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A SOCIAL EXCHANGE THEORY APPROACH TO WEB-BASED
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A SOCIAL EXCHANGE THEORY APPROACH TO WEB-BASED
DATA COLLECTION AS A PART OF CRM EFFORT

By

Harshavardhan Gangadharbatla

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Master of Arts

Department of Advertising
2002

ABSTRACT

A SOCIAL EXCHANGE THEORY APPROACH TO WEB-BASED
DATA COLLECTION AS A PART OF CRM EFFORT

By

Harshavardhan Gangadharbatla

Web-based data collection is an important part of customer relationship
management (CRM) efforts. The purpose of this thesis is to explore and determine the
conditions in which consumers are willing to share personal information on the Web.
Customers usually take part in web-based data collection for several reasons such as
being an existing customer, for discounts/coupons, free-trail of products or simply out of
their interest. The customer-firm relationship is considered from a social exchange point
of view and is analyzed using the various aspects of social exchange including
comparison levels, investment model, trust and uncertainty. Variables such as expected
outcomes/return, actual outcome/retum, type of benefits, nature of benefits, vested
interest of consumer, trust and uncertainty are considered in hypothesis formulation. A
survey of a random sample of consumers (n=99) is conducted to test the hypotheses.
Results indicate that the conditions under which consumers may positively respond to
such Web-based data collection are a) the benefits offered exceed their expectations, b)
the customer has a vested interest in the company, c) customer has no doubts about the
delivery of these benefits, and d) the benefits are immediate in nature rather than delayed.
Also, e) the longer the delay in receiving the benefits the less likely the customer is to

provide the required information. Managerial implications are drawn from the findings.

ACKNOWLEDGEMENTS

I wish to thank everyone who gave their valuable time, knowledge and
enthusiasm to make this thesis possible, especially my advisor, Dr. Hairong Li for his
constant encouragement, support and advice. For their critical analysis and very helpful
feedback, I wish to thank each of my committee members, Dr. Steve Edwards and Dr.
Keith Adler. I also would like to thank Dr. Shoba Ramanand, Alice Lin and Mr. Dave
Regan for their help during data collection.

Finally, I would like to thank my dad, Mr. Venkateswara Prasad who passed on
the genes of persistence and hard work to me. Before I forget in a hurry, my deepest

gratitude goes to Ms. Priya Santhanam for not bothering me too much during this period.

on.

TABLE OF CONTENTS

LIST OF TABLES ................................................................................... v

LIST OF FIGURES ................................................................................ vi

INTRODUCTION 1
Customer Relationship Management ...................................................................................................... 3
Web-based data collection as a CRM effort ............................................................................................ 6

LITERATURE REVIEW 10
The Communication Theory of Social Exchange ................................................................................. 10
Comparison levels ................................................................................................................................. 10
Investment model .................................................................................................................................. l 1
Trust and Uncertainty in relationships .................................................................................................. 12

CHAPTER 3 14
HYPOTHESES l4

METHODS 19
Pretest .................................................................................................................................................... 19
Final Survey .......................................................................................................................................... 20

Variables ........................................................................................................................................... 21
Participants ........................................................................................................................................ 22

RESULTS 23
Hypotheses testing ................................................................................................................................ 23

SUMMARY AND CONCLUSIONS 31
Discussion ............................................................................................................................................. 3 1
Limitations ............................................................................................................................................ 32
Implications ........................................................................................................................................... 34

References 43

LIST OF TABLES

TABLE 1: Attributes and their levels ................................................. 22
TABLE 2: Correlations ................................................................. 24
TABLE 3: Regression Analysis ....................................................... 25
TABLE 4: Scenarios and their aggregate ratings ................................... 26
TABLE 5: Parameter Estimates (Part-Worth) ....................................... 27

LIST OF FIGURES

FIGURE 1: Willingness Versus Information Requested ............................. 28
FIGURE 2: Willingness Versus Amount .............................................. 28
FIGURE 3: Willingness Versus Delay in receiving the amount .................... 29

vi

CHAPTER 1

INTRODUCTION

The customer is the king and she is always right.

In the beginning, there was an era when small shops hustled with business. There
were no chain stores, no national TV advertising and the concept of marketing never
existed. People had to deal directly with local shopkeepers who provided them
personalized service if the customers were loyal. In essence, the small-scale shopkeepers
had the ability to remember everything about the individuals who shopped with them,
their demographics, their preferences and even their key life events. They were good at
analyzing this information to determine the pricing and discounting, and at making other
major decisions in order to provide personalized service to their customers. They were
also good at presenting offers to these customers who in turn became ‘loyal’ to them. As
time passed, these customers grew in numbers and businesses flourished. Soon it became
practically impossible for any individual shopkeeper to keep track of her ‘numerous’
customers. The trend seemed to move towards ‘mass marketing’.

After the World War H, firms had better access to mass production technologies,
better transportation and communication facilities, greater financial resources, and better
human resource management (cf. Carson 1967; Mallen 1975). Mass marketing and mass
advertising through print, radio and television seemed more attractive compared to the
small local shopkeeper’s customer service strengths. Customers were satisfied with mass
produced and standardized products at reasonable prices thereby intensifying the
competition at local and regional levels. This caused the local shopkeepers to integrate

into a unified market system with mass distribution and communication, shifting the

focus to promoting, pricing and distributing products. The emphasis was on products,
leading companies to adopt organizational forms that were centered on products (Sloan
1963). These mass-marketing techniques soon proved to be less effective as the number
of players in the market increased. Firms started paying more attention to markets than
products.

The concept of marketing was first recognized in 19505. McKitterick (1957),
Borch (1975) and Keith (1960) formulated the tenets of marketing and McCarthy (1960)
introduced the concept of 4Ps namely product, price, placement and promotion that were
popularized by Kotler (1967). Soon strategies of segmentation and niche marketing
followed and researchers began concentrating on finding rational and more precise
adjustments to products and marketing efforts to better understand the consumer or user
requirements (of. Smith 1956). As we enter the let century, the convergence of
demographics and technology as well as the dissatisfaction with present marketing
productivity seem to increase the degree of focus at the consumer considerably.

This sudden change of trend that focuses on the customer can be vastly attributed
to the increase in competition and the demassification of media. The advent of the
Internet has also a major role in making mass media less effective (Godin 1999). There
has been a discemable shift in the power from firms to customers. The Internet gives
customers the unlimited access to information like never before (Romm and Sudweeks,
1998; Tapscott, 1998). Customers seem to know not just what they want but also how
and when they want it. Every business is now under this tremendous pressure to cater to
the needs of a more sophisticated consumer with rising demands and expectations. Firms

are using the same Internet as a powerful tool to combine the personalized touch and

customized service with mass-marketing techniques of selling to millions. Thus with the
help of the Internet and the concept of Customer Relationship Management it is now
possible to give the customer a personalized service like the local shopkeepers of the past

without compromising on any of the advantages of mass marketing.

Customer Relationshh) Mmgement

The Gartner Group coined the term Customer Relationship Management
popularly known as CRM and it encompasses sales, marketing, customer-service, and
support applications. CRM is the buzzword of contemporary marketing. According to
Aberdeen Group’s report, the total CRM market in 1999 was $8 billion and is predicted
to grow to a $24 billion by 2003 (Aberdeen Group, 2000). Some of the organizations like
[DC have been a little less optimistic and put the worldwide CRM applications market to
reach a $14 billion in revenue by the end of 2005 (Hoke Communications, 2001). One
has to first understand the concept of Relationship Marketing before defining CRM.
There are many definitions for Relationship Marketing floating around these days (Sheth
and Parvatiyar 2000). Three major points that bring out the uniqueness of this concept are
that it is a one-to-one relationship between the marketer and the customer, it is an
interactive process and not a transaction exchange and finally it is a value-added activity
through a mutual collaboration and interdependence between the firm and the customer
(Sheth and Parvatiyar 2002). In the ongoing debate over relationship marketing evolving
into a separate discipline Seth and Paravatiyar (2000, 2002) identified seven areas that
could potentially be looked at to help relationship marketing evolve into a discipline. The
areas that they suggested are delimiting the domain for relationship marketing, agreeing

on a definition that brings out the uniqueness of the concept, building respectable

databases, developing performance metrics, employing longitudinal research methods,
encouraging research scholars to publish more in top journals and finally developing an
explanatory theory. (Seth and Parvatiyar, 2002).

Thus, originating from this unique concept of relationship marketing CRM serves
the purpose of bringing mutual value for both parties in a relationship. As explained
earlier, the concept of CRM is not a new one. It has been in action since the time of local
shopkeepers. What was then a tactic has now become a strategy. Early adopters of this
mantra have not probably realized this and up to 70%, 80% or even 90% of the
companies that have ventured on to implement CRM hastily have failed (Chase 2001). So
the present focus of CRM research is on successful implementation of this strategy. Over
the past two years CRM gurus have laid over a series of steps to CRM success (Noyes A1
2000; Sweiger 2000; Lee Dick 2000; Bednarz Ann 2001; Murtha Kevin & Joe Foley
2001; CIO.com 2001). Even though CRM gurus varied in the number of steps, they
essentially focused on

1) Clearly defining the company’s objectives with respect to CRM and how they
plan to measure them.

2) Implementing customer centric business strategies (Lee 2000)

3) Creating various data collection points called touch points and unifying them into
one big data warehouse that gives the enterprise a total picture of Customer
Relationship for the first time (Sweiger 2000)

4) Using technology and software that support CRM strategies rather than drive it
(Lee 2000). Also choosing hi gh-end systems that allow for grth is very

important (Bednarz 2001 ).

The various steps mentioned above hold good for any traditional CRM
implementation. However, our focus in this thesis would be limited to B2C aspect of E-
Commerce driven CRM, also known as eCRM. The focus of CRM till now has been on
B2B customers because of obvious reasons like high average customer value. CRM has
been of late receiving a lot of attention in the business-to-consumer segment of E-
Commerce. One of the reasons why the business-to-consumer (B2C) aspect of E-
Commerce has attracted such great attention in the recent past is the fact that the BZC E-
Commerce market size was predicted to grow to a total of $200 billion by the end of the
year 2000 (Turban, Lee, King & Chung, 2000). This is expected to exceed $800 billion
by the end of the year 2003 (Schneider and Perry, 2000). Even though the economic
downturn in the United States reduced the pace of BZC growth, eMarketer predicts that ’
the number of people, age14 and over, who have purchased something online will grow
from 64.1 million in 2000 to over 100 million by 2003 in the United States (emarketer).
This when translated to US BZC E-commerce revenue, would mean $54.2 billion
(emarketer.com), $65 (Boston Consulting Group), $73.9 (Forrester Research) and $117
(Keenan Vision) just by the end of year 2001. Various research organizations may differ
in their estimates but they all concur on the fact that the BZC market is a rapidly growing
market. With the economy slowing down, there is also a lesser chance of seeing
acquisitions and mergers in the near future. What this means is that it would be really
difficult for businesses that are targeted to general consumers (B2C) to acquire new
customers through mergers and acquisitions. To put it in the words of Nelle Schantz, a
global strategist and CRM program director at Cary, a NC-based SAS institute, “now that

mergers have died down from the [pace of the] early mid-90$ it would be difficult for

businesses especially financial services and insurance providers to acquire new
customers. You have to grow and retain the existing customers.” In this highly
competitive BZC market, “acquiring new customers is much more expensive than
keeping them”(Stone et a1., 1996, p676). Not only this, loyal customers are far more
profitable to a firm than price-sensitive, deal-prone switchers (Page, Pitt, and Berton
1995; Reicheld 1996). Another reason why firms seek to establish long-term relationships
with consumers is that these relationships are far more difficult to understand, to copy or
to displace (Day, 1997).

The Internet is a very powerful customer relationship management medium tool. The
Web is a unique medium in that it allows for treating the customers according to their
individual needs and preferences with only a very little incremental cost (Noyes 2000). ‘
This is the main reason why we find a number of businesses investing a great deal in
promoting themselves and communicating with consumers on the Internet. More and
more companies are using the Internet to collect information about their customers and
build databases of customer profiles (Pardun and Lamb 1999). Let us now consider the

Web based collection employed by firms as a CRM effort.
Web-based data collection as a CRM effort

Both academicians and practitioners have been vociferous in pointing out the
importance of data about customers and its collection to the whole CRM process (Masci
1999; Zhang, Wang & Chen 2000; Abbott, Stone & Buttle 2001; Hall jr. 2001; Farah &
Hi gby 2001). Data about customers is often considered the most valuable differentiator
for competing firms (Meltzer 2000). It is very essential that the firm practicing CRM

know all about its customers and their habits and preferences if it plans to implement

CRM strategies positively. While all firms collect customer specific data, firms that
employ CRM collect not only more data but also organize it in a customer centric fashion
in a centralized CRM database to be made available to all front office personnel (K03,
2001). Information about customers prepares firms in predicting their customer’s needs in
a better way, which leads to more targeted and customized relationship strategies that
anticipate customer desires (Butler 2000). In fact, one of the first steps for any CRM
focused company is to profile and segment their customers (Meltzer 2000). In a
qualitative study done by Abbott, Stone and Buttle (2001), it has been found that
segmentation and targeting of customers based on clean and accurate data are the keys to
successful implementation of CRM regardless of the strategies employed.

A lot of firms implementing CRM turn to the Internet not just to establish their ‘
respective “Internet Presence Sites” but also to expand their customer information
databases through collection of data on their websites (Masci 1999). Given the fact that
as many as 90 million Americans use the Internet regularly (Federal Trade Commission,
2000) it should not surprise one to learn that about 68% of the websites attempt in some
extent to collect data from their website users to build a database of information about
prospective customers (Pardun & Lamb 1999). The percentage of commercial websites
that collect personal information in some form from users visiting their sites is about 97%
to 99% according to a recently concluded study by FTC (FTC 1998). The type of
information websites collect in order to build their databases for consumer research and
relationship marketing varies from gender, age, martial status, educational level,
ethnicity, occupation, household income, Internet usage profile to even buying patterns

(Turban, Lee, King and Chung 2000). Even though the novelty of the Internet is wearing

off, most businesses as well as consumers prefer online forms because they are fast and
easy (Harter 1999). Collection of data over the Internet eliminates the data entry step that
would be necessary if collected through mail or over the phone. It provides for data that
is already in a format that can be readily analyzed and used to profile existing customers
and target prospective ones.

However, without enough input from the customers little can be said from their
traditional demographic information. Most attempts to gather additional information
online are often rejected by Internet users because most customers are very concerned
about providing personal information or registering with a site sponsor (Camp 1999;
Caudill and Murphy 2000; O’Guinn, Allen and Semenik, 2000). Their concerns may be
privacy related (Turban et.al 2000; Schneider and Perry 2000; Miyazaki and Fernandez '
2000; Butler 2000; Milne 2000; Farah & Higby 2001) or simply time deficiency
(Kalakota and Whinston 1996). Often times complex questions that lack simplicity may
cause consumers to leave websites without completing the forms (Kalakota and Whinston
1996). A survey conducted by Zhang, Wang and Chen (2000, 2001) to measure
consumers’ responses to Web-based data collection efforts revealed that variables like
gender, age, occupation, cultural background and geographic distance may also impact
consumer’s willingness to provide information.

Researchers till now have limited themselves to establishing the effectiveness of
Web-based data collection and identifying the factors that influence these responses
through surveys. They have established that Web-based data collection efforts do pay off
and that businesses can safely continue their efforts of obtaining data online (Zhang,

Wang and Chen 2000). The factors Zhang et al., (2000) identified as most influential are

a) being an existing customer and b) discounts/coupons offered as incentive. These
factors can be thought of as an investment and a favorable outcome from the consumer’s
viewpoint. The consumer seems to disclose information if she has vested interest in the
firm or if she sees a favorable outcome of doing so. This brings to mind the social
exchange theory of relationships. So can we apply the communication theory of social
exchange to the relationship between firms (Web-based data collectors) and consumers
(online consumers)? The answer is yes and before we do so let us first see what the

communication theory of social exchange is all about.

CHAPTER 2

LITERATURE REVIEW

The Communication Theory of Social Exchange

Thibaut and Kelley (1952) proposed a theory called the Communication Theory
of Social Exchange that was based on the exchange of rewards and costs to quantify the
values of outcomes from different situations for an individual. The viability of social
exchange rests on the assumption that human beings recognize each other's life situations,
notice each other's needs, and in some ways are likely to engage in reciprocity - a
condition in which a response is correlated to the worth of the original message (Thibaut
and Kelley, 1952). Simply put, humans act with other humans in full recognition that
their acts will be noticed and in some way reciprocated (i.e., that they will receive a

return on their communicative investment).

Comparison levels

Social Exchange Theory (SET) can be conceptualized in terms of a 2X2 outcome
matrix that can be used to examine how people decide what to do in their relationships
(Roloff, 1981). The idea of totaling potential benefits and losses to determine human
behavior was put forward by the philosopher John Stuart Mill, as early as 1861, in his
famous book Utilitarianism. John Stuart Mill (1861) put forth the minimax principle that
claims that people seek to maximize their benefits and minimize their costs. According to
SET, people tend to think about their relationships in economic terms by totaling the
costs and then comparing it with the perceived benefits (West & Turner, 2000). There are

two standards of comparison by which one can evaluate a given outcome. The first is

10

called the comparison level (CL), which is the threshold above which an outcome seems
attractive (Thibaut and Kelley 1952; Roloff, 1981). Comparison level (CL) is related to
relative satisfaction — how happy or sad an outcome makes a member of the relationship,
which depends on expectation, which in turn depends on prior experience (Thibaut and
Kelley, 1952). The second standard to evaluate the outcomes of a relationship is called
the comparison level of alternatives (CLalt), which is essentially the worst outcome a
person will accept and still stay in a relationship (Thibaut and Kelley 1952; Roloff,
1981). CLalt relates to the stability of relationships and as more attractive outside
possibilities become available, or as existent outcomes slide below an established CLalt,

instability of the relationship increases (Thibaut and Kelley, 1952).

Investment model

We have seen how outcome-interdependence theory of Thibaut and Kelley (1959)
focuses on the rewards and costs derived from the relationship for the individual. Rusbult
(1980, 1983) extended this theory by bringing in a new component called investments.
According to this investment model, commitment is increased, not only by greater
satisfaction (meaning outcome greater than CL) and comparison level of alternatives
(CLalt) but also by investments. Rusbult (1980, 1983) defines investment as “the
resources one gives to the relationship that cannot be retrieved if the relationship were to
end.” Investments can be either intrinsic elements that are put directly into the
relationship 6. g. time, personal information disclosure or extrinsic elements that are in a
way connected to the relationship such as shared resources (Rusbult, 1983). Rusbult and
her colleagues have extended the investment model by examining consequences of

commitment and implications of the investment components for relationship-maintenance

11

processes (Rusbult & Buunk, 1993; Rusbult, Drigotas, & Verette, 1994). The effect of
investment and the implications of these investment components for the maintenance of

customer-firm relationships are discussed in the next section.
Trust and Uncertjintv in relationships

Trust is the most important factor in a social exchange and as mutual trust
flourishes so does the extent and commitment to the exchange (Chadwick-Jones 1976).
Trust in a relationship builds up gradually through cumulative commitment (Thibaut &
Kelley 1959; Blau 1964). A social relationship builds up slowly from minor transactions
that involve a little risk till a relationship with mutual trust is established (Chadwick-
Jones 1976). One of the most important differences between a social exchange and an
economic exchange as pointed out by Blau (1964) is that the former entails unspecified
obligations. Economic exchanges provide for specified obligations unlike social
exchanges where one has to trust others to discharge their obligations. The benefits
received have no exact value when it comes to social exchanges and unspecified
obligations. To put it in the words of Blau (1964), “It is not just the social scientist who
cannot exactly measure how much approval a given helpfirl action is worth; the actors
themselves cannot precisely specify the worth of approval or of help in the absence of a
money price.” It is therefore obvious that the returns in a social exchange cannot be
precisely specified or they cannot be bargained about, which is not the case of an
economic exchange:

The lack of trust or distrust is expected in economic exchanges, however it will
have a negative significance for social exchanges (Blau 1964). Anderson (1971) in his

study of kinship relations argues that lack of trust often leads to uncertainty that

12

influences people to be calculative and to seek shorter-term benefits from the
relationship. Although there may be a number of factors that make an individual’s
behavior uncertain in the future such as her health, financial security or availability of
resources, yet the uncertainty, that one may in the future be able to rely for support and
services from another, will be very critical if one has doubts about the other’s willingness
to reciprocate even if one means to do so (Chadwick-J ones 1976).

Among other factors that contribute to uncertainty in a relationship are doubts
about other’s ability to make a return, or the utility of it when it is made. Anderson
(1971) in his classic study comments “The longer the gap between service and
reciprocation, the greater is the uncertainty both about the other’s ability and about one’s
own needs. Furthermore, the greater the risk in terms of uncertainty about other’s ability
and one’s own future needs the greater ........... the greater will be the tendency to either
demand a large surplus in the future, or to demand instant reciprocation in the form of
some other desired source .......... to calculate the outcomes.” Where there is uncertainty
there will be calculation and preference for short-term returns; there will be an
abandonment of norms that encourage a person not to calculate at all or wait for returns

over the long-term (Anderson 1971).

13

CHAPTER 3

HYPOTHESES

The consumer and the firm can be considered as the two participating actors of a
relationship that has been developed over a period of time and is not just a one-time
purchase. Central to this relationship, as in every market relationship is an exchange
process where value is given and received (Day, 2000). The value firms receive from
such relationships is improved customer retention and thereby an increase in the revenue.
On the other side of the equation we have customers receiving preference based or
customized services. CRM research has been partial till now in concentrating only on the
benefits to the firm. Since we are talking about a relationship here, it becomes necessary,
to consider both parties. It is important to understand how the customer views the
relationship, not just by being a passive partner but by actively providing information
necessary for the healthy survival of this relationship. This thesis tries to look at the
relationship from the customer’s viewpoint, applying the communication theory of social
exchange, to establish the conditions under which the consumer will be more willing or
less reluctant to share personal information.

Before we apply the communication theory of social exchange to this relationship
we have to consider the following two aspects of this scientific theory, its explanatory
and predictive powers. It has explanatory power in that it predicts, “that individuals
minimize costs and maximize rewards within their relationships”(Thibaut and Kelley,
1952). This theory has predictive power in that it predicts that when outcomes are
perceived to be greater individuals self disclose more (Thibaut and Kelley, 1952). What

this means is, people strive to minimize costs and maximize rewards and then base the

14

likeliness of developing a relationship with someone on the perceived possible outcomes.
As mentioned earlier, when people perceive these outcomes to be greater, they disclose
more and develop a closer relationship. So in a Web based data collection context, the
basic assumption would be that consumers would be more willing to disclose information
about themselves and develop a closer relationship if they perceive the outcome of it to
be greater. Little or no research has been done to establish what these ‘outcomes’ are that
make consumers disclose more information about themselves. This calls for a qualitative
study to investigate and establish the various ‘outcomes’ and factors that influence
consumers in sharing personal information.

Once a possible set of outcomes is identified in this customer-firm relationship,
we can use the two standards of comparison that were mentioned earlier to evaluate them.
For instance, let us consider a website that offers free software (benefit) to customers in
exchange for personal information and other information relating to the Web surfing
habits of customers (costs). Customers have to register with the website and provide
information that is required to gain access to download the software. According to SET,
customers would register and provide information only if they perceive the benefit of
doing so is either equal to or greater than the cost involved (i.e. costs < = benefit). The
outcome of this relationship is then the access that the website provides to the individual.
The individual will be ‘satisfied’ with the relationship if and only if the outcome (access
to software) meets or surpasses her threshold, comparison level (CL). If the comparison
level (CL) for the individual is a minimum of 30 days version of the software, then an
access of software for one-time use may not meet her CL thereby making her feel

dissatisfied with such a relationship. On the other hand, a 60 days version would surpass

15

her CL resulting in a favorable relationship. This leads us to the first research proposition,
Web users would respond favorably to Web-based data collection efforts if they perceive
the benefits (outcomes) of it exceeding their expectations. (Outcome > CL)

Now, let us consider the pay-offs available to the customer outside of this
relationship. There could be another website offering the same software without a
registration requirement, or she could buy the software from a vendor without an
information disclosure or decide to do without it altogether. If there are no alternate
websites that provide the software and it is expensive software that is required then the
customer may decide to stick to the current relationship. She would provide information
even for a 15 days version of the software. This would be her comparison level of
alternatives (CLalts). This would then be the worst outcome that the individual would
accept and still stay in a relationship. However, as more attractive possibilities become
available (new website providing access without information requirement), or as existent
outcomes begin to slide below the established CLalt (one—time trial version only), the
current customer-website relationship starts becoming instable. An important point to
note here is that CLalt does not relate to attraction to relationships and satisfaction in
relationships. An individual may choose to buy the software from a different vendor even
though a 15 days version is available online for download on minimal information
sharing. Therefore an individual may not respond favorably to a Web-based data
collection effort in the presence of another company that offers the same or more benefits
(outcomes).

The investment model that was described in the literature review gives some

insight on the customer-firm relationships and helps forecast this relationship

16

commitment and maintenance. According to Rusbult (1983) relationship commitment is a
function of not just relationship satisfaction and alternatives but also a function of the
relationship investments. If an individual makes an investment, usually intrinsic in this
case, she would be more willing to provide information about herself. The intrinsic
investment can be that the individual is an existing customer of the company or even
more a shareholder. This kind of vested interest makes her more likely to divulge
information about herself.

H1: Individuals with vested interest in a company should respond favorably to
Web-based data collection effort that the company undertakes.

Finally, the customer-firm relationship when considered from a social exchange
point of view brings in the importance of trust. We have seen that in economic exchange
relationships actors tend to have distrust in the other’s ability and utility of the returns
provided. If a firm collecting data over Web promises the users benefits (outcomes) at a
later date, then the probability of the user providing data in such circumstances would be
little. Since we are considering this relationship only from a social exchange viewpoint
we have to take into account that the company has no specified obligations, that the
outcomes cannot be quantified or bargained, that there is a lack of trust between both
parties and that a little uncertainty arises because of distrust that influences people to be
calculative and to seek shorter-term benefits from the relationship. When this uncertainty
increases there will be a tendency to abandon the wait for benefits over the long-term and
in essence the relationship and seek the benefits elsewhere. This brings us to our last
three hypotheses,

H2: If individuals are doubtful about benefits provided by the company as an
exchange for Web-based data, they are less likely to provide information

17

H3: Consumers are more likely to give personal information on the Web for
immediate benefits than for delayed benefits.

H4: The longer the wait for benefits that the company has in exchange for Web-
based data collection, the less likely are customers to react favorably to it.

18

CHAPTER 4

METHODS

Pretest

Initially, a qualitative study was undertaken to find out the various outcomes and
returns that customers consider worthy enough to provide their personal information. The
reason why a qualitative study was undertaken was because qualitative research gives us
the chance to listen to consumers express their ideas in their own words and the
opportunity to connect with their minds, and hence draw insights and explanations fi'om
the participants themselves (Davis p.108). A random sample of students and working
professionals was asked to write essays and answer a few open-ended questions (see
APPENDIX A). The identity of the participants was disguised in the analysis to protect
their privacy. Questions were designed to find out the various factors that an individual
would take into consideration for responding favorably to Web based data collection
questions. Also, they were asked to mention the types of data that they were comfortable
in providing and the types of data that they were NOT comfortable in providing in Web-
based questionnaires.

A content analysis of the essays was done revealing that most respondents did not
consider anything that a company could provide in exchange for personal information
worthy enough to provide such information. A few others mentioned that “free gifts like
t-shirts, cap etc and access to online information like horoscopes and jokes” could make
them provide personal information. Other outcomes that result in favorable response to

9“

Web-based data collection are “money’ , assurance that it is the only company to which I

99 ‘6

need to give my personal information and that it would not sell it to others , incentives

l9

9, 6‘

that do not involve lucky dip , assurance that the company would verify the information
and correct it frequently” and “a promising attitude on the part of the company”.

The types of information that individuals are comfortable in providing are name, sex,
hobbies, likes/dislikes, job description, address and email. On the other hand, individuals
are hesitant in providing credit card numbers, social security numbers, phone numbers
and in some cases address and email too. Summarily, it was found from the open-ended
questions that individuals are comfortable in providing information that is already public
(like address, date of birth, citizenship etc) and uncomfortable in providing any
information that can tie back to an individual exactly suggesting that individuals do not

mind giving information for group analysis.

Final Survey

 

To test the four hypotheses, a survey method was adopted. The questionnaire (see
APPENDIX B), apart from the initial five personal questions, measured independent
variables familiarity of the company seeking information, the respondent’s relevance and
trust in the company and the dependent variable, their willingness to provide personal
information to the said company. To test the hypotheses relating to the effect of
outcomes and their nature on the customer willingness to provide personal information
and the type of information a series of nine questions were designed. Each of these
questions correspond to a unique profile with three attributes namely, type of information
requested, the magnitude of the outcome in return for providing such information and the
time period for receiving the said benefits.

All these attributes had three levels and a full profile, rating type metric analysis

was done. The dependent variable measured was the willingness to provide information

20

and it was rated on a seven point Likert’s scale. A full profile model was chosen over
pair-wise because full profile descriptors present an integrated multi-attribute concept just
as real life alternatives (Green and Srinivasan 1978). Pair-wise model calls for extensive
evaluations on the part of respondents that are greater in number compared to full profile.
Also, the evaluation task may be unrealistic when only two attributes are being evaluated
simultaneously. According to Malhotra (1996) both models yield comparable results, yet
the full profile approach is the more common one. Given the three attributes defined at
the three levels each we have a total 3X3X3 = 27 profiles. To evaluate twenty—seven
profiles is a tedious task; therefore, a fractional factorial design was employed to reduce
the number of profiles to nine. It is from this reduced set of nine profiles that the
willingness to provide personal information was estimated as associated with each of the
individual factors and their associated levels.

Variables

The independent variables used in this study are familiarity/relevance and
trust/uncertainty. There were about five questions that measure the familiarity level of the
respondents using Likert’s seven-point scale. Similarly the trust and uncertainty in the
company Macromedia was also measured using the same scale with another six
questions. The dependent variable was their willingness to provide personal information
to the company in question, Macromedia.

For the conjoint analysis, as mentioned earlier nine profiles were used out of the
27 possible one using fractional factorial design. The three variables that were
manipulated were the type of information requested by the company, the amount of
money offered as a return and the delay in receiving the return. These three variables had

three levels each. The three levels of the attributes are presented in a tabular form below

21

Table 1 Attributes and their levels

 

Attribute Number Description
Information requested 3 Name, Gender, Email, Address, Social Security #

and Credit card information

2 Name, Gender, Email, Address
1 Name, Gender
Coupon Amount 3 $2
2 $5
1 $10
Delay in redeeming the 3 14 days
coupon 2 7 days
1 Instant Online

 

Participants

A total of 99 students from a fairly large midwestem university participated in the
survey. The sample was drawn to include a variety of majors like pathology, elementary
education, computer science, kinesology, engineering, forensic psychology, criminal
justice, public administration, human biology, sociology, communications, pre-law,
Spanish, geography, business and advertising. The age of the participants varied from 16

to 35.

22

CHAPTER 5

RESULTS

Participants for the final survey (n=99) were recruited from four different
undergraduate classes that included a diverse number of academic majors as mentioned
earlier. The sample consisted of 46 women (46.5%) and 53 men (53.5%) with an average
age of 21 .86 (S_D =3.3). The majority of the respondents were seniors (67%) followed by
freshman and then graduate students. The company used for measuring the familiarity
and relevancy levels was Macromedia, a leading software provider for creating user
experiences on the Internet. The reason why macromedia was chosen was because of the
assumption that a wide range of familiarity and trust levels could be obtained with a
company like macromedia for a sample of student population (M = 5.6343, _S_I2 = 1.388

and range = 4.80 for familiarity and M = 4.3199, SD = 0.944 and range = 4.67).

Hypotheses testing

H1 states that in the event of an individual having a vested interest in the
company, she would respond favorably to a Web-based data collection effort that the
company undertakes. To test the first hypothesis, the independent variable familiarity,
which also measured the level of relevance and ‘vested interest’, was correlated with the
dependent variable willingness to provide information. A correlation was done between
the independent variable familiarity and the dependent variable ‘willingness’ to provide
personal information using statistical software SPSS 10.1. The results indicated a positive

correlation between familiarity and willingness (r=0.291,p<0.005) thereby indicating that

23

H1 was acceptable (see Table 2). Also, a regression analysis estimated the value of F =

13.895 (p<0.001). The results of regression analysis are summarized in table 2.

Hypothesis H2 is very similar to the first and is concerned with the effect of trust

on willingness to provide information. It states that if individuals are doubtful about

benefits provided by the company as an exchange for Web-based data, they are less likely

to provide information. A correlation was done between the independent variable trust

that also measured the level of uncertainty in the company Macromedia, and the

dependent variable ‘willingness’ to provide personal information using statistical

software SPSS 10.1. The result was a marked positive correlation between trust and

willingness (r=0.446 p< 0.001). Hence, H3 was also accepted (see Tables 2 and 3).

Table 2. Correlations

 

F AMILIAR Pearson Correlation
Sig. (l-tailed)

Sum of Squares and Cross-
products
Covariance
N
Pearson Correlation
Sig. (l-tailed)

Sum of Squares and Cross-
products
Covariance
N
Pearson Correlation
Sig. (l-tailed)

Sum of Squares and Cross-
products
Covariance
N

TRUST

Willingness

1.000
188.973

1.928
99
.311"
.001
39.899

.407
99
.291 **
.002
80.728

.824
99

F AMILIAR TRUST

.311"
.001
39.899

.407
99
1 .000

87.371

.892
99
.446“
.000
84.020

.857
99

Willingness

.291"
.002
80.728

.824
99
.446"
.000
84.020

.857
99
1.000
406.727

4.150
99

 

** Correlation is significant at the 0.01 level.

24

Table 3. Regression Analysis - Model Summary

 

Model R R Square Adjusted R Square

1 .474 * .224 .208 1.81

Std. Error of the Estimate

 

* Predictors: (Constant), TRUST, F AMILIAR

 

ANOVA"
Model Sum of Squares df Mean Square F Sig.
1 Regression 91.306 2 45.653 13.895 .000*
Residual 315.421 96 3 .286

Total 406.727 98

 

* Predictors: (Constant), TRUST, FAMILIAR

** Dependent Variable: Willingness to share information based on familiarity and trust '

 

Coefficients*
Unstandardized Standardized t Sig.
Coefficients Coefficients
Model
B Std. Error Beta
1 (Constant) -.547 .996 -.549 .584
FAMILIAR .248 .139 .169 1.788 .077
TRUST .848 .204 .393 4.159 .000

 

* Dependent Variable: Willingness to share information based on familiarity and trust

25

A conjoint analysis was done to test hypotheses 3 and 4 that are related to the
effect of outcomes and their nature on the customer willingness to provide personal
information and the type of information. In essence, the type of information, outcomes
and the nature of outcomes were considered as the three attributes. The three attributes
had three levels each. As mentioned earlier a total of nine scenarios were constructed
from the 27 possible ones (3X3X3) using fractional factorial design. Respondents were
asked to mark their level of willingness to provide information in each scenario (see table
4). This rating method was used over ranking because of simplicity and convenience in
analyzing the scenarios for the respondents. The ratings were obtained using a seven
point Likert’s scale (1 = not likely and 7 = very likely). Ratings obtained are shown in the
table 4. The ratings were analyzed at an aggregate level rather than on individual levels. '
The utility and importance of each attribute was calculated using statistical software

SYSTAT 9. The part—worth parameter estimates are summarized in table 5.

Table 4. Scenarios and their aggregate ratings

 

Scenario Attribute level Willingness
Number Rating
Information Coupon Delay in
Requested Amount Redeeming the

coupon
I 1 1 l 4.52
2 1 2 2 4.13
3 1 3 3 3.22
4 2 1 2 3.65
5 2 2 3 3.20
6 2 3 1 2.75
7 3 1 3 l .83
8 3 2 1 1 .85
9 3 3 2 1 .75

 

26

Table 5 Parameter Estimates (Part-Worth)

 

Name, Gender, Email,

 

 

gauge, 1:81??? 2:51:22 Email, Postal Address, Social
en er OS a security # and Credit
card information
0.462 0.092 -0.813
$10 $5 $2 Instant 7 days 14 days
0.248 0.080 —0.125 0.138 0.032 -0.115

 

To interpret the above-mentioned numbers in the table, we can think of the
numbers as the utility values for the particular attributes that they correspond to. This
means that a negative utility value indicates that consumers do not prefer that particular
attribute or in other words they do not want that particular aspect in Web-based data
collection. For example, consumers do not want to give out their credit card information
and social security numbers along with name, gender, email and address (-0.813) as
opposed to just name and gender (0.462). They see less utility in providing the
information requested for when the amount that is being offered is only $2 (part-worth
utility = -0.125) as opposed to $10 (part-worth utility = 0.248). Similarly they see lesser
utility in providing information and establishing a relationship when the delay in
receiving the amount is 14 days (part-worth = -0.115) as opposed to when there is no
delay (0.138). To better understand the various attributes and their utility values or part-
worths, the following graphs that were obtained from the data using SYSTAT 9 would be

helpful.

27

Figure 1.

Measure

Figure 2.

Measure

Willingness Versus Information requested

 

 

 

 

0.5 - _
0.0 ~ —
-O.5 — —
-10 1 1 1
1 2 3
INFO$

Willingness Versus Amount

 

 

 

 

1.0 l I l
0.5 -
0.0 — \
-0.5 —
-1.0 I l l
1 2 3
AMOUNT$

28

Information requested

1 --- Name, Gender

2 --- Name, Gender,
Email, Address

3 --- Name, Gender,
Email, Address,
Social security
number, Credit card
number

Amount

1 ----- $10
2 ----- $5
3 ----- $2

Figure 3. Willingness Versus Delay

 

1 -0 1 I 1 Delay

1 ----Instant redemption of
coupon online
2 ----7 days delay

0.5

I
1

 

 

 

g 3 ----14 days delay
g 00 " \ T
Q)
2

-O.5 - -

_1 .0 1 1 1

1 2 3
DELAY$

To interpret these results and apply them to the hypotheses that were drawn
earlier, we can clearly see from the figure 3 that as the delay or the time period for
redemption of the coupon increases the willingness to provide personal information
decreases. This was in fact what was hypothesized earlier in H3. Also it can be seen that
the curve becomes sharper as the delay increases from 7 to 14 in comparison to instant to
7 days. This proves that the greater the delay the lesser the likely a consumer to provide
personal information, which was our H4. Hence, we can safely accept both our
hypotheses 3 and 4.

Other interesting deductions that can be made from the graphs above are that the
value of information to the consumer rises very steeply as more and more items are added
to the list of requested information especially confidential information like social security
and credit card numbers. This is evident from the steep way the curve falls showing least

willingness to provide information when social security numbers and credit card

29

information is requested. Finally, the magnitude of outcome also has an effect on the
willingness to provide information. As seen in the graph (see figure 2) the fall in the
curve indicates that consumers will be more willingness as the magnitude of the outcome
increases or in other words exceeds or equals their expectations. Our research proposition
states that Web users would respond favorably to Web-based data collection efforts if
they perceive the benefits (outcomes) of it exceeding their expectations. (Outcome > CL).
Though the study has not directly measured the expected outcomes of the individuals the
linear correlation between the magnitude of outcome of a relationship and the willingness
to provide personal information does indicate the possibility of our proposition being

true.

30

CHAPTER 6

SUMMARY AND CONCLUSIONS

Discussion

Research has established the various conditions under which consumers would be
willing to respond favorably to data collection efforts (Zhang, Wang and Chen 2000).
Such studies have focused on finding out the conditions and the types of information that
individuals are comfortable in providing but without much theoretical justifications.
Social Exchange Theory is probably one way of providing a theoretical framework for
these studies. Given the rational and robust nature of SET, it does form a good starting
point to explore the relationships that exist between companies and individuals.

Familiarity, after all, does not breed contempt. It is only logical to expect that the
more one trusts a company the more likely one is to provide information and respond
favorably to the company’s data collection efforts. This likeliness would finther increase
should the individual have a vested interest in the company. Individuals do seem to
weigh the pros and cons before giving out any personal information. In cases where the
outcome is greater than their expectations they are more likely to provide personal
information than otherwise. The three major factors that were considered in this study
namely, the type of information requested, the magnitude of the outcome and the nature
of the outcome do seem to have a considerable effect on the willingness to part with
personal information. Like in all relationships individuals seem to weigh the outcomes
and make a very rational decision to either respond favorably or unfavorably to data

collection efforts.

31

Limitations

One of the limitations of the present study is that the customer— firm relationship
was not considered on the basis of power. Thibaut and Kelley (1952, 1959 and 1978)
pointed out the link between dependence and control by identifying three forms of
control including, reflexive control, fate control and behavioral control. If a person has
the ability to reward herself irrespective of the type of relationship she is in then she is
said to have a reflexive control in the relationship. Self-provided rewards are a
convenient way of providing independence in any type of relationship. Fate control is the
ability to affect one party’s outcomes regardless of what he or she does. The only
recourse this party has in such relationships is to break it off. When both parties in the
relationship have such power then it is termed mutual fate control. Finally, behavioral
control is the ability of people to change the other party’s behavior in a relationship
through variations in their own behavior. The concept of mutuality of these controls is
very interesting. Whereas mutual reflexive control is the ability of parties to make what
they want to come true in their lives, mutual fate control is the ability of parties to make
what they want to come true in the other party’s life. When these two powers conflict, the
ability to resolve that conflict is the mutual behavioral control (Thibaut and Kelley,
1978). So when we consider this relationship in terms of power and dependence and
when the consumer has reflexive control in a firm-customer relationship than it is highly
unlikely that she would provide personal information. The consumer is more likely to
provide personal information on a Web-based form if the website has fate control over
her rather than the other way. If the firm collecting data has the ability to make the user

provide information with some variation in its own behavior then the firm has behavioral

32

control over the relationship. It would be interesting to see the various ways in which a
company can exert power (reflexive, fate and behavioral) in the relationship making it
necessary for the individual to provide information in a Web-based data collection.
Further research is needed to estimate the effects of power on customer-firm
relationships.

Relationships as we know are developed over time and any one time purchase or a
visit to a company’s website may not constitute a relationship. This study is valid if and
only if it can be safely assumed that the one time purchases and visits to websites do
mark the beginning of a relationship that would be built with time. A basic assumption
that all relationships begin with that single purchase or visit to the company website is
what that underlines this study. Another assumption made during questionnaire design
was about the different items that are generally collected in a Web-based questionnaire.
The relevance of things mentioned as the information requested for example the social
security number and credit card information is doubtful. It has not been established that
firms meaning to implement CRM do need such information. So another direction future
research could take is an investigative on the various information/data types that
companies trying to implement CRM would need. It would be interesting to figure out
the different kinds of information that CRM firms need and collect.

Also, further research is necessary to find out the effect of the presence of another
company that provides the same or more benefits on an existing relationship between a
company and an individual. Though literature review points that individuals would be
less willing to provide information in such circumstances, this study failed to accumulate

empirical evidence to support it. As a future step, that aspect could be included in a

33

survey that probably uses a more random sample rather than the convenient student
sample that was used in the present study. Also, considering the limitations that studies
involving surveys have it would be interesting to see how the results would change if a

totally different approach was adopted to deal with this issue.

Implications

So what then is the best way to get the maximum information out of individuals,
given the huge importance they seem to attach to their privacy and personal information?
Is it possible to create the conditions that would make them respond favorably? Before
we set out to analyze the consumer responses, we do have to keep in mind that this study
has systematically shown that individuals do behave very rationally, weighing all the pros
and cons, before making a decision to provide any information. One of the findings is
that consumers are very cautious about giving out their social security and credit card
information. So it would be best to avoid such questions on any questionnaire that
attempts to collect information.

A wide variety of things were mentioned in the open-ended questions that were
included in the survey as the possible outcomes that would make them think about
providing information. These include access to music, free software, email access,
monetary benefits, freebies like t-shirts, access to networks like Kazaa, ebay, etc.,
coupons, mail in rebates and discounts, assurance that there will be no junk mail sent and
assurance that the company would not sell the information. However, a majority of the
respondents were not willing to compromise on any incentive if they were not prior
customers of the company. Almost all of them are particular about social security and

credit card information.

34

In situations where the consumer makes the initial contact with the company, the
conditions seem to be more favorable for the start of a strong relationship. The level of
relevance in such a situation is higher and therefore the likelihood of consumers giving
out information is greater. Marketers should always strive to present immediate benefits
rather than delayed benefits. Ideally, they should fit in the benefit such that the consumer
is forced is provide information for it. For example, if address of the consumer is required
then a gift coupon that would be sent to the individual’s house without delay would be
ideal, as that would necessitate the divulgence of receiver’s address.

In conclusion a word of caution for all marketers seeking favorable response to
Web-based data collection, you might have to think twice before investing a great deal of
money on such efforts, given a much rational consumer who would go the extent of

filling in false information just to reap the benefits.

35

APPENDIX A

Data Collection Survey
Please answer the following questions in your own words. Remember that there

are no right or wrong answers.

 

Many company websites ask you to submit personal information about yourself.
Is there anything a company could offer you to make you more likely to provide

this personal information?

 

H .11
What type of information are you comfortable in providing when requested by a

company website?

 

.21
:1

What type of information are you uncomfortable about providing on a company

 

website?

 

a
? .1

What is your age?

36

lam a...

C Male (‘ Female

lam a...

Freshman
Sophomore
Junior

Senior

3333')

Graduate student

Other (please specify)
If you selected other please specify:

I 1
1., -._-_.A___.,,7-7 -. -1- ,,‘ -__._T. _ .

My Email address (optional)

 

 

 

Thank you very much for your time.

37

APPENDIX B

Please answer a few questions about you before you start the survey
1) What is your gender? 1] MALE [:I FEMALE

2) What is your age?

 

3) What is your ethnic background? American Indian or Alaskan Native

D Black or African American E] Asian, Asian American or Pacific Islander
D White (Non—Hispanic) D Mexican, Mexican American or Chicano
D Other, please specify:

 

4) What is your grade level?

1:] Freshman [:1 Sophomore D Junior 1:] SeniorD Graduate

D Other, please specify:

5) What is your major?

 

 

 

Please answer the following questions by selecting the option that you think best
represents your choice. Remember that there are no right or wrong answers.

 

Macromedia (www.macromedia.com) is a company that provides software for creating
effective user experiences on the Internet. With operations in more than 50 countries it
has yearly revenue of over $300 Million. At its website it offers free download of its tools
like Dreamweaver, Flash, Fireworks, FreeHand, Director Shockwave Studio, HomeSite,
Authorware and other client/server software. However, users are required to register and
provide personal information like address, email, phone number and other product usage
details.

1) How familiar are you with the products and services offered by Macromedia?

Very familiar l 2 3 4 5 6 7 Not familiar at all

38

2) How often do you use Macromedia tools?

Very often 1 2 3 4 5 6 7 Never
3) How often do you visit the Macromedia company website?

Very often 1 2 3 4 5 6 7 Never
4) How often do you download software from Macromedia website?

Very often 1 2 3 4 5 6 7 Never

 

5) The company Macromedia to me

is unimportant l 2 3 4 5 6 7 is important

is of concern 1 2 3 4 5 6 7 is of no concern
is not relevant l 2 3 4 5 6 7 is relevant
means a lot 1 2 3 4 5 6 7 means nothing
does not matter] 2 3 4 5 6 7 matters

6) How likely are you to provide basic personal information like name, gender etc. in
exchange for free download of trial versions of software at the macromedia website?

Very likely 1 2 3 4 5 6 7 Not likely at all

7) Please rate the degree to which you agree or disagree with each of the statements
about Macromedia.
Strongly Strongly
Agree Disagree
Macromedia does not sell the information
that it collects on its website to other companies. 1 2 3 4 5 6 7

I completely trust macromedia with my 1 2 3 4 5 6 7
personal information.

I am confident that macromedia would provide
me the software for free download after I give 1 2 3 4 5 6 7

them the information that they need.

I am certain that the software downloaded

39

after giving them the personal information 1 2 3 4 5 6 7
will work for the said period of time.

If I provide them with my email address I am
certain that Macromedia would send me 1 2 3 4 5 6 7
junk mail even though they claim otherwise.

Giving out personal information in exchange
for free download of trial versions of software 1 2 3 4 5 6 7
is a big price to pay.

8) FreeStuffcom is a website that provides instant access to free software, chat, jokes,
horoscope, music and a variety of other services. However, users are required to register
before gaining access to their site.

Consider the following scenarios and mark how likely you are to provide the information
they need in exchange for their services.

Scenario — 1

Information requested: Name, gender
Gift in return: $10
Type of gift: Gift check can be redeemed instantly online

Very likely I 2 3 4 5 6 7 Not likely at all

Scenario — 2

Information requested: Name, gender

Gift in return: $5

Type of gift: gift check sent via post in 7days

Very likely I 2 3 4 5 6 7 Not likely at all

Scenario - 3

Information requested: Name, gender

Gift in return: $2

Type of gift: gift check sent via post in 14 days

Very likely I 2 3 4 5 6 7 Not likely at all
Scenario - 4
Information requested: Name, gender, email, postal address

Gift in return: $10
Type of gift: gift check sent via post in 7days

Very likely I 2 3 4 5 6 7 Not likely at all

40

Scenario - 5

Information requested: Name, gender, email, postal address
Gift in return: $5

Type of gift: gift check sent via post in l4days

Very likely I 2 3 4 5 6 7 Not likely at all

Scenario — 6

Information requested: Name, gender, email, postal address
Gift in return: $2

Type of gift: gift check can be redeemed instantly online

Very likely I 2 3 4 5 6 7 Not likely at all

Scenario — 7

Information requested: Name, gender, email, postal address, social security #, credit card
information

Gift in return: $10

Type of gift: gift check sent via post in 14 days

Very likely I 2 3 4 5 6 7 Not likely at all

Scenario — 8

Information requested: Name, gender, email, postal address, social security #, credit card
information

Gift in return: $5

Type of gift: gift check can be redeemed instantly online

Very likely 1 2 3 4 5 6 7 Not likely at all

Scenario - 9

Information requested: Name, gender, email, postal address, social security #, credit card
information

Gift in return: $2

Type of gift: gift check sent via post in 7days

Very likely I 2 3 4 5 6 7 Not likely at all
9) In the last year or so, what are the various incentives (gifts, returns, outcomes, services

etc.) that have made you provide basic personal information to any company on their
website?

 

 

 

41

10) If a company asks for basic personal information in exchange for the various

outcomes/returns/gifts/ services that it provides, what are the various
outcomes/retums/gifts/services that will make you provide such information in the

future?

 

 

42

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