Participation

(4 point likert scale: 1-Definitely not important ,4-Very important)

Ease of access Barriers to entry Responsiveness Ability to address participant needs Role assignment Member empowerment Content typology Membership duration Membership retainment Degree of flexibility Access to shared resources Shared goals

2. Degree of Participation (5 point likert scale: 1-Poor, 5-Excellent)

Frequency of Login Ability to navigate Ability to provide correct responses Reciprocity Interactivity Co-presence Increase in trust Volume of content contributed Computer and Internet proficiency

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122 Ph.D. Thesis

Abilility to navigate the internet

3. Emotional Attachment (5 point likert scale: 1-Never, 5-Always)

Emotional support Belongingness Familiarity Affinity Bonding and liking Aids recruitment

4. Online Trust (3 point likert scale: 1-Low, 3-High)

Resolve consumer problem Consumer Uncertainty Customer satisfaction Communication level Increase Reliability Increase Altruism Promote self disclosure

5. Usability of features of an online community (Rank Order Scaling )

Chat Wikis Q&A Debate Forum Blog File Manager

6. Commitment (5 point likert scale: 1-Poor, 5-Excellent)

Support members Explicit goals Focus on specific needs Promote same identity Motivate for contribution Interpersonal similarity Clustering members Named groups Promote frequent interaction Promote interdependent tasks Promote competition Reduce repelling forces Promote diversity Promote testimonials

7. Member Loyalty (5 point likert scale: 1-Strongly Disagree,5-Strongly Agree)

Recommend Product Suggesting others to join Resolve problems Proactive to help resolve problems Rebuy or repatronize Interaction with other members Growth in community membership Business and social goodwill Feedback Test a new product or idea

8. AttitudeTowards Switching (3 point likert scale: 1-Never, 3-Always)

Switch Brand Low switching costs Change brand Discount option Unique buying experience Emotional Attachment Perceive greater quality in product Reputation of the company


123 Ph.D. Thesis

9. Period of association with the network

Less than a year 1-2 years 2-3 years 3-4 years More than 4 years

3.7.2. Questionnaire for Online Community Consumers (RI-2)

This research instrument (RI-2) measures Consumer Price Sensitivity (For expensive, medium

and low cost products). The individual items of the question are highlighted in the table below

(Table 3.7).

Table 3.7: Individual items of RI-2 S.No. Constructs Items 1. Consumer Price Sensitivity

(5 point likert scale: 1-Strongly Disagree,5-Strongly Agree)

Desist from purchase if price out of line Resist purchase if price is more Evaluate price of competing brand Factors impacting resistance to brand

switching Sensitivity to product price

3.7.3. Questionnaire for Online Community Managers (RI-3)

This research instrument (RI-3) measures Community Dynamics, Co-creation, Online

community as a collaboration enabler, CRM goals of an organisation, Return on Investment and

Customer Life Time Value. The individual items of each of the questions are highlighted in the

table below (Table 3.8).

Table 3.8: Individual items of RI-3 S.No. Constructs Items

1. Community Dynamics (5 point likert scale: 1-Strongly Disagree, 5-Strongly Agree)

Fixed Purpose Fixed Policy Sufficient Community Size Quality of Participation Latest technologies and tools Restriction on membership Easily Navigable Greater Interactivity Breaks cross cultural barriers Designed for growth and change Generation of profits Platform for consumer learning Flexible for change

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124 Ph.D. Thesis

Greater accessibility to members Communication (Linked to search

engines) Presence of knowledge manager Influence of Moderator Depicts Self Disclosure Reciprocity Encourages the level of empathy Encourages the level of trust Provides clarity of procedure Creates and maintains feedback loop Strategic Marketing tool for company Supports the role of a developer Strategic marketing tool for company

2. Co-creation (5 point likert scale: 1-Not at all effective,5-Very Effective)

Improving customer satisfaction Achieving high productivity levels Collecting innovative ideas for new products Capturing and storing of new ideas Application and discussion on new ideas Harvesting new skills and competencies Develop consumer experts Creating value for existing products

3. Collaboration Enabler (5 point likert scale : 1-Never ,5-Always)

Operational performance Lower costs and higher quality Improved customer service and satisfaction Higher value added relationships Faster responsiveness

4. CRM Goals (5 point likert scale : 1Poor ,5-Excellent)

Consumer Information System Increase Customer Satisfaction Referrals from satisfied customers Predicting Consumer Behaviour Identify leads to generate customer pipeline Increase profitability Improve business planning Assess size of target market Assess growth rate of target market Improved customer sales to target ratio Increase overall customer profitability Increase customer service quality Increase customer retention Caters to Mass Customization Provides customized communications Long term customers to enter into

collaborations Lower Price Sensitivity Less time duration for conversion of prospect

to consumer 5. Return on Investment

(Dichotomous Question)

True False

6. Customer Life Time Value (5 point likert scale : 1Poor 5-Excellent)

Identify profitable customers High cost to serve customers Incremental Revenue per customer Average amount per purchase Increase in retention rate


125 Ph.D. Thesis

Identify lifetime value potential of customers Identify strategic customers Consumer segmentation Anticipated life time of the customer

relationships Reduce acquisition cost Decrease direct marketing costs Reduce cost of mailing Provide presale and post sale support Identify customers who value unique

functionality and features Increase average number of consumer

purchases Improved ordering and delivery relationships Raise switching costs Increase a consumer’s contribution margin Identify price sensitive consumers Reduce consumer price sensitivity Retention effort is aligned to customer life time

value Identify customer categories for cross selling

and up selling Development of service and product portfolios

3.7.4. Pretesting the Research Instruments (RI-1, RI-2 and RI-3)

Pretesting implies testing of the questionnaire on a small sample of respondents for the purpose of

improving the questionnaire by identifying and eliminating potential problems. Pretests were done by

conducting personal interviews. The respondents who were selected for pretesting of the

questionnaires were similar to those that were included in the actual survey. The respondents for the

first two questionnaires were online community consumers and for the third were community

managers. The sample size for pretesting was 30 respondents for RI-1, RI-2 and 10 respondents for

RI-3. The questionnaires were administered in an environment and context similar to that of the actual

survey. The debriefing procedure was used for pretesting. Debriefing occurred when the questionnaire

was completely filled up by the respondents when they were informed that the questionnaire filled up

by them was meant for a pretest. The objectives of the pretest were described to them and then they

were asked to a) describe the meaning of each question, b) explain their answers and c) state the

problems encountered by them at the time of answering the questions. After this, the questionnaires

were edited in the context of the identified problems.

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126 Ph.D. Thesis

3.7.5. Reliability and Validity of RI-1

In order to test the reliability and validity of RI-1, the same was administered to 100

respondents. The collected responses were tabulated. Reliability and validity tests were

conducted on the data to check the validity and usability of the instrument. Cronbach’s alpha,

KMO measure of adequacy and Bartlett’s test of sphericity were conducted.

Reliability refers to the extent to which a scale produces consistent results if repeated

measurements are made (Sinha P., 2000). Thus it is the degree to which selected constructs yield

similar results. It is assessed by determining the proportion of systematic variation in a scale by

determining the association between scores obtained from different administrations of the scale.

If the association is high, the scale yields consistent results and is therefore reliable. For my

research, I have used internal consistency method of reliability. The internal consistency

reliability of the research instruments was tested by using cronbach’s alpha. It is one of the most

frequently used methods to check the internal consistency of the survey items. A higher degree

of cronbach’s alpha coefficient demonstrates higher degree of inter item correlation among the

constructs. If the value of cronbach’s alpha is more than 0.7, then the instrument is considered to

be reliable.

Further, Kaiser-Meylen-Olkin test was done to measure the homogeneity of variables and

Bartlett’s test of sphericity was done to test for the correlation among the variables used. The

Bartlett’s test showed significant results for all the questions and thus the instrument was used

for further study. Table 3.9 summarizes the results. Thus the instrument was further circulated

for data collection.

Table 3.9: Tests of Reliability and Validity of RI-1 Questions No. of

Items Cronbach’s Alpha

Kaiser-Meyer-Olkin Measure of Sampling Adequacy

Bartlett's Test of Sphericity

Participation 12 0.897 0.916 Approx. Chi-Square

12088.012


127 Ph.D. Thesis

df 190.00

Sig. .000

Degree of participation

10 0.803 0.855 Approx. Chi-Square

5052

df 210.00

Sig. .000

Emotional attachment


4450

df 290.00

Sig. .000

Online Trust 7 0.858 0.789 Approx. Chi-Square

12354.031

df 180.00

Sig. .000

Usability of Online Tools


11543.051

df 235.00

Sig. .000

Commitment 14 0.863 0.931 Approx. Chi-Square

3975.321

df 310.00

Sig. .000

Member Loyalty


2495.720

df 156.00

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128 Ph.D. Thesis

Sig. .000

Attitude towards switching


6058.012

df 190.00

Sig. .000

Period of association with the network


5207.352

df 250.00

Sig. .000


In order to test the reliability and validity of RI-2, the same was administered to 100

respondents. The collected responses were tabulated. Reliability and validity tests were

conducted on the data to check the validity and usability of the instrument. Cronbach’s alpha,

KMO measure of adequacy and Bartlett’s test of sphericity were conducted. Table 3.10

summarizes the results. Thus the instrument was further circulated for data collection.

Table 3.10: Tests of Reliability and Validity of RI-2

Questions No. of Items

Cronbach’s Alpha



Consumer price sensitivity


3495.620

df 165.00

Sig. .000


129 Ph.D. Thesis


In order to test the reliability and validity of RI-3, the same was administered to 50 community

managers. The collected responses were tabulated. Reliability and validity tests were conducted

on the data to check the validity and usability of the instrument. Cronbach’s alpha, KMO

measure of adequacy and Bartlett’s test of sphericity were conducted. Table 3.11 summarizes

the results. Thus the instrument was further circulated for data collection.

Table 3.11: Tests of Reliability and Validity of RI-3 Questions No. of

Items Cronbach’s Alpha



Community Dynamics


5039.522

df 252.00

Sig. .000

Co-creation 8 0.872 0.786 Approx. Chi-Square

4058.012

df 160.00

Sig. .000

Collaboration Enabler


3450

df 180.00

Sig. .000

CRM goals 18 0.902 0.816 Approx. Chi-Square

2495.720

df 153.00

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130 Ph.D. Thesis

Sig. .000

Return On Investment


5072.552

df 153.00

Sig. .000

Customer Life Time Value


3039.522

df 162.00

Sig. .000

3.8. Co-creation model using INV based on Metcalf Law (C-INV))

This model aims at analyzing the determinants of Individual Network Value (INV), further goes

on to develop a framework for calculating Community Network Value and subsequently,

empirically identifies individuals who have high INV. The community value of a network is of

great importance to organisations, especially from the CRM, Marketing and Customer

Experience standpoint. The study segments consumers using hierarchical cluster analysis into

groups to identify consumer co-creators.

The study of INV addresses the following research questions:

1. Identification of determinants of Individual Network Value (INV) and Community Network

Value (CNV)

2. Weighting of determinants

3. Using Metcalf Law to create a framework for calculating Individual Network Value (INV)

4. In the purview of Metcalf’s law and Individual Network Value, create a framework for

calculating the value of an online community based on its customers

5. Creation of a framework to use INV as a basis for identifying consumer co-creators

3.8.1. Identification of Determinants of Individual Network Value

My previous research studies on E-CRM (Alavi, S, Ahuja V. and Medury Y., 2011), Online

Communities (Alavi, S, Ahuja V. and Medury Y., 2011) and Customer Experience Management


131 Ph.D. Thesis

(Alavi, S, Ahuja V. and Medury Y., 2010) helped identify the following determinants of INV. To

calculate the value of an individual consumer in a network, we consider the following variables.

1. Participation in the community network (P¡)

2. Emotional Attachment (EA¡)

3. Online trust(OT¡)

4. Commitment(C¡)

5. Member Loyalty(ML¡)

6. Attitude towards switching (AS¡)

7. Period of association with the network(PAN¡)

3.8.2. Weighting of Determinants

An Evaluation Grid 3 mentioned in Annexure I, was circulated to a focus group of 30

respondents, who were asked to rate the seven determinants of INV on a scale of 1-5 (5-

Excellent, 1–Poor). Based on their rating the following weights were extracted for each of the

determinant-Participation (P¡)-0.25, Emotional Attachment (EA¡)-0.13, Online Trust (OT¡)-

0.16,Commitment (C¡)-0.12, Member Loyalty (ML¡)-0.13, Attitude towards switching (AS¡)-

0.10, Period of association with the Network (PAN¡)-0.11.

3.8.3. Framework for calculating Individual Network Value (INV)

Based on the above determinants, the weighting criterion and Metcalf law, we create the following formula for calculating Individual Network Value.

Individual network Value = (0.25* P¡ + 0.13* EA¡ + 0.16* OT¡ +0.12*C¡+0.13* ML¡+0.10*

AS¡+0.11* PAN¡)

3.8.4. Framework for calculating value of an Online Community Further in the purview of Metcalf’s law and Individual Network Value, we create a framework

for calculating the value of an online community based on its customers.

Community Network Value=∑INV1+∑INV2+∑INV3+…..∑INV100

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132 Ph.D. Thesis

This framework modifies the above formula and CNV can be calculated as follows-

n (CNV) = ∑ (0.25* P¡ + 0.13* EA¡ + 0.16* OT¡ + 0.12* C¡ +0.13* ML¡+0.10* AS¡ i=1 +0.11* PAN¡)

Under this model the values of INVi (Table 4.27, Annexure III) were subjected to Hierarchical

cluster analysis using SPSS 17.0 and five consumer clusters were identified (Table 4.28, Section

4.3). Based on Table 4.27, Annexure III, the profiles of the 200 consumers divided into five

clusters were identified and appropriate targeting strategies were formulated (Table 4.28,

Section 4.3).

3.9. Consumer Price Sensitivity model using k-means cluster analysis (CPS)

This model attempts to study the usage of online communities to study consumer price

sensitivity in the context of the type of product purchased, i.e. expensive, medium or low cost

products. It examines the impact of reference price effect, difficult comparison effect, price

quality effect and switching cost effect on consumer price sensitivity and proceeds to segment

consumers into groups which demonstrate similar characteristics. This model uses data collected

from the Apple Online Discussion Forums

(discussions.apple.com/category.jspa?categoryID=204). Apple has over 40 product communities

formulated for consumer engagement, interaction, feedback, building strong online consumer

trust and reciprocity. The consumer profiling was done on the data collected using K-means

clustering and cluster memberships were extracted and the most significant consumers across all

three product categories were identified. Further the detailed profiles for most significant

consumer clusters for each price category were created (Table 4.40, Section 4.5).

Organisations will benefit by identifying the strategically significant consumers in each category

and then target them appropriately rather than investing in a blanket promotion program. The

objective is to enable organisations to identify consumers demonstrating future profit or

relationship potential and devise strategies to impact price sensitivity by responding to price

search intentions, improving product perceptions, improving consumer experiences, informing

consumers about new schemes and improving product perceived value. Most valuable consumers


133 Ph.D. Thesis

will be those who depict consistent behaviour across all three product typologies, as they will

tend to be the most loyal. Valuable consumers identified through a customer portfolio analysis

can be leveraged by hosting appropriate content in an online business community and

subsequently using customers engaged through business online communities as important

sources of competitive advantage. Due to these benefits, online business communities may

generate more profitable sales than transactional marketing methods.

3.10. Data Collection for RI-1

The data collection for the research instrument RI-1 has been done in two phases.

3.10.1. Phase I of primary data collection

The comprehensive list of 100 online consumer communities was used as the database for my

research. This list was sourced from the “Customer Community project, Building Professional

Peer Communities”, Leader Networks, 2009. The online questionnaire was drafted using

Qualtrics (www.qualtrics.com). The appropriate link was hosted in the respective community.

The data was primarily collected through a period of six months starting from July 2011 till

December 2011. As it was not possible to build one to one contact with all the online community

consumers, a sample size of 200 was chosen for the study. The sampling techniques are

discussed in detail.

Apart from this database, consumers from some other online business communities were also

tapped for data collection. The consumers of these communities were contacted through

professional networks like LinkedIn. Personal visits were made to locally available consumers in

order to gather the information through hard copy questionnaires. Thus data was collected

through hard copy questionnaires and also through the internet.

The sampling techniques involved “online intercept random sampling” as well as “snow ball

sampling”. In online intercept sampling, consumers to these communities were intercepted and

given an opportunity to participate in the survey. For a consumer to fill the questionnaire, the

criteria were (a) The consumer should have been a member of an online business/product

community for at least 1 year (b) The consumer should have a minimum frequency of

participation of at least once a month. The consumers who fulfilled the above criteria were

further selected on the basis of simple random sampling technique. Nevertheless, randomization

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134 Ph.D. Thesis

improved representativeness and discouraged multiple responses from the same respondent. In

order to increase the number of respondents in the survey and to improve the overall response

rates, up to two additional reminder invitations were typically mailed at two-to-four-days

intervals to those respondents who fulfilled the defined criteria but had not yet participated in

the survey.

I also used snow ball sampling technique, which is a non probability sampling technique for data

collection. Personal messages were mailed to certain distinguished consumers who depicted high

status level in these online communities to provide some referrals. The link of the questionnaire

was mailed to these referrals requesting them to fill the questionnaire. This process was carried

out in waves by obtaining referrals from referrals, thus leading to a snow balling effect. Even

though probability sampling is used to select the initial respondents, the final sample is a non

probability sample. It also results in relatively low sampling variance and costs.

In total, the questionnaire was sent to 300 online community consumers, of which 219

responded, thus making the response rate to be 73%. 19 questionnaires were discarded due to

incomplete information. 200 fully completed questionnaires were considered for the study. From

the 200 respondents, in total, 129 (64.5%) respondents were male and 71(35.5%) were female.36

(18%) of the respondents were under 25 years old, 51 (25.5%) were between 25 and 35, 72

(36%) were between 36 and 45 and 41 (20.5%) were older than 46.

3.10.2. Phase II of primary data collection (For Co-creation model using INV based on

Metcalf Law (C-INV))

The data collection for calculation of INV values using the framework created has been done

across communities of four companies, namely Apple (Apple I Pad), Cisco (Cisco Collaboration

Community), Dell (Ideastorm) and Microsoft (Microsoft Dynamics CRM) using “online

intercept random sampling Technique”. These companies are the only ones where information

with regard to the last date of consumer’s participation was available to facilitate online intercept

sampling technique. The participants to these communities were intercepted and given an

opportunity to participate in the survey. The criteria for participating in the survey was a status

level of 5 and a point range of 50,000 and above. The seven questions of RI-1 (participation,

emotional attachment, online trust, commitment, member loyalty, attitude towards switching and


135 Ph.D. Thesis

period of association with the network) were filled again by the participants who fulfilled the

above mentioned criteria from these specific four communities, for conducting the study of INV

based on Metcalf law. The data was primarily collected through a period of six months starting

from July 2011 till December 2011. As it was not possible to build one to one contact with all the

online community participants, a sample size of 50 participants of one community was chosen

for the study.

The participants who fulfilled the above criteria were further selected on the basis of simple

random sampling technique. Nevertheless randomization improved representativeness and

discouraged multiple responses from the same respondent. In order to increase the number of

respondents in the survey and to improve the overall response rates, up to two additional

reminder invitations were typically mailed at two-to-four-days intervals to those respondents

who fulfill the defined criteria but have not yet participated in the survey.

Finally the data collection was accomplished from 50 participants for each of the four

communities. In total, 100 participants from each community were requested to complete the

survey. Some questionnaires from each community were incomplete, so they were discarded. 50

fully completed questionnaires from each community were considered for the study.

Thus, for each consumer, we were able to calculate the respective values for Participation (Pi),

Emotional Attachment (EAi), Online trust (OTi), Commitment (C¡), Member loyalty (ML¡) and

attitude towards switching (AS¡) which was the summation for each variable, across each of the

determinants, for 200 consumers on the basis of the framework mentioned in Section 3.8.3

(Table 4.27, Annexure III).

Framework for calculating Period of Association with the network (PAN¡) for each

consumer: The consumers were asked to identify the time span of consumer-product association

which would prevent them from switching to another product or brand. There were 5 categories

of Period of Association (Less than 1year, 1-2 years, 2-3 years, 3-4 years and more than 4

years).The number of consumers who opted for the respective categories were summed up

(∑CPAN¡). Further, the PAN¡ index for each consumer was calculated by using the formula

[(∑CPAN¡)/50] for each of the respective four communities.

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136 Ph.D. Thesis


The data was collected across 200 consumers from the 40 online product communities of Apple

using online intercept sampling technique. In these communities of Apple, the information with

regard to the last date of consumer’s participation was available to facilitate online intercept

sampling technique. The participants were intercepted and given an opportunity to participate in

the survey.

The pre-requisite for a consumer to fill the questionnaire was:

(a) The consumer should have been a member of an online business/product community for at

least 1 year.

(b) The consumer should have a minimum frequency of participation of at least once a month.

3.12. Community Managers

Community managers serve as Community Advocates by representing the customer. This

includes listening, which results in monitoring, and being active in understanding what

customers are saying, in both the corporate community as well as external websites. They also

engage customers by responding to their requests and needs or just conversations, both in private

and in public. As a Brand Evangelist, the community manager promotes events, products and

upgrades to customers by using traditional marketing tactics and conversational discussions. The

community managers also gather community feedback for future product and services.

Perhaps the most strategic of all tenets, community managers are responsible for gathering the

requirements of the community in a responsible way and presenting it to product teams. This

may involve formal product requirement methods from surveys to focus groups, to facilitating

the relationships between product teams and customers. The opportunity to build better products

and services through this real-time live focus group are ripe. In many cases, customer

communities have been waiting for a chance to give feedback. Every network has an underlying

purpose and motivations for such network creation include: Mission, Business, Idea, Learning or

Personal. The Community Manager holds the collective vision to create and manage

relationships, manage collaborative processes, community avocation and brand

ambassadorship.


137 Ph.D. Thesis


The third research instrument RI-3 was administered to online community managers. The

community managers were asked to rate their respective online communities with respect to the

community dynamics, as also the ability of the communities to support the process of co-creation

within the community. Ability of the community to serve as a collaboration enabler, as also its

ability to cater to the process of Customer Relationship Management were measured. The

community managers were also asked to rate their communities with respect to the ability of the

community to provide a good ROI as also the aspects of Customer Lifetime Value.

In 2008, Jeremiah Owyang (Web strategist, Altimeteer Group, Research Analyst, Forrester 2008)

began maintaining a list of online community managers employed by large corporations (a

Fortune 5000 company or over 1,000 employees).

To fill Research Instrument RI-3, I have used this database of online community managers

(http://www.web-strategist.com/blog/2008/06/20/list-of-social-computing-strategists-and-

community-managers-for-large-corporations-2008/).

The contacts were established through LinkedIn/personal visits/and company websites and

communities. Community managers were contacted across Technology, Gaming, Consumer

Goods, Agriculture, Health, Education categories from the above mentioned database.

The Community Managers are listed in the following Table:

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138 Ph.D. Thesis

Table: 3.12: Database of Online Community Managers S.No. Sector Community Manager

1. Technology

Lionel Menchaca, Community Manager, Dell

Anton Chiang, Web Communities Manager,

Juniper Networks

Lacy Kemp, Social Media Communications

Specialist at Real Networks

Stephen Spector, Sr. Program Manager,

Xen.org Community, Citrix

Michael Sandoval, Global Communities

Manager, Texas Instruments

Vishal Ganeriwala, Sr. Manager of Citrix

Developer Network, Citrix

Amie Paxton, Channel Community Manager,

Dell

Angela LoSasso, Community & blogs

strategist, HP

Tom Diederich, Social Media/Web Community

Manager, Cadence Systems

Bill Pearson Bill, Manager, Intel Software

Network, Intel

Josh Hilliker, Community Manager of the vPro

Expert Center, Intel

Robyn Tippins, Community Manager, Yahoo!

Developer Network at Yahoo!

John Summers, Community Manager at

NetApp

Mario Sundar, Community Evangelist at

LinkedIn

Tom Ablewhite, Community Manager,

Thomson Reuters

Craig Cmehil, Community Manager for the

SAP Developer Network

Lou Ordorica, Social Media Producer at Sun


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Microsystems

John Earnhardt, Senior manager, media

relations and blogger in chief, Cisco Systems

Deirdre Walsh, Community Manager at

National Instruments

Rachel Luxemburg, Community Manager at

Adobe

Aaron Tersteeg, Software Developer

Community, Intel

Josh Bancroft, Software Developer

Community, Intel

Jeff Moriarty, Software Developer Community,

Intel

Cathy Ma, Yahoo Community Manager, Yahoo

Europe

Shashi Bellamkonda, Social Media Swami,

Network Solutions

Ian Kennedy, Product Guy, MyBlogLog,

Community Manager, Yahoo

David Kim, Manager, Online Marketing and

Communities at Symantec

Marilyn Pratt, Community Evangelist, SAP

Labs

Scott Jones, Community Manager and Content

Strategist, SDN at SAP Labs

Badshah Mukherji, Sr. Community Manager at

VMware

Jon Mountjoy, Community Manager & Editor-

In-Chief at Salesforce

Senior Director, OTN & Developer Programs

Oracle

Jake Kuramoto, Oracle Apps Labs, Oracle

USA

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140 Ph.D. Thesis

Kelly Feller, Web Marketing Manager leading

the IT Community site Open Port, Intel

Erica Kuhl, Sr. Producer & Community

Manager, Salesforce.com Community

Aaron Tersteeg, Community Manager (Multi-

core Development) Intel Software Network,

Intel

Jeff Moriarty, Community Manager (mobility)

for the Intel Software Network, Intel

Alison Bolen Editor, Sascom voices blog, SAS

Melissa Daniels, Community Manager for All-

Star group for Yahoo! Messenger, Yahoo!

Amy Barton, Strategic Programs Manager,

Intel Software Network, Intel

Holly Valdez, Community Manager, Cisco, the

WebEx Technology group

2. Electronics

Ray Haddow, Blogger Outreach, Nokia

Charlie Schick, Lead on Nokia corporate blog,

Nokia

3. Media, Gaming,

Entertainment

Kellie Parker, Online Community Manager at

Sega

Kristopher Shaw, Community Manager at MTV

Networks UK

EM Stock, Senior Community Manager at Sony

Online Entertainment

Katie Hamlin, Community Manager,

Fodors.com, Random House

Justin Korthof, Community Manager at

Microsoft

David Cushman, Digital Development Director,

Bauer Consumer Media UK

Laurent Courtines, Community Manager at

Games.com AOL


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4. Research John Cass, Online Community

Manager, Forrester Research

5. Finance

Scott Moore, Senior Online Community

Manager at Schwab Learning

Jose Antonio Gallego, Community Manager at

BBVA (Spain)

Amy Worley, Director, Marketing Manager,

HR Block

Fran Sansalone, Community Manager for the

Open Calais Web Service, Thomson Reuters

6. Automotive

Karen Spiegler, Community Manager,

Edmunds.com, Inc.

Alicia Dorset, Blog editor, General Motors

7. Retail

Slaton Carter, Online Community

Development Manager, Whole Foods Market

Winnie Hsia, Online Community Moderator,

Whole Foods Market

8. Consumer Goods Jennifer Cisney, Chief Blogger, Kodak

9. Agriculture

Christopher Paton, Social Media Team Lead,

Monsanto

In addition to the above database this research instrument was also filled by some eminent

industry experts in this domain. The contacts were established through LinkedIn/ personal

visits/and company websites and communities. These were Jay Ehret: Marketing educator and

resource provider for entrepreneurs and small business owners, speaker, blogger, podcast host,

Gautam Ghosh: Platform Evangelist at Brave New Talent, Blogger, Speaker and Writer at

Gautam on Organisations 2.0, Ashok Krish: Head - Web 2.0 Innovation labs at Tata

Consultancy Services, Sampson Lee: President and Founder at Global CEM (Global Customer

Experience Management Organisation) , Andrew Calvert: Experienced professional delivering

measurable results in Leadership, Engagement, Sales and Customer Loyalty, Pradeep Gairola:

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142 Ph.D. Thesis

COO at Applect Learing Systems Pvt. Ltd, Ashok Lalla: Marketing 3.0 Evangelist and

Consultant, Founder and Author of 'The Future Of Digital For Brands', Sujith Nair: Vice

President - Engineering at Info Edge India Ltd., Community manager from Shiksha.com.

3.14. Business Online Community Credibility Model (BOCC) using Linear Programming

The Business Online Community Credibility model (BOCC) was developed to measure the

credibility of an online community by incorporating the concepts of community dynamics, co-

creation, collaboration enabler, CRM goals, return on investment and customer life time value.

The model uses a numeric weighting technique to calculate a business online community

credibility score, using an Evaluation Grid 4 in Annexure I. The model further uses the linear

programming technique to maximise the BOCC score and further to determine an optimal

solution for the variables contributing to the BOCC score. The results are discussed under

Section 4.6.6. in Chapter number 4 on Results and Findings.

3.15. Tools for Data Analysis

Statistical Package for Social Sciences (SPSS) version 17.0 was used for statistical analyses of

the collected and tabulated data. The following statistical techniques have been used for analyses

across all the three research instruments. A brief description of these techniques is as follows-

3.15.1. Correlation

It is the most widely used statistic method for summarizing the strength of association between

two metric (interval or ratio scaled) variables, say X and Y. It is an index used to determine

whether a linear, or straight–line relationship exists between X and Y. It indicates the degree to

which the variation in one variable, X, is related to the variation in another variable, Y. The

correlation coefficient r is also known as Pearson correlation coefficient as it was originally

proposed by Karl Pearson. It is also referred to as simple correlation, bivariate correlation or

merely the correlation coefficient. Often correlation analysis is used in conjunction with

regression analysis to measure how well the regression line explains the variation of the

dependent variable, Y. Correlation can also be used by itself, however to measure the degree of

association between two variables.


143 Ph.D. Thesis

3.15.2. Regression

Regression analysis is a powerful and flexible statistical procedure for analyzing associative

relationships between a metric dependent variable and one or more independent variables. It can

be used in the following ways: 1. Determine whether the independent variables explain a

significant variation in the dependent variable. 2. Determine how much of the variation in the

dependent variable can be explained by the independent variables. 3. Determine the structure or

form of the relationship. 4. Predict the values of the dependent variable. 5. Control for other

independent variables when evaluating the contributions of a specific variable or set of variables.

Although the independent variables may explain the variation in the dependent variable, this

does not necessarily imply causation. The use of the terms dependent or criterion variables, and

independent or predictor variables, in regression analysis arises from the mathematical

relationship between the variables. These terms do not imply that the criterion variable is

dependent on the independent variables in a causal sense. Regression analysis is concerned with

the nature and degree of association between variables and does not imply or assume any

causality. The mathematical equation is derived in the form of a straight line by using the least –

squares procedure. When the regression is run on standardized data, the intercept assumes value

of 0, and the regression coefficients are called beta weights. The strength of association is

measured by the coefficient of determination, r 2.

Further it is the operation of learning a function that predicts the value of a real valued

dependent variable based on the values of other independent variables. It is a technique used for

the modelling and analysis of numerical data consisting of values of a dependent variable and of

one or more independent variables. The dependent variable in the regression equation is

modelled as a function of the independent variables, corresponding parameters (constants), and

an error term. The error term is treated as a random variable and represents unexplained variation

in the dependent variable. The standard error of estimate is used to assess the accuracy of

prediction and may be interpreted as a kind of average error made in predicting the dependent

variable from the regression equation. Parameters are estimated to give a "best fit" of the data.

Most commonly, the best fit is evaluated by using the least squares method, but other criteria

have also been used. Regression can be used for prediction (including forecasting of time-series

data), inference, hypothesis testing, and modelling of causal relationships.

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144 Ph.D. Thesis

3.15.3. Prediction

Prediction is the same as classification or estimation, except that the records are classified

according to some predicted future behaviour or estimated future value. In a prediction task, the

only way to check the accuracy of the classification is to wait and see. The primary reason for

treating prediction as a separate task from classification and estimation is that in predictive

modelling there are additional issues regarding the temporal relationship of the input variables or

predictors to the target variable.

3.15.4. Factor Analysis

Factor analysis, also called exploratory factor analysis (EFA), is a class of procedures used for

reducing and summarizing data. Each variable is expressed as a linear combination of the

underlying factors. Likewise, the factors themselves can be expressed as linear combinations of

the observed variables. The factors are extracted in such a way that the first factor accounts for

the highest variance in the data, the second the next highest, and so on. In formulating the factor

analysis problem, the variables to be included in the analysis should be specified based on past

research, theory, and the judgement of the researcher. These variables should be measured on an

interval or ratio scale Factor analysis is based on a matrix of correlation between the variables.

The appropriateness of the correlation matrix for factor analysis can be statistically tested. The

two basic approaches to factor analysis are principal components analysis and common factor

analysis. I have used the principal component analysis method. Here the total variance in the data

is considered. The diagonal of the correlation matrix consists of unities, and full variance is

brought into the factor matrix. Principal component analysis is recommended when the primary

concern is to determine the minimum number of factors that will account for maximum variance

in the data for use in subsequent multivariate analysis. The factors are called principal

components. In principal component analysis the initial or unrotated factor matrix indicates the

relationship between the factors and individual variables, it seldom results in factors that can be

interpreted, because the factors are correlated with many variables. Therefore rotation is used to

transform the factor matrix into a simpler one that is easier to interpret. The most commonly

used method of rotation is the varimax procedure, which results in orthogonal factors. The

rotated factor matrix forms the basis for interpreting the factors. Factor scores can be computed

for each respondent. Alternatively, surrogate variables may be selected by examining the factor


145 Ph.D. Thesis

matrix and selecting for each factor a variable with the highest or near highest loading. The

differences between the observed correlations and the reproduced correlations, as estimated from

the factor matrix, can be examined to determine model fit. The principal components method of

extraction was used for data reduction. Components with Eigen values greater than 1 were

extracted. As the communalities were all high, the extracted components represented the

variables well.

3.15.5. Simplex Tableau Method of Linear Programming

The linear programming method is a technique for choosing the best alternative from a set of

feasible alternatives, in situations in which the objective function as well as the constraints can

be expressed as linear mathematical functions. The simplex tableau method of linear

programming provides an efficient technique which can be applied for solving LPPs of any

magnitude involving two or more decision variables. In this technique, the objective function is

used to control the development and evaluation of each feasible solution to the problem. The

simplex algorithm is an iterative procedure for finding, in a systematic manner, the optimal

solution to a linear programming problem. This method, according to its iterative search selects

this optimal solution from among the set of feasible solutions to the problem. The algorithm is

indeed very efficient because it considers only those feasible solutions which are provided by the

corner points, and that too not all of them. Thus by using this technique we have to consider a

minimum number of feasible solutions to obtain an optimal one. Also, this technique has the

merit to indicate whether a given solution is optimal or not. For applying simplex method to the

solution of an LPP, first of all an appropriately selected set of variables is introduced into the

problem. The iterative process begins by assigning values only to these variables and the primary

(decision) variables of the problem are all set equal to zero. This assumption is analogous to

starting the evaluation process in the graphic approach at the point of origin, where both x1 and

x2 are equal to zero. The algorithm then replaces one of the initial variables by another variable –

the variable which contributes most to the desired optimal value enters in, while the variable

creating the bottleneck to the optimal solution goes out. This improves the value of the objective

function. This procedure of substitution of variables is repeated until no further improvement in

the objective function value is possible. The algorithm terminates there indicating that the

optimal solution is reached, or that the given problem has no solution. For the purpose of this

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146 Ph.D. Thesis

research thesis, linear programming has been performed using an online tool for the simplex

tableau method (http://www.zweigmedia.com/RealWorld/simplex.html).

3.15.6. Numeric Weighting Technique

It is a statistical adjustment to the data in which each case or respondent in the data base is

assigned a weight to reflect its importance, relative to the other cases or respondents. The value

1.0 represents the unweighted case. The effect of weighting is to increase or decrease the number

of cases in the sample that possess certain characteristics. It is most widely used to make the

sample data more representative of a target population on specific characteristics. For example, it

may be used to give greater importance to cases or respondents with higher quality data. Yet

another use of weighting is to adjust the sample so that greater importance is attached to

respondents with certain characteristics.

3.15.7. Datamining for Customer Relationship Management

Datamining techniques are algorithms and methods used to carry out datamining tasks. They

differ from each other in type of data handled, assumptions about the data, scope and

interpretation of the output. Analysis of large quantities of data require approaches that are very

different from the traditional data analysis approaches and this has given birth to the field of

Knowledge Discovery in Databases (KDD), more popularly known as Datamining. Knowledge

Discovery in Databases is a non-trivial process of identifying valid, novel, potentially useful and

ultimately understandable patterns in data, (Fayyad, U., Gregory, Piatetsky S., Padhraic S.,

2007). Others look at data mining in terms of a set of tools and techniques that operate on and

extract implicit patterns from data. Knowledge Discovery and Data Mining (KDD) is an

interdisciplinary area focusing upon methodologies for extracting useful knowledge from data

for Business Intelligence. The ongoing rapid growth of online data due to the Internet and the

widespread use of databases has created an immense need for KDD methodologies. The

challenge of extracting knowledge from data draws upon research in a wide variety of fields to

draw upon tools that can synthesize and organize knowledge on any given topic of interest from

a corpus of documents. There is an increasing realization that effective customer relationship

management can be done only based on a true understanding of the needs and preferences of the

customers. Under these conditions, data mining tools can help uncover the hidden knowledge


147 Ph.D. Thesis

and understand a customer better, while a systematic knowledge management effort can channel

the knowledge into effective marketing strategies. This makes the study of knowledge extraction

and management particularly valuable for marketing and customer relationship management,

(Shaw, M.J., Subramaniam, C., Tan, G.W. and Welge, M.E., 2001).

Datamining tools and Techniques operate on large databases and extract patterns that are implicit

in them, resulting in actionable information. Datamining can be directed or undirected. Directed

datamining attempts to explain or categorize some particular target field such as income or

response. Undirected datamining attempts to find patterns or similarities among groups of

records without the use of a particular target field or collection of predefined classes.

Customer understanding is the core of CRM. It is the basis for maximizing customer lifetime

value, which in turn encompasses customer segmentation and actions to maximize customer

conversion, retention, loyalty and profitability. Proper customer understanding and actionability

lead to increased customer lifetime value. Incorrect customer understanding can lead to

hazardous actions. Similarly, unfocused actions, such as unbounded attempts to access or retain

all customers, can lead to decrease of customer lifetime value (law of diminishing return).

Hence, emphasis should be put on correct customer understanding and concerted actions derived

from it.

In view of the above, corporates commenced accumulation of wide spectrum of consumer data

viz. transaction data, customer databases based on consumer behaviour and purchase transactions

and in this context, creation of data warehouses. But, due to lack of appropriate tools and

techniques to analyze these huge databases, a wealth of customer information and buying

patterns was permanently hidden and unutilized in such databases. But, memory is of little use

without intelligence. The central idea of datamining is that data from the past contains

information that will be useful in the future. It works because consumer behaviours captured in

corporate data are not random, but reflect the differing consumer needs and preferences.

Knowledge-based marketing, which uses appropriate datamining tools and knowledge

management framework, addresses this need and helps leverage knowledge hidden in databases.

Customer profiling is one of the major areas of the application of data mining for knowledge-

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148 Ph.D. Thesis

based marketing. This is of relevance because consumer behavioural data is a more valuable

source of information than consumer demographics data.

3.15.8. Clustering

Cluster Analysis, also called data segmentation, relates to grouping or segmenting a collection of

objects (also called observations, individuals, cases, or data rows) into subsets or “clusters”, such

that those within each cluster are more closely related to one another than objects assigned to

different clusters. Cluster analysis uses a number of techniques of sorting individuals into similar

groups, (J.Saunders, 1980). Hence, objects in a cluster are similar to each other. They are also

dissimilar to objects outside the cluster, particularly objects in other clusters, (Ahuja V., Medury,

Y., 2011). Cluster analysis is also called classification analysis or numerical taxonomy.

Clustering algorithms function such that intracluster similarity is the maximum and the inter-

cluster similarity is minimum. Clustering also has applications in the field of marketing

segmentation. What distinguishes clustering from classification is that clustering does not rely on

predefined classes. In classification, each record is assigned a predefined class on the basis of a

model developed through training on preclassified examples.

The following statistics and concepts are associated with cluster analysis

Agglomeration schedule: It gives information on the objects or cases being combined at

each stage of a hierarchical clustering process.

Cluster centroid: The cluster centroid is the mean values of the variables for all the

cases or objects in a particular cluster.

Cluster centers: They are the initial starting points in non hierarchical clustering.

Clusters are built around these centers or seeds.

Cluster membership: Cluster membership indicates the cluster to which each object r

case belongs.

Dendrogram: A dendrogram or tree graph, is a graphical device for displaying clustering

results.

Distances between cluster centers: These distances indicate how separated the

individual pairs of clusters are Clusters that are widely separated are distinct, and

therefore desirable.


149 Ph.D. Thesis

There are two major methods of clustering-hierarchical clustering and k-means clustering.

3.15.8.1. Hierarchical Clustering

This is a statistical method for finding relatively homogeneous clusters of cases based on

measured characteristics. It starts with each case in a separate cluster and then combines the

clusters sequentially, reducing the number of clusters at each step until only one cluster is

left. When there are N cases, this involves N-1 clustering steps, or fusions. This hierarchical

clustering process can be represented as a tree, or dendrogram, where each step in the clustering

process is illustrated by a join of the tree.

3.15.8.2. K-means Clustering

The clustering algorithm is initiated by creating k-different clusters and subsequently the

distance measurement between each of the sample, within a given cluster, to their respective

cluster centroid is calculated, (Berry, Michael, J. A., Linoff, Gordon S., 2007). We use Euclidean

distance measure for our study. After obtaining initial cluster centers, the procedure, (i) Assigns

cases to clusters based on distance from the cluster centers and (ii) Updates the locations of

cluster centers based on the mean values of cases in each cluster. These steps are repeated until

any reassignment of cases would make the clusters more internally variable or externally similar.

The initial cluster centers are the variable values of the K well-spaced observations. The final

cluster centers are computed as the mean for each variable within each final cluster. The final

cluster centers reflect the characteristics of the typical case for each cluster.

3.15.9. Consumer Profiling

Consumer Profiling is one of the major areas of the application of data mining for knowledge-

based marketing.Consumer profiling involves creating consumer models, based on which a

marketer can decide on the right strategies and tactics to meet the needs of the customer.

Profiling is an innate tool used for consumer behaviour and preference prediction. Consumer

profiles can be formed based on their purchase data or any other behavioural data.

3.16. Research Models-Summary of Data Analysis Tools and Procedures

Using the CRM concept of response modelling, four specific models have been developed during

the entire research study. These are-

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150 Ph.D. Thesis

1. Consumer Trustworthiness Regression Model using Netnography (CTR) - Under this

model the studies are conducted on a set of online communities of Apple and correlation

and regression model is applied for forty product categories. The results of a regression

model are used to analyze factors contributing to the growth of trust in an online

community and for finding out the contribution of the independent variable, that is,

number of points to the dependent variable, that is, number of views. The information is

subsequently applied for prediction by analyzing how far the dependent variable depends

on the independent variable. The same techniques are applied on the data for users on the

online community of Dell.

2. Co-creation Model using INV based on Metcalf Law (C-INV) - Under this model the

values of INVi (Table 4.27, Annexure III) were subjected to Hierarchical cluster

analysis using SPSS 17.0 and five consumer clusters were identified. Based on Table

4.27, Annexure III the profiles of the 200 consumers divided into five clusters were

identified and appropriate targeting strategies were formulated (Table 4.28, Section 4.3).

3. Consumer Price Sensitivity Model using k-means cluster analysis (CPS) - This model

uses data collected from the Apple Online Discussion Forums

(discussions.apple.com/category.jspa?categoryID=204).Apple has over 40 product

communities formulated for consumer engagement, interaction, feedback, building strong

online consumer trust and reciprocity. The consumer profiling was done on the data

collected using K-means clustering and cluster memberships were extracted and the most

significant consumers across all three product categories were identified. Further, the

consumer segmentation was performed and a detailed profile for most significant

consumer clusters for each price category were created (Table 4.40, Section 4.5).

4. Business Online Community Credibility Model (BOCC) using Linear Programming-

The model uses a numeric weighting technique to calculate a business online community

credibility score. The model further uses the linear programming technique to maximise

the BOCC score and further to determine an optimal solution for the variables

contributing to the BOCC score (Table 4.50, Section 4.6.6).

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