Diffusion of Innovation Finsl Work Report

7/31/2019 Diffusion of Innovation Finsl Work Report

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In the Name Of Almighty

ALLAH

The Most Beneficent and the Most Merciful

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Diffusion of Innovation Theory

One of the greatest pains to human nature is the pain of a new idea.

It ... makes you think that after all, your favorite notions may be wrong,

your firmest beliefs ill-founded ... Naturally, therefore, common men

hate a new idea, and are disposed more or less to ill-treat the original

man who brings it.

-Walter BagehotPhysics and Politics

Definition of Diffusion of Innovation

In his comprehensive bookDiffusion of Innovation, Everett Rogers defines diffusion asthe process by which an innovation is communicated through certain channels over time

among the members of a social system. Rogers' definition contains four elements that are

present in the diffusion of innovation process. The four main elements are:

(1) innovation - an idea, practices, or objects that is perceived as knew by an individual orother unit of adoption.

(2) communication channels - the means by which messages get from one individual to

another.

(3) time - the three time factors are:

(a) innovation-decision process(b) relative time with which an innovation is adopted by an individual or group.

(c) innovation's rate of adoption.

(4) social system - a set of interrelated units that are engaged in joint problem solving to

accomplish a common goal.

Make a better mousetrap, and the world will beat a path to our door.

-Ralph Waldo Emerson

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Background on Diffusion of Innovation

The concept was first studied by the FrenchsociologistGabriel Tarde (1890) and by

German and Austrian anthropologists such as Friedrich Ratzel and Leo Frobenius.[1]Its

basic epidemiological or internal-influence form was formulated by H. Earl Pemberton,[2] who provided examples of institutional diffusion such as postage stamps and compulsory

school laws.

In 1962 Everett Rogers, a professor of rural sociology publishedDiffusion of Innovations.

In the book, Rogers synthesized research from over 508 diffusion studies and produced a

theory for the adoption of innovations among individuals and organizations.

The book proposed 4 main elements that influence the spread of a new idea: the innovation,

communication channels, time, and a social system. That is, diffusion is the process by

which an innovation is communicated through certain channels over time among the

members of a social system. Individuals progress through 5 stages: knowledge, persuasion,

decision, implementation, and confirmation. If the innovation is adopted, it spreads via

various communication channels. During communication, the idea is rarely evaluated froma scientific standpoint; rather, subjective perceptions of the innovation influence diffusion.

The process occurs over time. Finally, social systems determine diffusion, norms on

diffusion, roles of opinion leaders and change agents, types of innovation decisions, and

innovation consequences. To use Rogers model in health requires us to assume that the

innovation in classical diffusion theory is equivalent to scientific research findings in the

context of practice, an assumption that has not been rigorously tested. [3]

The origins of the diffusion of innovations theory are varied and span across multiple

disciplines. Rogers identifies six main traditions that impacted diffusionresearch:anthropology, early sociology, rural sociology, education, industrial, and medical

sociology. The diffusion of innovation theory has been largely influenced by the work of

rural sociologist

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Elements

The key elements in diffusion research are:

Element Definition

InnovationRogers defines an innovation as "an idea, practice, or object that isperceived as new by an individual or other unit of adoption".

Communication

channels

A communication channel is "the means by which messages get from

one individual to another".

Time

"The innovation-decision period is the length of time required to pass

through the innovation-decision process". "Rate of adoption is the

relative speed with which an innovation is adopted by members of asocial system".

Social system "A social system is defined as a set of interrelated units that are engagedin joint problem solving to accomplish a common goal".

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Decisions

Two factors determine what type a particular decision is :

Whether the decision is made freely and implemented voluntarily,

Who makes the decision.

Based on these considerations, three types of innovation-decisions have been identified within diffusion of

innovations.

Type Definition

Optional Innovation-

Decision

This decision is made by an individual who is in some way distinguished from

others in a social system.

Collective Innovation-

DecisionThis decision is made collectively by all individuals of a social system.

Authority Innovation-

Decision

This decision is made for the entire social system by few individuals in positions

of influence or power.

Mechanism

Diffusion of an innovation occurs through a fivestep process. This process is a type of

decision-making. It occurs through a series of communication channels over a period oftime among the members of a similar social system. Ryan and Gross first indicated the

identification of adoption as a process in 1943 (Rogers 1962, p. 79). Rogers categorizes the

five stages (steps) as: awareness, interest, evaluation, trial, and adoption. An individual

might reject an innovation at any time during or after the adoption process. In later editions

of the Diffusion of Innovations Rogers changes the terminology of the five stages to:

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knowledge, persuasion, decision, implementation, and confirmation. However the

descriptions of the categories have remained similar throughout the editions.

Five stages of the adoption process

Stage Definition

Knowledge

In this stage the individual is first exposed to an innovation but lacksinformation about the innovation. During this stage of the process the

individual has not been inspired to find more information about the

innovation.

PersuasionIn this stage the individual is interested in the innovation and activelyseeks information/detail about the innovation.

Decision In this stage the individual takes the concept of the change and weighs the

advantages/disadvantages of using the innovation and decides whether to

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adopt or reject the innovation. Due to the individualistic nature of this

stage Rogers notes that it is the most difficult stage to acquire empiricalevidence (Rogers 1964, p. 83).

Implementation

In this stage the individual employs the innovation to a varying degree

depending on the situation. During this stage the individual determines theusefulness of the innovation and may search for further information about

it.

ConfirmationAlthough the name of this stage may be misleading, in this stage theindividual finalises his/her decision to continue using the innovation and

may end up using it to its fullest potential.

Be not the first by who the new is tried, nor the last to lay the old aside.

-Alexander Pope,An Essay on Criticism, Part II

Rejection and Discontinuance

Of course, as Rogers points out, an innovation may be rejected during any stage of theadoption process. Rogers defines rejection as a decision not to adopt an innovation.

Rejection is not to be confused from discontinuance. Discontinuance is a rejection thatoccurs after adoption of the innovation.

Rogers synopses many of the significant research findings on discontinuance. Many"discountenances occur over a relatively short time period" and few of the

"discountenances were caused by supersedence of a superior innovation replacing a

previously adopted idea". One of the most significant findings was research done byJohnson and Vandan Ban (1959):

The relatively later adopters had twice as many discountenances as the earlier adopters.

Previous researchers had assumed that later adopters were relatively less innovative

because they did not adopt or were relatively slow to adopt innovations. This evidencesuggests the later adopters may adopt, but then discontinue at a later point in time.

Rogers identifies two types of discontinuance:

(1) disenchantment discontinuance - a decision to reject an idea as a result of dissatisfaction

with it's performance, and

(2) replacement discontinuance - a decision to reject an idea in order to adopt a better idea.

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Rogers 5 Factors

Rogers defines several intrinsic characteristics of innovations that influence an individuals

decision to adopt or reject an innovation.

Relative advantage is the degree to which an innovation is perceived as better thanthe

idea it supersedes. The degree of relative advantage may be measured in economic terms,

but social prestige, convenience, and satisfaction are also important factors. It does not

matter so much if an innovation has a great deal of objective advantage. What does matter

is whether an individual perceives the innovation as advantageous. The greater the

perceived relative advantage of an innovation, the more rapid its rate of adoption will be.

Compatibility is the degree to which an innovation is perceived as being consistent with

the existing values, past experiences, and needs of potential adopters. An idea that isincompatible with the values and norms of a social system will not be adopted as rapidly

as an innovation that is compatible. The adoption of an incompatible innovation often

requires the prior adoption of a new value system, which is a relatively slow process.

Complexity is the degree to which an innovation is perceived as difficult to understand anduse. Some innovations are readily understood by most members of a social system; othersare more complicated and will be adopted more slowly. New ideas that are simpler tounderstand are adopted more rapidly than innovations that require the adopter to develop

new skills and understandings.

Trialability is the degree to which an innovation may be experimented with on alimited basis. New ideas that can be tried on the installment plan will generally be

adopted more quickly than innovations that are not divisible. An innovation that is

trialable represents less uncertainty to the individual who is considering it for adoption,

who can learn by doing.

Observability is the degree to which the results of an innovation are visible to others. The

easier it is for individuals to see the results of an innovation, the more likely they are to

adopt it. Such visibility stimulates peer discussion of a new idea, as friends and neighbors

of an adopter often request innovation-evaluation information about it.

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Rates of adoption

The rate of adoption is defined as the relative speed with which members of a social system

adopt an innovation. It is usually measured by the length of time required for a certain

percentage of the members of a social system to adopt an innovation (Rogers 1962, p. 134).The rates of adoption for innovations are determined by an individuals adopter category.

In general, individuals who first adopt an innovation require a shorter adoption period

(adoption process) than late adopters.

Within the rate of adoption there is a point at which an innovation reachescritical mass.

This is a point in time within the adoption curve that enough individuals have adopted an

innovation in order that the continued adoption of the innovation is self-sustaining. In

describing how an innovation reaches critical mass, Rogers outlines several strategies in

order to help an innovation reach this stage. These strategies are: have an innovation

adopted by a highly respected individual within a social network, creating an instinctive

desire for a specific innovation. Inject an innovation into a group of individuals who would

readily use an innovation, and provide positive reactions and benefits for early adopters of

an inn Innovators are the first 2.5 percent of the individuals in a system to adopt an innovation.

Venturesomeness is almost an obsession with innovators. This interest in new ideas leads them out of a

local circle of peer networks and into more cosmopolite social relationships. Communication patterns

and friendships among a clique of innovators are common, even though the geographical distance

between the innovators may be considerable. Being an innovator has several prerequisites. Control of

substantial financial resources is helpful to absorb the possible loss from an unprofitable innovation.

The innovator must be able to cope with a high degree of uncertainty about an innovation

at the time of adoption. While an innovator may not be respected by the other members of a

social system, the innovator plays an important role in the diffusion process: That of

launching the new idea in the system by importing the innovation from outside of the

system's boundaries. Thus, the innovator plays a gatekeeping role in the flow of new ideas

into a system.

Early adopters are the next 13.5 percent of the individuals in a system to adopt aninnovation. Early adopters are a more integrated part of the local system than areinnovators. Whereas innovators are cosmopolites, early adopters are localites. This adopter

category, more than any other, has the greatest degree of opinion leadership in most

systems. Potential adopters look to early adopters for advice and information about theinnovation. This adopter category is generally sought by change agents as a local

missionary for speeding the diffusion process. Because early adopters are not too far ahead

of the average individual in innovativeness, they serve as a role-model for many other

members of a social system. The early adopter is respected by his or her peers, and is the

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embodiment of successful, discrete use of new ideas. The early adopter knows that to

continue to earn this esteem of colleagues and to maintain a central position in thecommunication networks of the system, he or she must make judicious innovation-

decisions. The early adopter decreases uncertainty about a new idea by adopting it, and

then conveying a subjective evaluation of the innovation to near-peers through

interpersonal networks.

Early majority is the next 34 percent of the individuals in a system to adopt aninnovation. The early majority adopt new ideas just before the average member of a

system. The early majority interact frequently with their peers, but seldom hold positions

of opinion leadership in a system. The early majority's unique position between the veryearly and the relatively late to adopt makes them an important link in the diffusion

process. They provide interconnectedness in the system's interpersonal networks. The

early majority are one of the two most numerous adopter categories, making up one-third

of the members of a system. The early majority may deliberate for some time beforecompletely adopting a new idea. "Be not the first by which the new is tried, nor the last to

lay the old aside," fits the thinking of the early majority. They follow with deliberatewillingness in adopting innovations, but seldom lead.

Late majority is the next 34 percent of the individuals in a system to adopt aninnovation.The late majority adopt new ideas just after the average member of a system. Like theearly majority, the late majority make up one-third of the members of a system. Adoptionmay be the result of increasing network pressures from peers. Innovations are approachedwith a skeptical and cautious air, and the late majority do not adopt until most others intheir system have done so. The weight of system norms must definitely favor aninnovation before the late majority are convinced. The pressure of peers is necessary tomotivate adoption. Their relatively scarce resources mean that most of the uncertaintyabout a new idea must be removed before the late majority feel that it is safe to adopt.

Laggards are the last 16 percent of the individuals in a system to adopt an innovation.

They possess almost no opinion leadership. Laggards are the most localite in their outlook

of all adopter categories; many are near isolates in the social networks of their system. The

point of reference for the laggard is the past. Decisions are often made in terms of what has

been done previously. Laggards tend to be suspicious of innovations and change agents.

Resistance to innovations on the part of laggards may be entirely rational from the

laggard's viewpoint, as their resources are limited and they must be certain that a new idea

will not fail before they can adopt.

The social system

The fourth main element in the diffusion of new ideas is the social system. A social system

is defined as a set of interrelated units that are engaged in joint problem-solving toaccomplish a common goal. The members or units of a social system may be individuals,

informal groups, organizations, and/or subsystems. The social system constitutes a

boundary within which an innovation diffuses. How the system's social structure affectsdiffusion has been studied. A second area of research involved how norms affect diffusion.

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Communication Channels

Communication Channels refers to the rate and degree that people talk about and spread the news about

the innovations. Two major communication channels were described by Rogers:

Mass Media Channels

These are effective in creating knowledge about the innovation, for instance system related videos or

DVDs, or television commercials within the mainstream media

Interpersonal Channels

Person to person communication is very effective in changing peoples attitudes about the innovation

which ultimately influences their decision to accept or reject the innovation. Peer subjective evaluations

of an innovation are very influential.

Time

The time dimension is involved in diffusion in three ways.

First, time is involved in theinnovation-decisionprocess.Theinnovation-

decisionprocess is the mental process through which an individual (or other

decision-making unit) passes from first knowledge of an innovation to forming an

attitude toward the innovation, to a decision to adopt or reject, to implementation of

the new idea, and to confirmation of this decision. An individual seeks information

at various stages in the innovation-decision process in order to decrease uncertaintyabout an innovation's expected consequences.

5-Step Process:

(1) Knowledge person becomes aware of an innovation and has some idea of how it

functions

(2) Persuasion person forms a favorable or unfavorable attitude toward the innovation

(3) Decision person engages in activities that lead to a choice to adopt or reject theinnovation

(1) Innovators 2.5%

(2) Early adopters 13.5%

(3) Early majority 34%

(4) Late majority 34%(5) Laggards 16%

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The third way in which time is involved in diffusion is inrate of adoption.The rateof adoption

is the relative speed with which an innovation is adopted by members of a social system. The

rate of adoption is usually measured as the number of members of the system that adopt the

innovation in a given time period. As shown previously, an innovation's rate of adoption is

influenced by the five perceived attributes of an innovation. --- (Time/Infected Population)(4) Implementation person puts an innovation into use(5) Confirmation person evaluates the results of an innovation-decision already made

The second way in which time is involved in diffusion is in theinnovativenessofan individual

or other unit of adoption. Innovativeness is the degree to which an individual or other unit of

adoption is relatively earlier in adopting new ideas than other members of a social system. There

are five adopter categories, or classifications of the members of a social system on the basis on

their innovativeness:

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CASE STUDY

Diffusion of Innovation in Social Networking Sites among

University Students

Abstrat

Diffusion of Innovations (DOI) is a theory of how, why, and at what rate new ideas and

technology spread through cultures. This study tested the attributes of DOI empirically, using

Social networking sites (SNS) as the target innovation. The study was conducted amongstudents of the Bahauddin Zakarya University Multan. The population comprised of

people already connected to one social networking site or the other. Data collection

instrument was a structured questionnaire administered to 120 respondents of which 102were returned giving 85% return rate. Principal

Factor Analysis and Multiple Regression were the analyticaltechniques used. Demographic characteristics of the respondents revealed that most of themwere students and youths. From the factor analysis performed, it was revealed the constructs:

relative advantage, complexity, and observability of SNS do not positively affect the attitude

towards using the technology while the compatibility and trialability of SNS does positivelyaffect the attitude towards using the technology. The study concluded that the attitude of

university students towards SNS does positively affect the intention to use the technology.

INTRODUCTION

Social networking sites (SNS) such as MySpace, Facebook, Cybworld, Bebo BlackPlanet,Dodgeball, and YouTube have attracted millions of users, many of whom have integrated thesesites into their daily practices. A social network service focuses on building online communitiesof people who share interests and/or activities (Dwyer et al , 2007). The websites allowusers to build on-line profiles, share information, pictures, blog entries, music clips, etc. Afterjoining a social networking site, users are prompted to identify others in the system with whichthey have a relationship. The label for these relationships differs depending on the site-popularterms include "Friends," "Contacts," and "Fans." Most SNS require bi-directional confirmationfor Friendship.

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Diffusion is defined as the process by which an innovation is adopted and gains acceptance

by members of a certain community. A number of factors interact to influence thediffusion of an innovation (Lee, 2004). The four major factors that influences the diffusion

process are the innovation itself, how information about the innovation is communicated,

time, and the nature of the social system into which the innovation is being introduced(Rogers, 1995). The Diffusion of Innovation Theory (DOI) is used in this study to examine

the factors influencing adoption of social networking sites innovation. The theory proposed

five beliefs or constructs that influence the adoption of any innovation (Davis et al, 1989).These are relative advantage, complexity, compatibility, trialability, and observability. The

essence of the use of these constructs is to empirically test part of DOIs attributes with a

view to exploring factors that brought about the adoption of the innovation of social

networking sites (Penning and Harianto, 2007).

Therefore in this paper, the constructs that could affect the adoption of thesenetworking sites were studied. The theory of diffusion of innovation will therefore beextented to social networking among University students to determine the extent of useand acceptance with a view to knowing what could be done to prevent or allow theinhibition surrounding its use. Thus, it could be reasoned that the benefits of thesesites would accrue to adopters when barriers to

their diffusion and adoption are identified. The DOI theory was used in an attempt tomodel the adoption of social networking sites, so that the progression of its use couldbe anticipated and fully catered for.

Hence, the study analyses the adoption of social networking sites among the University

Students and their intention of using it with selected constructs such as relative advantage,complexity, compatibility, trialability, and observability.

RELATED WORKS

The social networking sites associated to a particular region differs, hence the reason forjoining these sites differs from one person to another. Although, social networking siteshave been in existence for quite a while, its adoption in Africa has recently increased.Social networking sites are built for users to interact for different purposes like business,general chatting, meeting with friends and colleagues, etc. It is also helpful in politics,dating, with the interest of getting numerous advantages with the people they meet.

Recently, the use of network siteshas increased overtime in Africa with the improvement in technology and the use ofmobile phone to surf the web and statistic have shown that 90% of people on the internetat one point in time or the other are visiting social network sites (Boyd and Ellison, 2007).

In Africa, social networking sites is becoming widely spread than it has ever been beforeand it tends to be majorly accepted by the youths. Yet the widespread adoption by users ofthese sites is not clear, as it appears that peoples perception of this technology is diverse,which in turn affects their decision to actually trust these sites or not. Moral panic is amajor problem to trusting the innovation (Adler and Kwon, 2002; Bargh and Mckenne,2004). These one-directional ties are sometimes labelled as "Fans" or "Followers," butmany sites call them Friends as well. The term "Friends" can be misleading, because theconnection does not necessarily mean friendship in the

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everyday vernacular sense, and the reasons people connect are varied (Boyd, 2004). Unsafedisclosure of information to both known and unfamiliar population, reputation ofindividuals, cyberbullying, addiction, risky behavior and contacting dangerouscommunities are issues affecting trust of SNS, though, it is adopted. The primary reasonfor its adoption may be unknown. There is obviously, a need to investigate the issue ofadoption of social networking sites in this context, because the diffusion of the innovationof these sites can be specifically perceived by the users through their attitudes and actions.

Many researchers have studied the Innovation diffusion theory, but none has applied it to

Social networking sites. Among them are Lee (2004), who applied Everett Rogers

innovation-diffusion model to analyze nurses perceptions toward using a computerized

care plan system. Twelve nurses from three respiratory intensive care units in Taiwanvoluntarily participated in a one-on- one, in-depth interview. Data were analyzed by

constant comparative analysis. The content that emerged was compared with the models

five innovation characteristics (relative advantage, compatibility, complexity, trialability,and observability), as perceived by new users. Results indicated that Rogers model

can accurately describe nurses behavior during the process of adopting workplace

innovations (Shao, 2007). Also, related issues that emerged deserve further attention to

help nurses make the best use of technology. (Lee, 2004). The application of healthinformation technology to improve healthcare efficiency and quality is an increasingly

critical task for all healthcare organizations due to rapid improvements in IT and

growing concerns with regard to patients safety.

Oladokun and Igbinedium, (2009) presented a work on the adoption of Automatic Teller

Machines (ATM) in Nigeria: An Application of the Theory of Diffusion of Innovation.The study tested the attributes of the theory of diffusion of innovation empirically, using

Automatic Teller Machines (ATMs) as the target innovation. The study was situated in

Jos, Plateau state, Nigeria. The population comprised banks customers in Jos who usedATMs. The sampling frame technique was applied, and 14 banks that had deployed

ATMs were selected. Cluster sampling was employed to select respondents for the

study. Data collection instrument was a structured questionnaire administered to 600respondents of which 428 were returned giving 71.3% return rate. Principal FactorAnalysis, and Multiple Regression were the analytical techniques used. The demographic

characteristics of the respondents revealed that most of them were students and youths.

From the factor analysis, it was revealed that the respondents believed in their safety inusing ATM; that ATMs were quite easy to use and fit in with their way of life; that what

they observed about ATMs convinced them to use it and that ATM was tried out before

they use it.

Zhenghao et al, 2009 worked on the 3G Mobile Phone Usage in China: Viewpoint from

Innovation Diffusion Theory and Technology Acceptance Model. The paper analyzed the

reasons behind Innovation Diffusion Theory (IDT) and Technology Acceptance Model(TAM) perspectives. Some suggestions were also given to 3G business operators and

researchers.

Dwyer et al, 2007 analysed an online survey of two popular social networking sites,Facebook and MySpace, compared perceptions of trust and privacy concerns, along

with willingness to share information and develop new relationships. Members of both

sites reported similar levels of privacy concern. Facebook members expressed

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significantly greater trust in both Facebook and its members, and were more willing to

share identifying information. Even so, MySpace members reported significantly more

experience using the site to meet new people. These results suggested that in onlineinteraction, trust is not as necessary as the building of new relationships, as it is in face to

face encounters. They also showed that in an online site, the existence of trust and the

willingness to share information do not automatically translate into new social interaction.

This study demonstrated online relationships can develop in sites where perceived trustand privacy safeguards are weak

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RESEARCH MODEL

Figure 1 shows the research model. Relative advantage indicates the usefulness ofan innovation; compatibility is the degree to which an innovation is perceived as consistentwith existing values, past experiences, and the needs of the potential adopter;complexity is the degree to which an innovation is perceived as relatively difficult tounderstand and use; trialability is trying out or testing an innovation so that it makesmeaning to the adopter; and observability is the degree to which the results of an

innovation are visible to others.

Relative AdvantageH1

ComplexityH2

Compatibility

H3

Trialability H4

Observability H5

H6Attitude Intention to use

FIGURE 1: Researchmodel

These constructs ought to affect the intention to use a particular innovation which in this

case is Social Networking sites. Thus, the model indicates that the five constructs: relative

advantage, complexity, compatibility, trialability and observability of using social network

sites would affect the intention of the adopter to use these sites. The hypotheses proposedfor this study are as follows:Ho1: The relative advantage of using social networking sites does not positivelyaffect users attitude towards using the technology

Ho2: The complexity of the use of social networking sites does not positively affectusers attitude towards using the technology.

Ho3: The compatibility of social networking sites with the adopters values doesnot positively affect users attitude towards using the technology.

Ho4: The trial ability of social networking sites does not positively affect usersattitude toward using the technology.

Ho5: The observability of social networking sites does not positively affect users

attitude towards using the technology.

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Ho6: The attitude towards social networking sites does not positively affect users intention

to used the technology

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Sample and Procedure

The six attributes measured users perception regarding the advantage, trust and security of SNS

to the University students and most especially the rate of adoption of the innovation. Relative

advantage, complexity, compatibility, trialability, observability and trust were measured to

access individual perceptions and adoption of effectiveness of the innovation. The survey

subjects were mainly students in University. A close-ended questionnaire was designed to collect

relevant data on the relative advantage of using social networking sites, whether any

complications had been encountered from the use of these sites, and on the suitability of usingthese sites with the belief system, moral and ethical values of the respondents. Information on

how the experiences of the respondents with the use of social networking sites have affected

their intentions regarding the continuous use the SNS technology. One hundred and twenty (120)

questionnaires were administered to students in the University, out of which a hundred and

two were returned and eighteen were not returned.

Demographic Information of the Sample (n=102)

Variables Frequency Percent (%)

Gender

Male 58 56.9Female 44 43.1

AgeUnder18 0

19-29 102 100

Period of use of Social networksites

Less than a month 2 2.01-6months 16 15.76months to a year 28 27.5

1-2years 34 36.32-3years 12 11.8Over 3years 7 8.67

How many friends in total do you have in all of your networking sites?

1-20 7 6.921-60 18 17.661-100 38 37.3100+ 39 38.2

Do you believe visiting these sites is a waste oftime?Yes 5 4.9

Maybe 29 28.4No 68 66.7

TABLE 1: Demographic Information of the Sample (n=102)

As shown in Table 1, there were more males than females at 56.9% to 43.1%. All of the respondents were

between the ages of 19-29 years.


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Data Analysis and Results

The data collected were analysed using Cronbachs alpha which was to determine the

internal consistency and reliability of the individual and multiple scales. Cronbachs alphawas used in this study because every item in the questionnaire measured an

underlying construct. Cluster sampling was adopted; this involved the division of the

population into clusters or groups and drawing samples from the clusters. A cluster in

this study was represented by the number of users who are parts of one socialnetworking site or the other. The validity of the measures was verified by observing the

correlations between the items on the various scales. All pre-existing constructs used in

the diffusion theory met the criteria of validity and reliability except trust which is anewly introduced construct.

Construct Cronbachs No of items that make up

Alpha the constructs

Relative advantage 0.415 4

Complexity 0.359 3

Compatibility 0.754 3

Observabilty 0.320 3

Triability 0.562 3

TABLE 2: ReliabilityTest

Table 2 showed the Cronbachs alpha that was computed for the items that made up eachconstruct used in this study. The alpha values for the 5 constructs (from 0.32 and 0.75)indicated that the items that formed them do not have reasonable internal consistencyreliability. The items which were deleted had alpha values that were either lesser than 0.3or higher than 0.75. Items lower than 0.3 might affect the consistency of the results offurther analysis. Items with alpha values over 0.73 were probably repetitious or added upto be more than what was required for the construct. The scores used for the constructsin this study were standardized using SPSS package for the regression analysis.

Tables 3 and 4 presents the result from the multiple regression carried out using thefive constructs: Relative Advantages, Complexity, Compatibility, Observability,

Trialability as the independent variables and Attitude as the dependent variable. This isdone to determine the best linear combination of the constructs for predicting Attitude.


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Model Sum of

Squares

Df Mean

Square

F Sig.

Regression 2.917 5 .583 2.338

Residual 23.955 96 .250 0.48

Total 26.873 101

TABLE 3: ANOVA fortheConstructs

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant)

Relative Advantage

Trialability

Compatibility

Observability

Complexity

2.276 .533 4.269 .000

-.028 .052 -.054 -.548 .585 .958 1.043

-.112 .050 -.217 -2.235 .028 .987 1.013

.207 .092 .221 2.242 .027 .956 1.046

.112 .080 .142 1.407 .163 .908 1.102

-.111 .080 -.140 -1.396 .166 .918 1.090

TABLE 4: Coefficient oftheConstructs

Table 4 presents the ANOVA report on the general significance of the model. As p is

less than

0.05, the model is significant. Thus the combination of the variables significantly predictsthe dependent variable. Table 5 shows the beta coefficients for each variable. The t and pvalues present the significance of each variable and their impact on the dependent variable(attitude). From table 4 only trialability and compatibility had significant impact onrespondents attitude, with compatibility having the highest impact on attitude. Themultiple regression equation for this analysis is given as

Attitude = 2.276 0.28 (Relative Advantage) -0.112 (Trialability) +0.207 (Compatibility)+ 0.112 (Observability) 0.111 (Complexity) (1)

Tables 5, 6 and 7 present the result from the multiple regression carried out using Attitudeas the independent variable and Intention as the dependent variable. This was done to

determine the best linear combination of Attitude for the prediction of Intention


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Model R R Square Adjusted

R Square

Std. Error of

the Estimate

1 .050a

.003 -.007 .720

Predictors: (Constant), Attitude

Dependent variable: Intent

TABLE 5: Model Summary for attitude and intent

Model

Sum of

Squares Df

Mean

Square F Sig.

1 Regression

Residual

Total

.132 1 .132 .254 .615a

51.829 100 .518

51.961 101

Predictors: (Constant), Attitude

Dependent Variable: Intent

TABLE 6: ANOVA for attitude and intent

Model

Unstandardized

Coefficients Standardized Coefficients

T Sig.B Std. Error Beta

1 (Constant)

Attitude

1.248 .149 8.389 .000

.048 .094 .050 .504 .615

Dependent Variable: Intent

TABLE 7: Coefficients for attitude and intent

From table 6, it can be seen that R square value is very low; hence the variance in the

model cannot be predicted from the independent variable, attitude. Table 7 gives theANOVA test on the general significance of the model, as p is greater than 0.05, the model

is not significant. Thus, attitude of the respondents cannot significantly predict the

dependent variable, Intent. Table 7


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shows the coefficients of attitude, and from the table it can be seen that attitude has a very low

impact on Intention, the small t value and corresponding large p-value shows this.

The regression equation for this analysis consequently is:

Intention = 1.248 + 0.048(Attitude).

Test of Hypotheses

Table 8 shows the result of the hypothesis tested against p values that were obtained from theabove results.

Variable Beta P

Relative Advantage -0.54 P


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RelativeAdvantage

Complexity

H1 (-.054)

H2 (-0.217)

Compatibility

H3 (0.221) Attitude H6(0.0498) Intention to use

Trialability

Observabilit

y

H4 (0.142)

H5 (-.140)

DISCUSSION OF FINDINGS

Relative Advantage ( = -0.54, p < 0.05) does not have significant positive effect on theattitude towards using social networking sites. From the responses, the advantages ofusing these sites do not make them prefer social network sites use to the previous oneused. Some of these advantages include speed, efficiency, availability, ease of use,faith in the security of their personal information. The contribution of the Complexityconstruct ( = 2.21, p < 0.05) was not also significant to the model and hence not

supported in this study. The complexity of a technology affects how well thattechnology diffuses in a social network system because if the technology is easy to use,more people are likely to adopt its use. Findings from this study suggested that socialnetworking sites were not quite easy to use and are not more likely to be more widelyadopted. The Compatibility construct ( = -1.40, p < 0.05) was found to positivelycontribute to the DOI model. This suggested that the compatibility of usage socialnetworking sites to the lifestyle of the respondents was important. The use socialnetworking sites now belong firmly to the modern way of doing things.

The Observability construct ( = 1.42, p < 0.05) also have impact on the attitude towards


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the use of these sites. It also showed that people paid more attention to it than might

have previously been the case. The Observability construct was not simply about watching

others using the technology, but (as the results from the factor analysis revealed) involvedperception and discernment, usually brought on by the influence of others. Of the five

constructs, Trialability ( =-0.217, p < 0.05) had the highest impact on the attitude towards using social networkingsites, it was positively significant. The results implied that the respondents have attemptedto try SNSs before adopting its use. This finding suggested that people just decide to adoptand use social networking sites after testing it. This could be because of their already

perceived notions as to the advantages of using these sites. Since the construct is verysignificant in this study, it meant that potential adopters of these sites may well benefitfrom trial demonstrations as an introduction tousing the technology. This would help eliminate uncertainty about socialnetworking sites,

improve confidence in its use and make its diffusion more widespread. The Attitude ( = 0.050,

p < 0.05) towards SNSs positively and significantly affected the Intention to use the

technology. The low impact of Attitude on Intention to use social network sites expressedthe importance of how Attitude could affect the Intention to use social networking sites. A

positive attitude meant that a potential adopter or a past user of social sites would have the

Intention to use it in future and vice versa. The contribution of Attitude to Intention in theDOI model has been in line with the findings of other studies such as those of Davis et al (1989).

The findings showed that attitudinal dispositions do not have significant influence use of socialnetwork sites. All the five attitudinal constructs have strong influences on adoption and

intention to use social networking sites. Complexity also does not have significant

relationship with intention to use it. Analysis for compatibility revealed that the use of social

networking sites was compatible with the lifestyle of the respondents. The study also revealedthat the use of social networking sites is widespread and a current practice today because of

its usefulness but because of its compatibility with users previous values. The

implications of observability construct showed that the observations made by the respondentseffectively convinced them not to use SNS. Influence was apparently a factor for using social

networks, probably because the students quickly get influenced by their colleagues. Another

construct that influenced attitude and trust of SNS supported in this study is trialability. Potentialsocial networking sites adopters will be more inclined to use it if they can try it out first.

These findings have shown what the Diffusion of Innovation model in the diffusion of Social

networking sites. It is therefore noteworthy for builders of these sites to examine the attributes of

the model to see how they could improve on the use of these sites.


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Conclusions

This study analysed the issues surrounding the adoption of social networking sites (SNS) usingdiffusion of innovation theory (DOI) to test its adoption among University students. Five major

constructs: Relative Advantage, Complexity, Compatibility, Observability and Trialabilitywere used to test the impact on the attitude and trust regarding SNS and to determine how

attitude would impact on the intention to use it. From the results, it could be said that the

relative advantage of using SNS; how hard it was to use; how compatible it were with thelifestyle of the users; how much has been registered about SNS by the users; and whether

social networking sites could be tested before consistent use, were issues that influence users

attitude towards intention it use. The Attitude of a user would later affect his/her intention to usethe site. Since trialability and compatibility had the greatest impact on attitude, it follows

that the social networking sites follow the students lifestyle and would assist in

consummating greater diffusion of social networking sites in among students and opportunityfor adopters to experiment with the system before making any long-term commitment. Future

studies could consider the inclusion of specifics on innovation diffusion with respect to

geographical location and the cultural considerations of another area. The diffusion of social

networking sites in Multan could also be studied from the perspective of non-users, to determinewhy they persist in non-usage of this technology.

Documents

Diffusion of Innovation Finsl Work Report