Evaluation of the Acceptance of Social Media

11246 words (45 pages) Dissertation

18th May 2020 Dissertation Reference this

Tags: Information Technology

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Abstract

This research examines closely data mining techniques for identifying patterns and attitudes of different user groups on social media. Data mining techniques allow the researchers and practitioners to extract and analyse large data sets and identify trends on the behaviour of large user groups.  Users of different social media platforms are of different backgrounds, age, gender, education, material status, income/social status and have different purpose and motives, e.g. access to entertainment, news, social connection and represent large user groups.  We will focus on usability trends for these user groups and will aim to understand how users attitude will adapt to changing circumtances (change this).  The results will be evaluated and compared to existing literature.

Contents

Declaration of Authorship

Acknowledgements

Abstract

List of Figures

List of Tables

Abbreviation

Chapter 1 Data Mining and Social Media

1.1 Introduction

1.2 Project Objectives and Aim

Chapter 2 Literature Review – Social media

2.1 The Impact of Social Media on People’s Personal and Social Life.

2.3 Financial Backgrounds of Social Media Users.

2.4 Effects of Social Media on Older Age Group

2.5 Growing Trends of Social Media

2.6 Influence of Politics on Social Media

2.7 Social media trends ( write about the platform that have failed, e.g goole shoe laces)

2.8 Negative impacts of Social Media

Chapter 3 Literature Review – Data Mining

3.1 What is data Mining?

3.2 Sentiment Analysis ( check this, maybe call it trend analysis)

3.4 Machine Learning Classifications algorithms used for Social Media

3.5 Supervised learning based methods

3.6 Supervised Learning : Regression method

3.7 Supervised based methods algorithms

3.8 Logistic Regression

3.9 Previous Trends on Social Media

Chapter 4 Literature Review Adoption

4.1 Introduction

4.2 Adoption Process

Chapter 5 Research Model

5.1 Introduction

Chapter 5 Methodology

5.1 Introduction

5.2 Justification of the Research Methodology Adopted

5.2.1 Data collection

5.2.2 Literature Research

5.2.3 Descriptive Statistics Analysis

5.2.4 Reliability – Cronbach’s Alpha

5.2.5 Univariate and Multivariate Analysis

5.2.6 Data mining techniques

5.2.7 Logisic regression

5.2.8.2 Sentiment Analysis and Survey

5.3 Requirements Analysis

5.3.1 Project Scope

5.3.2 Project Deliverables

5.3.3 Project Risk Assessment

Chapter 6 Results & Discussions

6.1 Introduction

6.2 Evaluation of different user groups (maybe should be called descriptive statisctic analysis)

6.3 Demographic factors vs innovativeness

6.3.1 Gender vs Innovativeness

6.3.2 Age group vs Innovativeness

6.3.3 Marital status vs Innovatiness

6.3.4 Education level vs Innovatiness

6.3.5 Annual Income vs Innovatiness

6.4 Reliability Test

6.5 Pearsons Correlation

Section 5.0 Ethics

5.1 Professional, legal

5.2 Ethics

5.3 Social Issues

Section 6.0 Project Plan

Section 7.0 Conclusion

List of Figures

Figure 1.1 Three Stages of social media mining or analytics

Source: https://www.researchgate.net)

Fig 1: Three Stages of social media mining or analytics

Fig 2: Profile of social media news readers

Fig 3: Social networks share of time

Fig 4: Using Scrum in your research

Fig 5:  3 x 3 Risk matrix

Fig 6: Cluster Analysis

Fig 7: Project Schedule

Fig 8: Gantt Chart

List of Tables

Table 1: Table of risks with high impact

Chapter 1 Data Mining and Social Media

1.1             Introduction

Social Media is one of the most successful technologies in the current marketplace and widely adopted around the world.  In many ways Social Media has enhanced the way societies interact with each other. The Social Media platform has increased the adoption of knowledge economy during the current the information and digital age. Social Media has increased the ability to transfer information freely and provide instant access to the knowledge which previously would have been difficult or impossible to obtain.  (Sayre et al. 2012)

Social media is very popular with all age groups, although due to legislation in various countries for example United Kingdom, the use of Social media is restricted to 16 and over.

Social media encourages users to promote usage of online communication, sharing information and thoughts, view the latest showbiz news and fashion trends. Social Media is also actively used to promote and advertise retail products.  (Edosomwan.et al 2011)

Social Media has taken over our lives. Social Media platforms actively monitor and capture User preferences, for example how regularly does the User follows pages, trends, lifestyle and fashion. This allows the retailers to invest resource to capture Users habits and trends to ensure relevant marketing strategies are adopted to promote products to the User. (Blank and Lutz, 2017).

Social media has a major influence on general public’s political views.  This is evident from the recent American general elections and the Arab spring.  With reference to the recent American elections, a third party app was launched by Facebook, called Open graph.  The data could be accessed by external developers and analysed by using various data mining techniques to establish marketing strategies to target the voters.  (Meredith, 2018, lewis. et al 2018 and Lee et al. 2019). 

Data mining is a technique used to discover patterns and trends to predict in future outcomes, Data mining allows to extract and analysis data from large data sets. Different data mining techniques can be used to gather information and predict future trends, sales and adoption. Data mining techniques are used further to build machine learning models, these are then used to power artificial intelligence (AI). (Injadat et al. 2016 and Verbraken et al. 2014).     Figure 1 shows the typical process of data mining of social media.  Data is gathered from Social media sources, data is cleansed, the unstructured data becomes structured, analysis is performed, various analysis i.e. sentiment analysis, perform analysis could be performed depending on the type of data to be collected.  In the final stage, the summarised data presents the results, the results help to predict the future trends.

Figure 1.1 Three Stages of social media mining or analytics

Source: https://www.researchgate.net)

1.2             Project Objectives and Aim

Adopting data mining techniques will assist to determine the acceptance of Social Media.   This project will examine the literature on Social Media and Data Mining to evaluate various Social media applications and Data Mining techniques.   The aim of this project is to investigate the factors that influence the acceptance of the following social media platforms Facebook, Snapchat, Twitter and LinkedIn by adopting data mining techniques.

The objectives of this project are:

  1. To evaluate the different factors which influence the use of social media.
  2. Discuss various data mining techniques available and adopt the most relevant data mining technique for Social Media Acceptance.
  3. Evaluate the current trends in the acceptance of Social Media for Facebook, Snapchat, Twitter and LinkedIn.
  4. Determine a Technology Acceptance Model (TAM), to establish the acceptance of Social Media. Then further an overall technology acceptance model is established by evaluating additional factors such as Demographics and social economic factors, experience, ease of use, confidence and technical changes. 

Chapter 2 Literature Review Social media

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2.1.           The Impact of Social Media on People’s Personal and Social Life.

The use of Social media promotes a channel for communication, interaction, discussion and connectivity among users.  Such activity encourages information and news sharing among various users with different backgrounds (Kaplan et al 2010).

According to Edge (2017) and Rajeev (2015), the use of social media has dominated our lives and we enjoy sharing personal life’s and activities to the public for open viewing. Al-Sharq et al. (2015) and Shabir et al. (2014), suggests that multiple studies have been carried out by the researchers on the student’s social media usage and its negative and positive influence on their academic performance, social behaviour and education. Khan (2012), carried out research on impacts on the students due to social media website usage and suggested that the social lifestyle of the youth is greatly influenced by social media.

According to Rajeev (2015), the usage of social media changed the ways of interaction, socialising, communication in the educational institutions, workplace and other places. The social media allows the students in the academic institutions to share content and communicate with other students through the robust connectivity provided. They can participate in posting comments, images, pictures, take part in social discussions and share their ideas. Student’s life is greatly influenced by the use of social media such as Facebook, Instagram, Twitter and Snap Chat.

According to Khurana (2015), the younger generations are naive and are heavily influenced   by peers and may not be aware what is in their best interest.  Khurana suggests social media lacks in providing an opportunity to build strong personal relationships face to face.

Williams (2012), Xiaojun et al. (2019) and Sheldon et al. (2019) concluded that social media influences human and social feelings which can result in an emotional and mental breakdown. It can also lead to a crisis in the marital life. It is also said that the inefficient usage of social media can result in breakups in the relationships of couples. (Sheldon et al. 2019).   The use of Facebook influences relationships and brings changes in the areas of human interaction.  Overall, social media can be used by females and males for various activities and reasons, (Abbasi 2019).  Chen et al. (2016), stated that if social media is used wisely, it can act as a righteous platform for students by using it for collaborative learning.

Various sentimental issues concerned with religion or community makes people get inclined towards a specific political belief. Such beliefs get reflected into political views posted on various social media platforms. When someone finds similarity of opinions between a political parties then the User naturally gets inclined towards it, (Lee et al. 2019).

Gross (2004) and Kim et al (2019)  concluded that as social media usage has drastically increased over the past few decades and this has introduced a rise to a lot of conjectures like for example:

  1. Social media usage has led to isolation and depression among adolescents.
  2.  The adolescent uses social media for so many different unknown purposes.
  3. The social media use predictions are that girls devote most of their time chatting while boys use the social media for browsing the internet.

These social media technologies can be accessed through various devices like smartphones and computers from anywhere and anytime, (Giunchiglia et al 2018 and Levine et al 2019). Mostly social media is used to stay in contact with family and friends and share thoughts, (Shepard et al. 2019). The usage of social media technologies for example Facebook, Snapchat, LinkedIn and Twitter can lead to dependency and addictive behaviour among the students which is a matter of concern (Killian et al. 2019 and Jasso-Medrano et al 2018). It can also cause lead to sleeplessness, depression, and self-esteem. (Brunborg et al 2019).

Chukwuere and Onyebukwa (2017) states that the new age of information brought by the change in the form of the internet has created a new media or social world. It has created a new alternative world of communication, interconnectivity, and information that cannot be created by the use of face to face methods by the people in various walks of life like students, professionals, elderly, children and the others. 

2.2.           Financial Backgrounds of Social Media Users.

According to Blank and Lutz (2017), in terms of economics, it is seen that most of the social media users are from the economically weaker section with most of them with income below the national average. When the same category is analysed further by age, the limited users and the non-users are seen to be over the age of fifty-five years while the number of frequent users of social media is found to be more in the age group of below fifty-five years. So, it can be stated that the usage of social media is predominant among younger people who are unemployed.

2.3.           Effects of Social Media on Age

Shabir et al. (2014) states, usage of social media influences different age groups.  Jha et al. (2016) and Ozimek et al. (2016) concluded that people in all the age groups and professions widely use the social media technology such as Facebook. Lowisz (2014) has concluded that almost 50% of the people around the world get access to news and current events through social media platforms. Recent studies by Pew Research Centre in 2018 conducted a study to determine the use of Social Media of consuming news by various profiles as illustrated in figure 2.1.  

Fig 2.1: Profile of social media news readers (Source: Pew Research Centre, 2018

https://www.journalism.org/2018/09/10/news-use-across-social-media-platforms-2018/)

 

The above figure illustrates that Males are more frequent consumers of social news compared to females. The most frequently used application according to the study was Reddit. Females mostly used Facebook and Snapchat. The most used application by Age range for 18-29 was Snapchat and whereas LinkedIn was mostly used by educated working age group of 30-49. Social media applications are widely used between white and non-white backgrounds.

Whereas Moss et al (2013), explained that older age group of people are showing great interest for adopting new networking tools which allows to share, build a chain of contacts, pictures, news, status and updating videos. According to Finn (2010), Social media helps to enhance the opportunities and skills related to communication, searching of information, sharing of knowledge and also relationship formation among elderly people. According to Lin and Chou (2013), Social Networking sites help older age groups to fully express their point of view, indulge in various discussions and also stay well connected with the society.

Tennant et al (2015), suggests that the participation of elderly people in various Social Networking sites helps to empower them and also provides them with the feeling of self-efficiency and connection. A very interesting set of a trend among elderly social networking sites users is the medical sector. It provides a good medium of medical support. All elderly patients could gather information on issues relating to health (Alhaddad 2018). According to Lee et al (2011), the elderly generation using Social Networking sites may face four-dimensional obstacles such as Interpersonal obstacle, Intrapersonal obstacle, functional obstacles, and structural obstacles.

2.4.           Growing Trends of Social Media

Whereas another study taken place by comScore illustrated the popularity of Social Media by analysing the shared time on the following Social Media applications Twitter, Facebook, Instagram and Snapchat, as illustrated in figure 2.2.

Fig 2.2: Social networks share of time.

(Source: comScore, https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/)

Fig 2.2 illustrates the adoption of different social media apps in different countries. Twitter, facebook, Instagram and snapchat usage is compared in different countries. The analysis shows Facebook is widely adapted and secondly Instagram.  In the UK, France Canada and USA Snapchat also adopted.  Twitter is adopted at a very small percentage in USA, Canada, Spain and UK.  In UK mostly Facebook is used followed by Snapchat, Instagram and Twitter.

According to Gemmill and Peterson (2006) and Stathopoulou et al (2019), the use of social networking sites among many college students is growing day by day all across the globe. In modern times, social networking sites play an important part among younger generations. The trend of using social networking sites among college/university students is significantly higher as compared to other groups of the population. Youths of today are attracted towards the new Social Media technologies, in addition to this the Youths have a chance that social media can offer creating a chain of social networks. Bicen and Cavus (2010), studied trends related to the use of social networking platforms in students from the department of instructional technology and computer education. Their study concluded that the sharing and use of knowledge through internet forms a vital part of college/university student. The study also revealed that Facebook and Live Spaces are the most favourites and commonly used networking sites among the students. As mentioned by Nicole Ellison (2008), that the level of the utilization of various social networking sites among students is increasing rapidly from many years and also stressed on the fact that the extent of use of social Networking sites differs among various age groups. In his study,  he found that the majority of Networking sites users from 18-19 years old have friends more than 200 and users above 30 years old have approximately less than 25 friends on these sites.  Almost all the users of the various social networking sites spend around five hours or less time on networking sites per week. In his study Facebook came across the popular.  Younger generations spend more time as compared to the older age group. He further added that mostly females and young students are likely to set access restrictions on their individual profile.

North et al. (2008) and Khurana (2015) also concluded that compared to females, males spend more of their time on social networking websites.  He also stated that Social Media sites are very addictive, as result users are having difficulty to concentrate on their work.  Users’ addiction drives them to log in and jump across from one site to another. Some Social Media sites have a benefit, as users have become academically challenged by the use of these sites. Some Individuals have set their own limits as to when and when not to access these sites, but we witness very few groups of users opt out of using Social Media sites.

{Write Paragraph on previous Social Media Apps which failed}

2.5.           Influence of Politics on Social Media

Deep et al. (2018) suggests there is an incline on social media being used in a political context.  This is being used by citizens and the political institutions, i.e. political parties, politicians.  In the best interest of the political intuitions, it is best to remain active on social media, especially during the time of political elections, as this promotes their political parties as they can spread the positive objectives in less time. Deep et al. (2018) also states

“Social media thereby represents the ideal vehicle and information base to gauge public opinion on policies and political positions as well as to build community support for candidates running for public offices.”

Politicians have adapted to social media in a very short time, this allows the politicians to practice democracies in a modern approach.  This approach has allowed them to

 “Entering into direct dialogs with citizens and enabling vivid political discussions.” Deep et al. (2018)

Communication via social networking sites allows the politicians and their parties to build a closer bond with the potential voters.  The reach to each citizen is targeted, without having any intervention from mass media.

“Reactions, feedback, conversations and debates are generated online as well as support and participation for offline events. Messages posted to personal networks are multiplied when shared, which allow new audiences to be reached. The Internet is seen as an advance in communication between citizens and elected politicians, with the growing access to information, the chance for feedback, and transparency.” Deep et al. (2018).

Baker (2014) emphasised that the social media technologies such as YouTube and Facebook are providing an enhanced level of participation on citizens in political arena. Internet has become an increasingly popular and reliable tool to the citizens not only to acquire knowledge but to involve directly into it.  While media is regarded as a popular factor in the contemporary lifestyle, it also a very popular platform to obtain news feed on politics. He also pointed out that these social media websites have become even more influential as the number of users on these platforms is increasing by leaps and bounds making the platform part and parcel of their daily lives.

Stieglitz (2014), proposed a structure concerned with social media analytics with respect to politics. He concluded that Social Media sites such as Facebook and Twitter have the power to enhance political engagement. He pointed out various approaches with regard to data interpretation, data tracking on the basis of different methods on data analysis that might help in generating better understanding into the political analysis or discussions on social media technologies.

Floss (2013) states, social media has serious effects on the confidence level of citizens with regard to various political dimensions. Considering researches on political science which develops a theory of discrepancy out of cognitive approaches, he discussed about the choice of citizens regarding how the various political institutions should carry on their tasks and how they should influence the political outcomes with the help of social media

Satterfield (2016) states that there has been a lot of reforms in political landscapes, this has dramatically changed over last couple of decades.  The internet has played a large role in transforming online influences on politics. Social media is now considered one of the main influential factors in political campaigns, as this effects the way people feel about political issues. There are regular updates on their parties and their party supports post their views on Facebook and Twitter. Once a tweet had been put on twitter, this spreads dramatically.  Each political party has their own pages, this is a method to broadcast and spread propaganda.  Political parties will also use their sites to collect any donations.

2.6.           Negative impacts of Social Media

Makinde et al. (2016) concluded that various researches have proven that the influence of the Social Media has a huge growth in the economy. However, there are also negative consequences such as cyberbullying, terrorist attacks, wasting precious time and exploitation in various forms. It was also concluded that the use of the social media has resulted in many incidences of violence and abuse against the females and minors by meetings arranged via social media by the people of predatory nature.

This chapter described the current uses of Social media and the current trends. The next chapter will explorer the different Data mining techniques which can be adopted to determine the acceptance of Social Media.


Chapter 3 Literature Review Data Mining

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3.1.           Data Mining

There are various Data Mining techniques which can be adopted to evaluate the acceptance of technology and trend analysis. This chapter will discusses various data mining techniques which can be adopted.  

3.2.           Sentiment Analysis

Sentiment Analysis is a data mining technique, this is also known as opinion mining.  This approach uses data mining techniques to extract and analyse data to recognize the subjective opinion of the document.  Sentiment analysis is a natural approach to Natural language processing (NLP), this identifies the emotional tone behind the actual text.  Sentiment analysis helps businesses and organizations to collect perceptions from unorganised and unstructured text that comes from online sources such as social media sites, emails, blog post and forums. Opinion mining extracts the polarity (positivity, negativity and neutral) with in a text.

Sentiment Analysis is becoming a more utilized tool for upcoming businesses, politics and topics of interest.  This tool allows to identify the Users insight about how they feel about a certain topic.  Pandey et al (2007) suggests sentiment analysis methods can be categorized in  different categories these are, lexicon-based methods, machine learning -based methods, and hybrid methods, these could be sub-divided in to further sub categories as illustrated in figure 3.1.

Figure 3.1 Sentiment analysis methods

Adopted from https://www-sciencedirect-com.ezproxy1.hw.ac.uk/science/article/pii/S0306457316302205

3.3.           Machine Learning Classifications algorithms used for Social Media

Machine Learning (ML) classification algorithms are used for sentiment analysis in Social Media.  ML algorithms build a mathematical model on sample data, known as training data, this helps to make predictions and decisions without being programmed to perform a task.  ML algorithms are used widely in our day to day life’s, such examples are face recognition, speech recognition on smart phones and laptops, spam email filtering.  Machine learning tasks are categorised into broader categories.  These are unsupervised, supervised and semi supervised, (Gosal et al 2019).

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Zoonen et al (2016) states that Supervised learning is the data mining of the inferring a function from a labelled training data. This maps an input to an output based on the input-output pairs. Then in a supervised learning algorithm this analyses the training data and produces an inferred function.  Supervised learning can be divided into two categories, for example Classification and Regression.  The classification predicts the category the data belongs to for example email filtering, churn prediction, sentiment analysis.  The regression predicts a numerical value based on previous observed data, for example house prices, stock price and height- weight. (Shetty, 2018)

According to Shetty (2018), algorithms learn from the labelled data in supervised learning.  The algorithm determines which new data should be given best suited label according to the pattern, also associating the patterns created with any new data which is not labelled.

Wallace (2007), states “people are often prone to making mistakes during analysis” when they are manually trying to establish relationships between datasets.

There are some drawbacks to supervised learning algorithms, as ML is an actual process of learning from a set of rules from training sets, during the initial step of collection of data, there could be errors encountered at this stage, as the data collected by the “brute force” method, which measures everything available in the hope accurate data will match.  As a result, the data matched can have missing and impossible values, Wallace (2007)

3.4.           Logistic Regression

Logistic regression is a type of predictive model which is used in the analysis because of the presence of dichotomous variables (Use Social Media / No Use Social Media) (Thomas et al., 2006); and, as Press and Wilson (1978) have suggested, the probability of usage must lie between 0 and1 (Lasser et al., 2005). In general, the logistic regression model has the form

 

where p is the probability of the outcome of interest, β0 is an intercept term, β0 is an intercept
term, βi is the coefficient associated with the corresponding explanatory variable Xi. (Thomas
et al., 2006; Bienstock et al., 2007; Shin, 2007).

According to Wojtusiak, 2014 logistic regression is a “powerful algorithm” when compared to other machine learning algorithms such as support vector machine (SVM).  He also states the popularity of logistic regression can be accredited to its easiness and interpretability of model parameters.


Chapter 4 Literature Review Social Media Adoption

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4.1.           Introduction

Adoption refers to the stage at which a technology is selected for use by an individual or an organisation (Rogers, 2005). Diffusion refers to the stage at which it spreads into general use and application. Adoption of new technology is affected by many factors; for example, technical infrastructure, the cultural attitude and social pressure to new technology, the radicalism of the technology, its ease of use and its cost. (Zhang, 2018; Gong, 2009; Castaneda et al 2007). Many theoretical models have been proposed to determine how to analyse the significance of these factors for technology adoption. (Siamagka et al 2015; Maldifassi et al 2009 and Choi et al 2008).  The purpose of this study is to establish a generic technology acceptance model for Social Media. To develop a solid theoretical research framework, this chapter will review the relevant literature and provide an extensive study of the technology acceptance models most frequently employed in determining the technology adoption of information systems.

4.2.           Adoption Process

The Diffusion of Innovation (DOI) theory (Rogers, 2005) clarified how adoption takes place over time within a social system (Shepard et al 2019 and Gong, 2009). Rogers (2005) defines Diffusion of Innovation as the process by which an innovation is communicated through certain channels over time among the members of a social system and Innovation as an idea, practice or object that is perceived as new by an individual or another unit of adoption.

The diffusion of innovation theory tries to explain the innovation decision process, the factors contributing to the rate of adoption and the different categories of adopter. (Lee et al 2009 and Zhu and He, 2002) It assists in predicting the likelihood and rate of adoption of an innovation. Rogers (2005) described adoption as a normal distribution based on timing of adoption. DOI Theory allows diffusion analyses to be carried out on the individual level or the social system level. Chen et al (2002) observed that Rogers’s (1962, 1983, 1995) diffusion of innovation model is one of the theories most frequently applied in subject areas such as anthropology, sociology, education, communication and marketing. As Gong (2009) showed, the factors can contribute to further analysis:

“These factors can be used to compare the adoption rates of different innovations as well as the relative extent to which an innovation is adopted within communities, countries, or other social units of different economic, demographic and cultural characteristics.”

Parker (2009) pointed out that the DOI theory is an overarching framework which seeks to clarify the social and relational elements of innovation diffusion and how it transpires over a period of time in the social system. Rogers (1983) focused on four significant elements that influence the rate of adoption of an innovation. Rogers (1995) presented several adoption/diffusion theories. These are now outlined.

Innovation Decision Process theory – This theory proposes that the prospective adopters of technology innovation over a given period of time go through five phases of the diffusion process which are Knowledge, Persuasion, Decision, implementation and Implementation and Confirmation.


Individual Innovativeness theory – This theory applies to those individuals who are risk takers or otherwise innovative who will adopt an innovation relatively early in the process of adoption/diffusion (Rogers, 1995).


Rate of Adoption theory– This theory accounts for the rate of adoption, measuring the diffusion taking place over time with innovations accumulating through a slow, gradual growth period, followed by dramatic and rapid growth, gradual stabilization and final decline (Rogers, 1995). According to Rogers (1983), the adoption rate of an innovation is affected by innovation characteristics, the successful marketing of the innovation’s benefits through appropriate communication channels, the time elapsed since the introduction of the innovation, and the social system in which the innovation is to diffuse. Mahajan et al. (1990) argued that the main focus of DOI theory is on communication channels. Social influences rely on media, interpersonal channels or nonverbal observations to obtain information about a new innovation. The Rate of Adoption theory, an S-curve representing the rate of adoption of an innovation over time, is depicted in Figure 4.1.


Figure 4.1 Rate of Adoption Theory (Rogers, 2005)

Extracted from Rogers , E.M., (1995). “Diffusion of Innovations” The Free Press, New York. PP. 106


Perceived Attributes theory:This theory suggests that there are five attributes which allow innovation to be considered. These are trial ability, which permits the innovation to be tried before it is accepted; Observability, which takes account of the observable results of the trial; Relative Advantage, the advantage of the innovation compared to others; Complexity, or the difficulty of using and adapting to the innovation; and Compatibility, or how well the innovation will be adapted to the adopter’s existing life style and circumstances. Previous research, for example Parker (2009), has established that the DOI innovation variables of relative advantage, compatibility, complexity, trialability and observability can explain 49-87% of the variance in the rate of adoption of e-commerce applications.

Individual Innovativeness theory: According to this theory, there are five categories of potential technology adopters. In the first category are the Innovators, those individuals who tend to adopt the technology earliest. They are described as experimentalists with a general interest in new developments (Gong, 2009). Next come the Early Adopters, technically knowledgeable individuals who are interested in technology for resolving professional and academic problems. These pragmatic individuals comprise the first mainstream of adopters. The Late Majority are less comfortable with the innovation and form the pessimistic second half of the mainstream. Finally, the laggards are the individuals who may never adopt the technology. The Individual Innovativeness theory is displayed in Figure 4.2, which shows the bell-shaped distribution of Individual Innovativeness and the percentage of potential adopters who fall into each category.

Figure 4.2 Individual Innovativeness Theory

Extracted from Rogers, E.M., (1995). “Diffusion of Innovations” The Free Press, New York. PP. 262


Information systems implementation is expensive and has a relatively low success rate, Hossain (2009) observe that lack of technology user acceptance can lead to severe financial loss. Companies need to invest in new innovations for many reasons such as cutting cost, producing more without increasing cost, decreasing infrastructure cost and simply improving service and products in order to stay in business. During the past three decades, extensive research into information systems has yielded a better understanding of the technology adoption process and its outcomes. Therefore it is important for the major information technology providers to identify clearly which factors will contribute to the user adoption and success of the new innovation. Davis (1986) stress that the user’s attitude has a significant impact on the acceptance and success of new technology. Therefore it is necessary to identify the factors that contribute to Social Media acceptance.

4.3.           Theoretical Framework

There have been a number of studies, utilising different theoretical models, which seek to understand and explain the factors contributing to improved user acceptance of information technology. Cheung et al. (2005) carried out an extensive review of research done between year 1994 to 2002 on technology adoption and adopter profiles. It was concluded that Theory of Perceived Behaviour (TPB) e.g. Ajzen, 1991; Mathieson, (1991), Decomposed Theory of Perceived Behaviour (DTPB) e.g. Taylor and Todd, (1995), Theory of Reasoned Action (TRA), Diffusion of Innovation (DOI) and Technology Acceptance Model (TAM) e.g. Davis, 1989; Davis et al., (1989) are the most dominant models for determining acceptance of information technology and services. These models are all valid for discovering the individual’s acceptance of new information systems (Scherer et al 2019; Ritter, 2017). Many researchers have examined the relationships among perceived ease of use, perceived usefulness, attitude and usage of information systems (Zhao et al 2016, Chintalapati et al 2017).

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4.3.1.    Technology Acceptance Model (TAM)

TAM was originally proposed by Davis et al. (1989). Since then, TAM has gained popularity among many scholars and researchers. The TAM model is applicable to a wide range of technologies. TAM is believed to be robust, parsimonious and influential in explaining IT/IS adoption behaviour, and so is one of the major models used in determining the factors having an impact on user acceptance and use of information technology systems (Kim et al., 2009). Davis (1998) developed the TAM to explain the effect of user perception of system characteristics on acceptance of the information technology systems. The original TAM explained the causal relationships between internal psychological variables such as beliefs, attitudes and behavioural intention and actual system use. Davis’s (1989) original TAM contained only two factors determining the use of an IT system: perceived ease of use and perceived usefulness (Suh, 2002; Davis, 1998; Grandon and Pearson, 2004). The TAM showed that an individual’s perception of the usefulness and ease of use of IT are positively associated with the attitude towards employing the IT. This eventually leads to acceptance and use of the technology (Kim et al., 2009), as shown in Figure 4.3.

 Figure 4.5 Technology Acceptance Model (TAM)

Extracted from Davis et al. (1989) User acceptance of computer technology: a comparison of two. Manage Science Vol. 35 No.8 PP. 982–1002.


Davis (1989) defined perceived usefulness as the degree to which a person believes that using a particular system would enhance his or her job performance and perceived ease of use as the degree to which a person believes that using a particular system would be free of effort.
In the TAM, perceived usefulness is considered the primary factor compared to perceived ease of use in determining information system acceptance and usage (Shih, 2004), whereas Davis’s findings showed that perceived ease of use is positively associated with perceived usefulness and has an indirect impact on the usage and acceptance of information systems (Davis, 1989 and Shih, 2004). Davis and Bagozzi (1989) also found that perceived usefulness has a positive although indirect effect on attitude. Davis (1989) considered perceived usefulness and perceived ease of use the most important underpinning constructs of TAM.  However, TAM fails to consider complex, significant and interconnected relationship factors, especially social factors, which may prevent technology acceptance.

4.4.           Theoretical Models applied to Social Media

Davis (1989) and Taylor and Todd (1995) argued that user acceptance is
important in ensuring that the return on the investment is higher than the
productivity cost. It is therefore important to determine user acceptance of Social Media. Various factors have been identified which contribute to users’ adoption of applications which will be considered for this study which are further discussed in the next chapter Research Model.

Chapter 5 Research Model

  1.  

5.1.           Introduction

The previous chapters explored the literature on Social Media, Data Mining Techniques, Innovation Theory and Technology Acceptance model. The literature review gave an extensive introduction to Social Media and discussed various theoretical models and techniques. This has permitted the development of an underlying theoretical framework for this project and provided assistance in evaluating the acceptance of Social Media.

Acceptance of Social Media has various drivers and inhibitors. This chapter will underline the
research model adopted to evaluate the acceptance of Social media and examine the factors that influence Social Media adoption.  The acceptance of technology is influenced by factors such as demographic characteristics, attitude, social influences and many more. The following factors will be examined and testable hypotheses will be proposed.

5.2.           Demographic and Social Economic Factors

Various studies have been carried out to identify the impact of demographic factors and social economic factors on the adoption of the social media technology. Briones et al. (2010) suggests social media has become the mainstream to build relationships, furthermore they suggest how American red cross delivering updates regarding any occurrence of natural disasters via social media provides a faster service for the community, this provides positive and negative feedback to improve the organizations.

Zarafat et al. (2013), Agrawal et al. (2012), Hsu and Lin (2007), Lee and Paris (2013) suggests age and education are significant factors for acceptance of advancing of technologies.  The following demographic and social factors will be considered, Gender, Age, Martial Status, Education and Income.  

  1.  

    1.  
    2.  

5.2.1.    Gender

In adoption of social media gender plays an important role.  According to Nelison et al. 2019 states gender has a significant Impact on an individual’s attitude towards accepting the use of Social Media.  According to Alnjadat et al.  2019 more females where addicated to Social Media compared to male. However compared to Gefen and Straub (1997) states males may be more inclined towards concern for the perceived usefulness of the innovation.  According to Goswami and Dutta (2016) females have higher computer anxiety and lower self – efficacy than males.  The following hypothesis has been proposed.

      H1(a): Gender is signficantly associated with the intention to use information technology.

5.2.2.    Age

Age is factor that has influence on the adoption on the use of social media sites.  The older the generation less willingly they adapt the use of technology, as older the person they have less experience and more hesitation towards the use of mobiles and computers.  According to Abbasi (2019) and Correa et al. (2010) suggest that younger generation are more inclined to use Social Media. Chaffin and Harlow (2005) and Udawatta et al. 2019) suggest the older a person the more reluctant they become with the use of technology, such as computers because of anxiety and lack of confidence in using new technology.  The following hypothesis has been proposed.

      H1 (b) Increasing Age isnegatively associated with intention to use social media sites.

5.2.3.    Education

Education is important factor that contributes towards the adoption of the use of interactive technology. Roger (1995) states people who have adopted the use of technology at an early stage have higher education levels, he further states they have better ability to understand the complex technology and they know how to function and have an ease of use.  Further research indicates individuals the less knowledge avoid learning and adapting to new information technologies, such as internet and apps.

According to Roblyer et al. (2010) and DeAndrea et al (2012) they suggest there is a significant association between education and use of Social Media.  This allows to assume that the perceived ease of use of social sites will be lower for less educated as compared to better educated people.  The following hypothesis has been proposed.

      H1 (c)  Education attainment is positively associated with intention to use Social media sites on interactive technology.

5.2.4.    Marriage status

With the growing trends of social media, married couples tend to use social media sites more, there are many reasons behind this.  Social media not only allows the individual to know the latest trends and world information, but it is also a form of communication, easier method to exchange photographs, post updates of individuals lives. This is a better and one of the latest approaches to stay in contact with families.  Zhang and Maruping (2008) and Wang and Driskell (2009) states marriage status is a significant factor which influences technology adoption.  The following hypothesis has been proposed.

      H1 (d) Marriage Status will be positively associated with the intention to use social media sites on interactive technology.

5.2.5.    Income

Income has an important impact on the attitude towards adopting the use of interactive technology.  Rogers (1995) observed that innovation enters through homophilous groups, especially influencing higher socio-economic status groups first. It is very unlikely that these groups will influence lower social groups regarding the usefulness of interactive technologies, since communication between high and low socio-economic groups is infrequent.  This suggests that the perceived usefulness of social media apps on interactive technology is lower for individuals with a lower income.  Driskell and Wang (2009) carried out a research to establish the relationship between the adoption of WIFI access and social demographic factors.  Income was one of the factors which influences the adoption of WIFI.  The following hypothesis has been proposed.

      H1 (e) Income will be positively associated with the intention to use social media

5.3.           Attitudinal Beliefs

5.3.  

5.3.1.    Attitude

Rogers (1995) argued that the attitude towards an innovation is a significant variable in determining the adoption decision. Attitude has many dimensions. An individual’s attitude towards interactive technology usage is positively associated with that person’s use of it. Lederer et al. (2000) and Venkatesh and Davis (2000) stated that an individual’s perceived ease of use is positively associated with actual ease of use. Terzi et al. (2019) and  pointed out that individuals’ attitudes towards using Social Media have a positive impact on their intentions to use it. Therefore the following hypothesis can be proposed:

      H2(a) : Individuals’ Attitude towards interactive technology is positively associated with their use of the technology

1.    References

  1. AHMED, S., et al. (2016), The 2014 Indian elections on Twitter: A   comparison of campaign strategies of political parties, Telematics and Informatics, Vol.33(4), pp. 1071–1087.
  2. AL-SHARQI, L., et al. (2015), Perceptions of Social Media Impact on student’s social behaviour: A Comparison between Arts and Science Students, International Journal of Education and Social Science, Vol. 2(4), pp. 122-131.
  3. BAKER, M. (2014), The Impact of Social Networking Sites on Politics, The Review: A Journal of Undergraduate Student Research, Volume 10.
  4. BICEN, H. and CAVUS, N. (2010), The Most Preferred Social Network Sites by Students, Procedia Social and Behavioural Sciences, Vol-2, pp. 5864-5869.
  5. BLANK, G., and LUTZ, C. (2017), Representativeness of Social Media in Great Britain: Investigating Facebook, LinkedIn, Twitter, Pinterest, Google+, and Instagram, American Behavioral Scientist, Vol. 61(7), pp. 741-756.
  6. BOASE, J (2008), Personal networks and the personal communication system, Information, Communication and Society, Vol. 11, pp. 490-508. https://dx.doi.org/10.1080/13691180801999001
  7. CALDERARO, A. (2017), Social Media and Politics, Cardiff University.
  8. CHAFFEY, D. (2019), Global social media research summary 2019. https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/
  9. CHEN, C. et al. (2016), Using social media to enrich information systems field trip experience: student satisfaction and continuance intentions, Computers in Human Behavior, Vol. 63, pp. 256-263.
  10. CHUKWUERE, J., & ONYEBUKWA, C. (2017), New media and politics: An assessment of 2016 South African local government elections. In Proceedings of International Academic Conferences (No. 4607534), International Institute of Social and Economic Sciences.
  11. DONNA, R. and FRASER, R. (2004), Insights into the Social and Psychological Effects of SMS Text Messaging, pp. 1-11.
  12. EDGE, W. (2017). Nursing Professionalism: Impact of Social Media Use among Nursing Students, Journal of Healthcare Communications, Vol. 2(3), pp. 1-3.
  13. EDOSOMWAN, et al. (2011), “ The History of Social Media and its Impact on Business” https://www.researchgate.net/profile/Simeon_Edosomwan/publication/303216233_The_history_of_social_media_and_its_impact_on_business/links/57fe90ef08ae56fae5f23f1d/The-history-of-social-media-and-its-impact-on-business.pdf
  14. FERDIO, “Cluster Analysis” https://datavizproject.com/data-type/cluster-analysis/
  15. FINN, K. (2010), Social media use by Older Adults, International Journal of Electronics and Telecommunications.
  16. FLOSS, D. (2013), Mass Media’s Impact on Confidence in Political Institutions, Swiss National Science Foundation, Paper No 26.
  17. GEMMILL, E., and PETERSON, M. (2006), Technology Use Among College Students: Implications for Student Affairs Professionals. NASPA Journal, Vol. 43(2), pp. 280−300.
  18. GROSS, E. (2004), Adolescent internet use: what we expect, what teen report, Applied developmental psychology. Vol. 25, pp. 633-649.
  19. HO, R. (2006), Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS
  20. JHA, R. et al. (2016), Facebook use and its effect on the life of health science students in a private medical of Nepal. Vol. 9.
  21. KABIR, A. et al. (2018), https://www.researchgate.net/figure/3-stages-of-social-media-mining-or-analytics_fig1_324201476
  22. KAPLAN, A. & HAENLEIN, M. (2010), Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53 (1): 59–68. 
  23. KEENAN, A. & SHIRI, A. (2009), Sociability and Social Interaction on Social Networking Websites, Library, Vol. 56, pp. 438-450.
  24. KHAN, S. (2012), Impact of social networking websites on students, Abasyn Journal of Social Sciences, Vol. 5(2), pp. 56-77.
  25. KHURANA, N. (2015), The Impact of Social Networking Sites on the Youth, Journal of Mass Communication Journalism, Vol. 5(285).
  26. KUSHIN, M. & YAMAMOTO, M. (2010), Did social media really matter? College students’ use of online media and political decision making in the 2008 election, Mass Communication and Society Vol. 13.5, pp. 608-630.
  27. LAKKYSETTY, N.  et al. (2018), Social media and its impacts on politics, International Journal of Advance Research, Ideas and Innovations in Technology, Volume 4, Issue 2.
  28. LEWIS, P. et al. (2018) “Cambridge Analytica kept Facebook data models through US election”, https://www.theguardian.com/uk-news/2018/may/06/cambridge-analytica-kept-facebook-data-models-through-us-election
  29. LIN, S., & CHOU, W. (2013), Developing a social media system for the Taiwanese elderly by participatory design, Vol. 60, pp.39–48.
  30. LOWISZ, S. (2014), The Influence of Social Media on Today’s Culture, http://foclonline.org/sites/staging.foclonline.org/files/outline_345.pdf
  31. MAKINDE, O. et al. (2016), Gender Based Violence following Social Media Acquaintance in Nigeria, African Journal of Reproductive Health, Vol. 20 (4), pp. 67.
  32. MEREDITH, S. (2018), “Facebook-Cambridge Analytica: A timeline of the data hijacking scandal”, https://www.cnbc.com/2018/04/10/facebook-cambridge-analytica-a-timeline-of-the-data-hijacking-scandal.html
  33. MATSA, K. et al. (2018), “News Use Across Social Media Platforms 2018”, https://www.journalism.org/2018/09/10/news-use-across-social-media-platforms-2018/
  34. MOSS, G. et al.(2013), Internet marketing to 50+ generations in the UK and France, Vol. 25(1), pp. 45–58.
  35. ELLISON, N. et al. (2007), The Benefits of Facebook ‘Friends’: Social Capital and College Students’ Use of Online Social Network Sites, Journal of Computer-Mediated Communication, Vol. 12/4, pp. 1143–1168.
  36. NORTH, S. (2008), Digital Tastes: Social class and young people’s technology use, Information, Communication & Society, Vol. 11(7), pp. 895-911.
  37. RAJEEV, M. (2015), Effects of Social Media on Social Relationships: A Descriptive Study on the Impact of Mobile Phones among Youth Population, Vol. 4(2), pp.11-16.
  38. Risk matrix. https://www.changefactory.com.au/service/creating-strategy/risk-management/risk-matrix/
  39. RODRIGUEZ, D. (2016) “Using scrum in your research”, https://blog.xrds.acm.org/2016/10/scrum-at-research/
  40. SATTERFIELD, H. (2016), How Social Media Affects Politics, The Press.
  41. SEIF, G. (2018), “The 5 Clustering Algorithms Data Scientists Need to Know”, https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68
  42. SHABIR, G. et al. (2014), The Impact of Social Media on Youth: A Case Study of Bahawalpur City, Asian Journal of Social Sciences and Humanities, Vol. 3(4), pp. 132-151
  43. STIEGLITZ, S. (2014), Social Media and Political Communication – A Social Media Analytics Framework, Med. Internet Res.
  44. TENNANT, B. et al. (2015), eHealth literacy and web 2.0 health information seeking behaviors among baby boomers and older adults. J. Med. Internet Res.  Vol. 17.
  45. WILLIAMS, L. M. (2012). “Facebook ruined my marriage: Digital intimacy interference on social networking sites” (Doctoral dissertation, Syracuse University).
  46. WON, K., & SANG-WON, L. (2009), On Social Websites, Information Systems.
  47. YAVUZ, G. (2012), The Impact of Media Exposure on Vote Choice, ELECDEM Final Conference, Florence, Italy.
  48. ZHANG, W. et al. (2010), The revolution will be networked: The influence of social networking sites on political attitudes and behavior, Social Science Computer Review, Vol. 28.1, pp.  7

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