Aragats Amirkhanyan
email: aragats.amirkhanyan@hpi.de

Christoph Meinel
email: christoph.meinel@hpi.de

Analysis of the Value of Public Geotagged Data from Twitter from the Perspective of Providing Situational Awareness

Keywords: analysis, statistics, big data, location-based social networks, geotagged data, georeferenced data, situational awareness, public safety awareness

In the era of social networks, we have a huge amount of social geotagged data that reflect the real world. These data can be used to provide or to enhance situational and public safety awareness. It can be reached by the way of analysis and visualization of geotagged data that can help to better understand the situation around and to detect local geo-spatial threats. One of the challenges in the way of reaching this goal is providing valuable statistics and advanced methods for filtering data. Therefore, in the scope of this paper, we collect sufficient amount of public social geotagged data from Twitter, build different valuable statistics and analyze them. Also, we try to find valuable parameters and propose the useful filters based on these parameters that can filter data from invaluable data and, by this way, support analysis of geotagged data from the perspective of providing situational awareness.

Introduction

Nowadays, social networks are an essential part of modern life for millions of users around the word. Users use social networks to communicate with friends and share what happens with them, what they feel and so on. One of the biggest social networks is Twitter that, according to the recent statistics [START_REF]The number of monthly active Twitter users worldwide[END_REF], has more than 300 million active monthly users, who post every day more than 600 million tweets (messages) [START_REF]Twitter statistics[END_REF]. Twitter is not only the social network, but it is the source of real-time news feeds and it is the place where breaking news appear firstly, before we read it in the newspapers, listen at the radio or watch on TV [START_REF] Kwak | What is twitter, a social network or a news media?[END_REF]. The amount of data produced by social networks increases dramatically every year. And, also, there is a trend, that users post geotagged messages that reflect the situation around them. For research, it is important, because it gives us more possibilities for visualization and analysis of social data, since we can be interested not only in the content of messages but also in the location, from where these messages were posted.

Analysis of public social geotagged data is a big topic and there are many papers and enterprise solutions, which cover different aspects and challenges of social data analysis. One of the most popular cases is to use geotagged data for analyzing and detecting natural disasters. For example, Sakaki et al. [START_REF] Sakaki | Earthquake shakes twitter users: Real-time event detection by social sensors[END_REF] used Twitter users as social sensors to detect earthquake shakes. They investigated the real-time interaction of events, such as earthquakes, in Twitter and proposed an algorithm to monitor tweets and to detect an earthquake target event. Another use case of using public social data is to analyze natural disasters was presented by De Longueville et al. [START_REF] De Longueville | A use case of mining location based social networks to acquire spatiotemporal data on forest fires[END_REF]. In their paper, they showed how location-based social networks (LBSN) can be used as a reliable source of spatiotemporal information, by analyzing the temporal, spatial and social dynamics of Twitter activity during a major forest fire event in the South of France in July 2009. Later in 2013, the amount of public social geotagged data increased and researchers had a possibility to detect not only global but, also, local geo-spatial events [START_REF] Walther | Geo-spatial event detection in the twitter stream[END_REF].

In 2013, Kalev Leetaru et al. [START_REF] Leetaru | Mapping the global twitter heartbeat: The geography of twitter[END_REF] presented their deep study of geography of Twitter. They analyzed data from Twitter posted around the world and built many statistics, such as total tweets per day, average tweets per hour, all exact location coordinates and the top 20 cities by percent of georeferenced tweets. Also, their study includes linguistic, textual and user profiles analysis of data. In 2014, Muhammad Adnan et al. [START_REF] Adnan | Social dynamics of twitter usage in london, paris, and new york city[END_REF] presented their results of analysis geotagged data from Twitter. In their work, their concentrated on social dynamics of Twitter usage based on data from London, Paris and New York City. They showed the areas of tweeting activity, they provided the results of ethnicity analysis of Twitter users, geography of tweets of different ethnic groups and gender analysis. Another analysis of geotagged data, Diansheng Guo et al. [START_REF] Guo | Detecting non-personal and spam users on geo-tagged twitter network[END_REF] presented in 2014. In their work, they were aimed to detect non-personal and spam users on geotagged Twitter network. Their approach contains extracting user characteristics, constructing training datasets, conducting supervised classification for detecting non-personal users and the evaluation of the approach. In 2015, Umashanthi Pavalanathan et al. [START_REF] Pavalanathan | Confounds and consequences in geotagged twitter data[END_REF] presented interesting results. They showed that young people and women more often write geotagged tweets; users, who geotag their tweets, tend to write more, making them easier to geolocate; and text-based geo location is significantly more accurate for men and for older people.

Based on papers of previous years including mentioned in this section, we see how geotagged data are valuable and what we can obtain from analysis of them. Therefore, in this paper, we provide our research results of analysis of geotagged data in the scope of our specific challenge, which is analysis of the value of geotagged data from the perspective of providing situational awareness. For that, we start with Section 2, in which we provide the description of our research project, in the scope of which we address the current challenge, and our motivation for that. The remainder of the paper is organized as follows: in Section 3, we describe how we collect data and what data we have for analysis. Afterwards, in Section 4, we provide statistics and our analytic results of collected data. Based on that, we try to propose methods for making data more valuable for analysis of situational awareness and public safety awareness. We conclude the paper and provide future directions of research in Section 5.

Project Scope and Motivation

In the scope of our main research project, we are aimed to analyze and visualize in real-time publicly available social geotagged data to provide situational and public safety awareness. This challenge requires a complex solution, therefore, during work on the project, we face with many additional challenges that come from the practice [START_REF] Amirkhanyan | Real-time clustering of massive geodata for online maps to improve visual analysis[END_REF] [9], such as real-time clustering and visualization of massive geotagged data, provision of advanced methods for searching and filtering, provision of real-time valuable statistics and so on. Nowadays, there are a lot of social analytics tools, such as TweetDeck, Twitonomy, Hootsuite, Tweepsmap, Geofeedia and so on. Many of them provide powerful functionality for analysis and visualization of data for different purposes and solve some challenges mentioned above. But mostly, they are aimed to support marketing and business, therefore, their solutions of filtering and providing real-time statistics are not always can be applied to our research project that has other focus.

We have our specific goal, which is provision of situational and public safety awareness. To achieve this goal, we want to work with social geotagged data that describe the situation around the place, from which they were posted. But it is not always the case, and we have to work with a huge amount of data that are useless for our analysis and they do not reflect valuable information and do not describe the situation around. Therefore, we face with the challenge that presented in this paper. This challenge is analysis of the value of public geotagged data from Twitter from the perspective of providing situational awareness. In the scope of this challenge, we want to understand what and which parameters can help us to recognize whether some concrete message does have any value in describing the situation around or not, based on which characteristics we can fully exclude invaluable data from our dataset and which statistics of data can help us to better understand the situation around. All together should support our research project. Therefore, we are aimed to collect social geotagged data, build statistics and analyze them. Results of these statistics can help us to exclude invaluable data and develop advanced filters to support analysis of data from the perspective of providing situational awareness.

Data

Data is an essential part of any research. For our research, we use public social geotagged data from Twitter. Twitter provides a quite powerful API to fetch required data [START_REF]Bright planet[END_REF] [5]. One of them is commercial Twitter's Firehose, which guarantees access to 100% of tweets, and other one is public Streaming API, which does not guarantee the percentage of receiving data in real-time. More comparisons between data from Twitter's Streaming API and Twitter's Firehose, you can find in the paper of Fred Morstatter et al. [START_REF] Morstatter | Is the sample good enough? comparing data from twitter's streaming API with twitter's firehose[END_REF]. But we want to mention that, according to some studies [START_REF]Bright planet[END_REF], using Twitter's Streaming API users can expect to receive anywhere from 1% of the tweets to over 40% of tweets in near realtime. Also, you can increase the percentage of receiving tweets by applying more strict criteria. The criteria can be keywords, usernames, locations, named places, etc. In our case, we use location as a criteria and we collect data from London, because London is one of the world's most active Twitter cities [4]. To collect data, we use the Java-based application that connects to Twitter's Streaming API, specifies needed criteria and starts to receive tweets in real-time. For our experiments we decided to collect data from the area that covers London and its neighborhood. The coordinates of monitored area are {51.247948, -0.569042; 51.727184, 0.303813}, where the first pair is latitude and longitude coordinates of the south west and the second pair is latitude and longitude coordinates of the north east. We parse all received data, normalize them and save them into the database for further analysis. For research and further analysis, we collected 1 million tweets that cover about 12 days: from the 28th of January 11:37:30 AM until the 8th of February 12:41:48 PM.

Analysis

This section is the main part of the paper. In this section, we provide different valuable statistics and full description and analysis of them. Also, we try to find out how our results could be used to support analysis of situational and public safety awareness. It means that based on our statistic and statistics results, we try to propose (1) methods for reducing the amount of data by excluding (removing) invaluable data and (2) filters that can support analysis of geotagged data. Both proposals (excluding data and filters) are aimed to support analysis of situational awareness based on social geotagged data. We start with the statistics in Table 1. In this table, you can see the most popular languages of tweets posted from London. Information about languages we take from the tweet object, which is provided by Twitter [5]. Firstly, we can see that the official language is in the first place. About 83.80% of all tweets are posted in English. Then we can see that for 6.76% of tweets, Twitter was not able to identify the language. In most cases, it means that tweets without the language do not contain sentences or even words. They are just a set of hashtags, user mentions, symbols and URLs. So, such tweets do not have semantic sense. In the next places, we have Spanish (1.66%), Arabic (1.37%), Portuguese (1.16%), French (0.92%) and so on. Now, we need to consider how we can use obtained information to support analysis of data. Firstly, we definitely can exclude all tweets with the undefined language, because they bring no sense and we can not use them to analyze situational awareness. By this way, we can reduce the amount of data by 6.76%. Other thing that we can do, we can use the knowledge about used languages to filter dataset by languages or filter dataset from tweets in other foreign languages. It can support analysis of data, because we would fetch only relevant data. Usually tweets contain the place information, because users are asked to attached a geographic place to the tweet before to publish it. Additional, Twitter asks users to attach exact coordinates. So, the final tweet could have the exact geo coordinates and attached place. Places could be city, administrative area (admin), country or some concrete place of interest (poi ), for example, Big Ben in London. In our dataset, almost all tweets have attached places (only 5 tweets do not have a place), but only 11.53% of data have exact coordinates. In Table 2, you can see which place types are usually attached to tweets. In the first place we have the city with about 79.08%, then the admin (administrative area) with about 18.50% and in the last place we have the country place type with about 2.42%. In our research, we are interested only in the tweets that have the city place type or more narrow as a place of interest (poi ). Place types, such as country and administrative area (admin), are too big areas and we can not use such data for analyzing situational awareness. It means that we can easily exclude such data from our dataset, but with one assumption. We exclude tweets with irrelevant place types only if these tweets do not have exact geo coordinates. Because if the tweet contains exact geo coordinates, we should not consider to which place the tweet is attached. So, if we exclude tweets with country and admin place types, we can reduce dataset by maximum 20.92%. Not all content published in Twitter is original. Some tweets come from other social networks or have links to the external web sites or services, such as Facebook, Instagram, Swarm (Foursquare) and son on. If the tweet contains several links, we consider only the last link, because usually the last link refers to the original source of information. According to Table 3, the main external source of tweets is twitter.com (8.46%). It means that users post tweets that contain links to other tweets. Then we have instagram.com (6.54%), bit.ly (1.51%), swarmapp.com (0.98%) and so on. We can suppose that if user posts a tweet about what happens around him, he does not include links to external websites into the tweet, otherwise, user likely does not really describe the situation around him. But this statement does not work if the tweet has link to another social network, because, in this case, it could be that user posted about the situation around in Instagram but then he reposted the message to Twitter. Therefore, it is not always obviously, which data we can exclude from dataset. It requires more detailed analysis. But some of them we can definitely exclude. For example, we can exclude tweets that refer to Foursquare, Swarm or Yelp, to social networks that have nothing with describing the situation around. Usually, tweets, which refer to Swarm, have information, such as "Hi, I am in London" or something similar to it. So, if we exclude tweets at least only from Swarm, we can reduce dataset by about 0.98%. We have 1 million tweets but only 11.53% of tweets have exact geo coordinates, others have geo coordinates based on an attached geographic place (the center of the place). It means that if tweets have the same attached places then they have the same coordinates. Based on our dataset, we calculated that from 1 million possible geo coordinates, we have only 37650 unique geo coordinates, which is about 3.76% of all possible coordinates. This percentage is less than the percentage of tweets with exact coordinates. So, we can conclude that tweets with exact geo coordinates can have the same coordinates. Usual case for that is when tweets are reposted from other social networks. Additionally, we analyzed which geo coordinates are the most demanding. In Table 4, you can find the calculated statistics. The coordinates are presented in geohash 1 form to simplify the represent. They can be easily converted back to latitude and longitude by the geohash function. According to our statistics, about 5.79% of tweets have coordinates gcpe6rh4k4j9 (51.23513843, -0.59857568), which is in the area of the city Guildford. The next, about 5.75% of tweets have coordinates gcpvjc9kxvpg (51.51294588, -0.09681718), which is the center of London. By this statistics, we can see which places are the most attractive by Twitter users. Above we mentioned that about 79.08% of tweets contain attached places of the city type. But these places are different. If user posts a tweet in London and he attaches the place as London then his tweet would have coordinates of the center of London. But if he attaches some concrete district of the city then the tweet would have more concrete location. For example, such district of London can be Barnet, Hackney, Lambeth and so on. Actually, Twitter suggests the closest and the most appropriate place when user wants to attach the place to the tweet, and it helps more correctly determine the location for the tweet even if user did not attach the exact geo coordinates. Now, the question is what we can do with this statistics to support analysis of situational awareness. We can use this statistics to design a filter. This filter would use statistics from Table 4 and provide filtering data from tweets, which were posted from the most demanding geo coordinates. In some cases, it can facilitate visual analysis of situation.

In Section 3, we mentioned that we subscribe for data from London and its neighborhood defined by the following coordinates {51.247948, -0.569042; 51.727184, 0.303813}. But Twitter usually returns not only data from the specified area but also from areas that overlap that area. For example, we receive, also, tweets with coordinates of the entire country UK. Therefore, additionally, we calculated the percentage of tweets that have coordinates inside of the specified area. The result is 82.51%, which is close to the percentage of tweets containing a city place type. If we exclude tweets outside the monitored area, we can reduce the amount of data by 17.49%. But this percentage will be less, if we, firstly, exclude tweets with inappropriate place types admin and country.

In Introduction, we mentioned that some tweets contain location information, such as hashtags or words of locations, and they can be geolocated. Therefore, we want to find out how many tweets in our dataset contain additional location information. In some cases, it can help to identify the location more precisely than it is specified. For example, we could have the tweet "The house is on fire in Carnaby Street" and this tweet could have an attached place as the entire London, which has coordinates of the city center. But in this case, this message would be more valuable if it would have more concrete location. We can see this concrete location in the text message "in Carnaby Street". Therefore, we want to find out how many tweets in our dataset contain such additional location information. To find out it, we used Stanford Named Entity Recognizer (NER) [START_REF]Stanford Named Entity Recognizer (NER[END_REF]. And after applying this library for our dataset, we obtained that about 9.51% of tweets contain additional location information in their text content. It means that potentially 9.51% of tweets could be more accurate geolocated than it is specified. The next thing, in which we are interested in, is how many unique users we have. We calculated that in our dataset we have 107105 unique users, and it means that we have about 9.37 posts per user. Not all users are equally active. Therefore, we want to find out which users produce more tweets than others. In Table 5, you can find partial statistic results. In the first place, we have user tegrenade, who produced about 1.40% of all tweets from our dataset. It is about 14004 tweets during 12 days, which means 1167 tweets per day. The Twitter accounts in the next palaces produced also huge amount of data, you can see it in Table 5. Some of these active users we can consider as a non-personal or spam users. It is an interesting challenge to identify it and Diansheng Guo et al. [START_REF] Guo | Detecting non-personal and spam users on geo-tagged twitter network[END_REF] presented their approach of detecting non-personal and spam users. But in the scope of our paper, we would like to use found statistics to design a filter. And this filter would be aimed to filter data from the most active users by using statistics from Table 5. In some case, it can facilitate analysis of data. Now, we consider hashtags, user mentions, symbols, URLs and media objects in tweets, and how they are mentioned by users. We start with symbols. The symbol is the character started with $ dollar sign and it is often used in the financial area, for example, to put price information or to include the name of the company in the stock market. Examples of symbols are AMZN, GOOG, FB, GBPUSD, where AMZN -Amazon, GOOG -Google, FB -Facebook, and GBPUSD -British Pound to Dollar. So, we can suppose that tweets with symbols have nothing with describing the situation around, therefore, we can exclude them. But they constitute just about 0.01%, so the benefit is small. Table 6: The percentage of tweets that contain URLs in their text content has URL 1 URL 2 URLs 3 URLs 4 or more URLs 25.70% 23.96% 1.71% 0.03% 0.003% Many tweets contain URLs. According to our statistics, about 25.70% of tweets contain URL. Mostly, tweets contain only one URL (23.96%), but some of them have more links. Details statistics you can find in Table 6. Such URLs could be links to other social networks or to some external websites with some news. We can suppose that if user posts the tweet about what he sees now, likely, he will not include a link to the external resources, but with the exception when user reposts the tweet from other social networks. Therefore, we can assume only one or maximum two links. Others tweets we can exclude from dataset. Some tweets contain media objects: image or video. According to our statistics, 14.24% of tweets contain media object, and 14.238% of them contain only one media object and 0.002% of them contain 2 media objects. Media-based tweets are produced by 35.81% of users. So, it is common to include one media object to the tweet, but existence of media objects in tweets does not tell us about the relevance of the tweets from the perspective of describing the situation around. Therefore, we should not consider this parameter for filtering data. Users often mention other users in their tweets to start or to keep discussion. For that, they use @ character and the account name of the user. From Table 7, we can see that about 51.68% of tweets contain user mentions. We could suppose that the number of user mentions can affect how the tweet describes the situation around. More user mentions in the tweet then more likely that this tweet is just a part of the discussion, but not describing the situation around. Therefore, we are interested in a filter that can filter data by number of user mentions. For example, we could want to filter data from tweets that contain 3 or more user mentions. It can reduce dataset by 5.74% for further analysis. In Table 8, you can see the statistics of hashtags in tweets. According to our results, about 22.71% of tweets contain hashtags. About 12.04% of them contain only one hashtag, about 5.04% of them contain two hashtags, about 2.23% of them contain 3 hashtags and about 3.40% of them contain 4 or more hashtags. We can suppose that when user wants quickly to post a tweet about the situation around, he would not consider about including a huge amount of hashtags into the tweet. But we can not fully rely on this statement. We should consider another additional parameter. In Table 9, we provide the statistics of the most utilized hashtags during the time periods of collected data. 799 hashtags appear every day (12 days) in our dataset, 580 hashtags appear in 11 days (91.66% of the entire date range), 548 appear in 10 days (83.33%) and so on. Some hashtags, which appear every day, could be useless hahstags from our perspective, for example, the hashtag #happybirthday. Whereas among these hashtags, there are hashtags that represent geographic places, for example, #london. The hashtag #london appears every day, but we assume that this hashtag can be included into the tweet to describe the situation around. Therefore, we need to consider an additional parameter that can help us to understand the value of tweets with popular hashtags. Therefore, we built the statistics of the distribution of the most popular hashtags among users. You can find this statistics in Table 10. From that statistics, we can see that, for example, the hashtag #london is used by 5.07% of users. This hashtag is used by significant amount of users and it appears every day, therefore, we can not exclude tweets with this hashtag. Whereas, the hashtag #happybirthday, which appears also every day, is used only by 76 users (0.071% of users). This fact could give us a guess that such hashtag does not bring any situational information into the tweet. Therefore, we can think about filtering them. We have three parameters related to the hashtags: the number of hashtags in the tweet, the distribution of hashtags during the date period of dataset and the distribution of hashtags among users. We want to filter tweets that do not describe the situation around. For that, we need to use all three parameters. Therefore, our filter should assume that an invalid tweet should have more than n hashtags, at least m of these hashtags should appear every day during the monitored period d (in our case it is 12 days, but it can be customized), and less than p percents of users should use these m hashtags. We need to point out that we do not consider which values of parameters n, m, p, d to choose to obtain the best filtering results, but we have only showed the parameters that should be considered. Finding the concrete optimal values is a part of future work.

Conclusion and Future Work

We devoted this paper for analysis of the value of social geotagged data from the perspective of providing situational awareness. We were motivated to do it because we have to work with a huge amount of invaluable data and we wanted to reduce this amount of invaluable data, design filters and build statistics that can support analysis. During work on analysis of data, we built statistics by different parameters and different compound parameters, and we presented the most valuable found results from our perspective.

It is always important to point out that we do analysis of social geotagged data from the perspective of providing situational awareness and, all statistics which we built, we analyzed for our concert use case that makes our research different from many existing social analytics tools focused mostly on business and marketing. For example, in the statistics of the most popular languages in Table 1, we tried to find out how this statistics can help us to remove partially invaluable data. And we found out that the indicator of invaluable data, from the perspective of providing situational information, can be the undefined language. We went further and we tried to heuristically find out how number of different entities in the text content can affect the situational value of data. We had an assumption that if the tweet has many URLs, user mentions, hashtags and symbols then such tweet has less the situational value. Based on this assumption, we built statistics by mentioned parameters that showed us what the benefit we can obtain if we exclude much littered tweets. Another example of analysis of geotagged data from perspective of providing situational awareness is the statistics of original sources of tweets. We found out that some sources of tweets can be indicators of invaluable data, but it requires manual analysis and making the list of irrelevant sources, such as Swarm and Foursquare, which do not bring situational information in their content.

To evaluate the benefit from analysis of data and built statistics, we applied some recommendations for our research project. If we remove data with inappropriate place types (country, admin), data outside the specified monitored area, data from irrelevant services: Swarm and Foursquare, data with symbols and data with undefined languages, then we exclude 28.86% of invaluable data. Meanwhile, we can expect the higher percentage of removed invaluable data, if we apply more advanced methods for filtering based on compound parameters from built statistics. Therefore, we would like to continue research and, for that, we determine some future work directions.

As a future work, we would like to build and visualize in real-time presented in the paper statistics. Also, we would like to use statistics from this paper to develop proposed filters. These filters can help to better analyze data and provide more accurate situational awareness. Such filters could be filters of the spam hashtags, inappropriate URLs, inappropriate geo location coordinates, tweets that contain too much user mentions and so on. Also, we need to find the optimal values of the parameters for filters from the perspective of providing the optimal filtering to have mostly data that describe the situation around. With results in this paper and future work mentioned in this section, we plan to go further to achieve the main goal of the research project -real-time situational and public safety awareness based on public social geotagged data.

Table 1 :

 1 Tweets' languages statistics

	English Undefined Spanish Arabic Portuguese French Others
	83.80% 6.76%	1.66% 1.37% 1.16%	0.92% 4.33%

Table 2 :

 2 Tweets' place types statistics

	City	Admin Country
	79.08% 18.50% 2.42%

Table 3 :

 3 Statistics of external sources of tweets twitter.com instagram.com bit.ly swarmapp.com goo.gl trendinalia.com youtube.com

	8.46%	6.54%	1.51% 0.98%	0.47% 0.44%	0.44%

Table 4 :

 4 Statistics of the most demanding geo coordinates

	gcpe6rh4k4j9 gcpvjc9kxvpg gcpuuqtyeztv u120jz6zfbbe gcpusn4djt0k gcpvnqvtp20z
	5.79%	5.75%	4.93%	4.33%	4.15%	3.34%

Table 5 :

 5 The most active Twitter users

	tegrenade hesjkr94 trendinaliagb orgetorix blankiiam015 don jide a rockas biggucci idz
	1.40%	0.61% 0.50%	0.26%	0.23%	0.22% 0.22% 0.20%

Table 7 :

 7 The percentage of tweets with user mentions in their text content

	has mention 1 mention 2 mentions 3 mentions 4 or more mentions
	51.68%	36.35%	9.58%	3.12%	2.63%

Table 8 :

 8 The percentage of tweets with hashtags in their text content

	has hashtag 1 hashtag 2 hashtags 3 hashtags 4 or more hashtags
	22.71%	12.04% 5.04%	2.23%	3.40%

Table 9 :

 9 The count of hashtags and the count of days when these hashtags appear

	12 (100%) 11 (91.66%) 10 (83.33%) 9 (75.00%) 8 (66.66%) 7 (58.33%) 6 (50.00%)
	799	580	548	606	720	988	1286

Table 10 :

 10 Statistics of usage concrete hashtags by the percentage of users

	london love cbb superbowl uk	sb50 art	fridayfeeling valentinesday tbt
	5.07% 0.71% 0.70% 0.60%	0.53% 0.52% 0.50% 0.49%	0.44%	0.43%

https://en.wikipedia.org/wiki/Geohash


Salma S Abed
email: sabid@kau.edu.sa

An Empirical Examination of Factors Affecting Continuance Intention towards Social Networking Sites

Keywords: social networking sites, e-commerce, s-commerce, TAM, consumers, Saudi Arabia

This study examines the factors that affect consumer continuance intention to use social networking sites as a business tool. Using the Technology Acceptance Model (TAM) as the theoretical framework, the researcher tested the research model and related hypotheses using structural equation modelling. The results from a survey of 304 Facebook users in Saudi Arabia indicate that trust, perceived usefulness, and perceived enjoyment had significant positive effects on behavioural intention to continue to use social networking sites. Furthermore, perceived ease of use was found to have a significant positive effect on perceived usefulness, perceived enjoyment, and trust. Discussion of the findings and research contribution as well as limitations and future research directions are presented.

Introduction

Virtual communities and Social Networking Sites (SNS) allow people to connect with each other in general or on a particular subject online [START_REF] Kang | How Does Social Commerce Work for Apparel Shopping? Apparel Social E-shopping with Social Network Storefronts[END_REF], [START_REF] Murray | Social Networking Goes Abroad[END_REF]. Recently, the number of members of online social networks has increased explosively. For example, the social networking site "Facebook" registered 1.06 billion monthly active users, 618 million daily active users, and 680 million mobile active users in December 2012 [START_REF] Tam | Facebook by the Numbers: 1.06 billion monthly active users[END_REF]. Indeed, the popularity of SNS is highly demonstrable through the huge number of people using them, as it has quickly become a new phenomenon that has transformed online communication [START_REF] Kuitunen | Social Media in International Brand Communication of SMEs: A Multiple Case Study of Small Finnish Design-Intensive Companies[END_REF]. Consequently, Saudi Arabia has witnessed the biggest growth of diffusion of social media in the Arab world, which is the strongest empowering factor to e-commerce adoption within the Kingdom of Saudi Arabia (KSA) [START_REF] Orloff | E-commerce in Saudi Arabia: Driving the Evolution Adaptation and Growth of E-commerce in the Retail Industry[END_REF]. According to [START_REF]Arab Social Media Report: Arab Social Media Influences Summit[END_REF], around 87% of social media users covered by the survey in the Arab world are subscribed to Facebook. In Saudi Arabia, the subscription rate is 80% of total users of Facebook, 81% of current Facebook subscribers in KSA access the channel on a daily basis, and 76% of social media users in KSA visit Facebook through their smartphones and/or tablets. As a result, firms are starting to make wider use of SNS to build closer links with suppliers and customers [START_REF] Abed | Consumers' Perceptions of Social Commerce Adoption in Saudi Arabia[END_REF], [START_REF] Jamal | Mining Social Network Data for Personalisation and Privacy Concerns: A Case Study of Facebook's Beacon[END_REF]. Therefore, many firms have been creating new ways to make profitable use of SNS applications [START_REF] Abed | Social Commerce as a Business Tool in Saudi Arabia's SMEs[END_REF]. Accordingly, the development of SNS has improved a new e-commerce model called social commerce (s-commerce). The term 's-commerce' is defined as a concept of Internet-based social media, which enables people to participate actively in the selling and marketing of different products and services in online marketplaces [START_REF] Kim | A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents[END_REF]. This dynamic process helps consumers obtain better information about different products and services provided by companies [START_REF] Hajli | Social Commerce for Innovation[END_REF]. However, very limited empirical research has been conducted in this context [START_REF] Hajli | Social Commerce for Innovation[END_REF], [START_REF] Kim | A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents[END_REF], [START_REF] Ng | Intention to Purchase on Social Commerce Websites Across Cultures: A Cross-Regional Study[END_REF][START_REF] Noh | Effects of Collectivism on Actual S-commerce Use and the Moderating Effect of Price Consciousness[END_REF], [START_REF] Sharma | Disclosing Too Much? Situational Factors Affecting Information Disclosure in Social Commerce Environment[END_REF][START_REF] Sharma | Intention to Engage in Social Commerce: Uses and Gratifications Approach[END_REF][START_REF] Shen | Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites[END_REF], specifically in the Arab world and particularly in Saudi Arabia. Accordingly, this study aims to investigate factors affecting consumer continuance intention to use SNS as a business tool. The following sections of the paper include the relevant literature review and hypothesis development in the second section. These are followed by the methodology, data collection, as well as results and discussion outlined in the third, fourth, and fifth sections respectively. Finally, the research contribution, conclusion and limitations are delivered in sections six, seven, and eight.

Literature Review and Hypothesis Development

As Davis' technology acceptance model integrates findings from the information systems literature, the model's use became widespread in the diffusion of innovation literature. The previous studies have explained the importance of the Technology Acceptance Model (TAM). Many research studies have provided better empirical support for TAM in information technology research regardless of the country concerned [i.e. 16], [START_REF] Hajli | Social Commerce for Innovation[END_REF][START_REF] Irani | Understanding Consumer Adoption of Broadband: An Extension of the Technology Acceptance Model[END_REF], [START_REF] Sun | Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach[END_REF], [START_REF] Venkatesh | Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior[END_REF]. Accordingly, TAM has been utilized in the Saudi Arabian context in various studies. [START_REF] Al-Harby | The Feasibility of Biometrics Authentication in E-commerce: User Acceptance[END_REF] used TAM to examine biometric technology to determine user identity in e-commerce. [START_REF] Al-Somali | An Investigation into the Acceptance of Online Banking in Saudi Arabia[END_REF] examined factors that influence the online banking adoption behaviour of 400 customers based on the TAM. [START_REF] Al-Mowalad | The Extension of TAM: The Effects of Social Media and Perceived Risk in Online Purchase[END_REF] presented a conceptual framework that was extended from the advanced TAM to examine social media effects. [START_REF] Brislin | Comparative Research Methodology: Cross-Cultural Studies[END_REF] utilized TAM to examine consumers' online shopping intention and risk perceptions in both Saudi Arabia and the United States. [START_REF] Al-Rowili | Predicting Citizens' Acceptance of M-Government Services in Saudi Arabia: An Empirical Investigation[END_REF] extended the TAM to examine the influence of user acceptance of m-government services. [START_REF] Al-Gethmi | Mobile Commerce Innovation in the Airline Sector: An Investigation of Mobile Services Acceptance in Saudi Arabia[END_REF] investigated factors influencing customers' behavioural intention to adopt mobile services during their travel process using TAM and DOI. Finally, [START_REF]Arab Social Media Report: Arab Social Media Influences Summit[END_REF] investigated the adoption of self-service technologies by consumers in Saudi Arabia using the TAM model. In the s-commerce context, a number of studies have examined TAM in different geographical contexts; this includes the UK [START_REF] Hajli | An Integrated Model for E-commerce Adoption at the Customer Level with the Impact of Social Commerce[END_REF][START_REF] Hajli | Social Commerce for Innovation[END_REF], USA [START_REF] Shen | Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites[END_REF], and South Korea [START_REF] Noh | Effects of Collectivism on Actual S-commerce Use and the Moderating Effect of Price Consciousness[END_REF]. Accordingly, it is worthy to investigate s-commerce adoption utilizing TAM in the Saudi Arabian context.

In fact, TAM was developed for the explanation of the factors affecting computer acceptance in general rather than for a specific topic. As a result, a further modification of TAM is essential to explain consumer Behavioural Intentions (BI) to continue to use SNS. In this section, the researcher highlights the original constructs of TAM, specifically perceived usefulness (PU) and perceived ease of use (PEOU), followed by perceived enjoyment (PE) and trust (TR), which has been examined in s-commerce in other geographical contexts and found to be significant, PE [START_REF] Sharma | Intention to Engage in Social Commerce: Uses and Gratifications Approach[END_REF][START_REF] Shen | Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites[END_REF], TR [START_REF] Hajli | An Integrated Model for E-commerce Adoption at the Customer Level with the Impact of Social Commerce[END_REF][START_REF] Hajli | Social Commerce for Innovation[END_REF], [START_REF] Kim | Effects of Various Characteristics of Social Commerce on Consumers' Trust and Trust Performance[END_REF], [START_REF] Ng | Intention to Purchase on Social Commerce Websites Across Cultures: A Cross-Regional Study[END_REF] and are worth further investigation in the Saudi Arabian context. The proposed hypothesis of this study are presented: Perceived Usefulness (PU): perceived usefulness is defined as the degree to which a user is aware of specific information technology innovation and how it will improve his/her work performance [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[END_REF]. In fact, there is widespread research in the Information Systems (IS) context that provides evidence of the significant effect of PU on BI [START_REF] Venkatesh | Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior[END_REF].

For the present research, the proposed relationship between PU and BI is based on studies that have found perceived usefulness to have a positive effect on behavioural intention to use s-commerce [START_REF] Hajli | Social Commerce for Innovation[END_REF], [START_REF] Ng | Intention to Purchase on Social Commerce Websites Across Cultures: A Cross-Regional Study[END_REF], [START_REF] Sharma | Intention to Engage in Social Commerce: Uses and Gratifications Approach[END_REF][START_REF] Shen | Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites[END_REF]. Accordingly, the following hypothesis has been formulated: H1: Perceived usefulness will have a significant positive effect on behavioural intention to continue to use social networking sites.

Perceived Ease Of Use (PEOU): Perceived Ease Of Use refers to the degree to which the users' expectations about using information technology innovations would be free of effort [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[END_REF]. Over the past decade, much research has provided evidence of the significant effect of PEOU on BI, either directly or indirectly through its effect on PU [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[END_REF], [START_REF] Venkatesh | Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior[END_REF]. Furthermore, other studies have found the PEOU to have a significant effect on Perceived Enjoyment (PE) as well as Trust (TR). In the context of SNS acceptance and adoption, [START_REF] Noh | Effects of Collectivism on Actual S-commerce Use and the Moderating Effect of Price Consciousness[END_REF] found that PEOU has a significant effect on BI. In contrast, [START_REF] Hajli | An Integrated Model for E-commerce Adoption at the Customer Level with the Impact of Social Commerce[END_REF] found the effect of PEOU on IB to be non-significant. Furthermore, few studies have found that PEOU has a significant positive effect on PU [START_REF] Hajli | An Integrated Model for E-commerce Adoption at the Customer Level with the Impact of Social Commerce[END_REF], [START_REF] Noh | Effects of Collectivism on Actual S-commerce Use and the Moderating Effect of Price Consciousness[END_REF], [START_REF] Sun | Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach[END_REF]. Additionally, other studies in the literature found that PEOU has a significant positive effect on PE [START_REF] Bruner | Explaining Consumer Acceptance of Handheld Internet devices[END_REF][START_REF] Cyr | Design Aesthetics Leading to M-loyalty in mobile commerce[END_REF]. Finally, a number of studies have integrated trust to the TAM [START_REF] Gefen | Trust and TAM in Online Shopping: An Integrated Model[END_REF], [START_REF] Mccloskey | The Importance of Ease of Use, Usefulness, and Trust to Online Consumers: An Examination of the Technology Acceptance Model with Older Consumers[END_REF], [START_REF] Noh | Effects of Collectivism on Actual S-commerce Use and the Moderating Effect of Price Consciousness[END_REF] and found the PEOU has a significant positive affect on Trust [START_REF] Li | The Effects of Internet Shoppers' Trust on their Purchasing Intention in China[END_REF]. By extending the results of this study, the author proposes the following hypotheses for SNS intention of continuous usage: H2: PEOU will have a significant positive effect on behavioural intention to continue to use social networking sites.

H3: PEOU will have a significant positive effect on perceived usefulness of social networking sites.

H4: PEOU will have a significant positive effect on perceived enjoyment of social networking sites.

H5: PEOU will have a significant positive effect on the Trust of social networking sites.

Perceived Enjoyment (PE):

In this study, the researcher defines PE as the degree to which a person believes that using social networking sites for business purposes will be associated with enjoyment. If the user can enjoy the adoption of new technology, his attitude toward the adoption will be more positive than if that same technology was not enjoyable. Several studies on PE have indicated that PE significantly affects intention to use a technology [START_REF] Dickinger | The Role of Perceived Enjoyment and Social Norm in the Adoption of Technology with Network Externalities[END_REF], [START_REF] Sun | Causal Relationships between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach[END_REF], [START_REF] Teh | MOA and TRA in Social Commerce: An Integrated Model[END_REF][START_REF] Teo | An Assessment of the Influence of Perceived Enjoyment and Attitude on the Intention to Use Technology Among Pre-service Teachers: A Structural Equation Modeling Approach[END_REF]. Previous studies of s-commerce have empiri-cally added PE to the TAM [START_REF] Shen | Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites[END_REF]. Furthermore, limited studies in the context of s-commerce have found that PE has a significant positive effect on BI [START_REF] Sharma | Disclosing Too Much? Situational Factors Affecting Information Disclosure in Social Commerce Environment[END_REF], [START_REF] Shen | Social Comparison, Social Presence, and Enjoyment in the Acceptance of Social Shopping Websites[END_REF]. In contrast, [START_REF] Sharma | Intention to Engage in Social Commerce: Uses and Gratifications Approach[END_REF] found the relationship to be non-significant. Therefore, we hypothesize that: H6: Perceived enjoyment will have a significant positive effect on behavioural intention to continue to use social networking sites.

Trust (TR): [START_REF] Gefen | Trust and TAM in Online Shopping: An Integrated Model[END_REF] defined trust as "an individual willingness to depend based on the beliefs in ability, benevolence, and integrity". According to [START_REF] Gefen | Consumer Trust in B2C E-commerce and the Importance of Social Presence: Experiments in E-products and E-services[END_REF][START_REF] Gefen | Trust and TAM in Online Shopping: An Integrated Model[END_REF], trust is a key feature to enable e-commerce and is linked to uncertainty in most economic and social transactions. Furthermore, trust has a direct effect on online buying intention and on reducing uncertainty in e-commerce sites. Various studies on e-commerce adoption found trust to be a key aspect of e-commerce adoption [START_REF] Gefen | Consumer Trust in B2C E-commerce and the Importance of Social Presence: Experiments in E-products and E-services[END_REF], [START_REF] Kim | Effects of Various Characteristics of Social Commerce on Consumers' Trust and Trust Performance[END_REF][START_REF] Kim | A Trust-Based Consumer Decision-Making Model in Electronic Commerce: The Role of Trust, Perceived Risk, and Their Antecedents[END_REF], [START_REF] Li | The Effects of Internet Shoppers' Trust on their Purchasing Intention in China[END_REF][START_REF] Mccloskey | The Importance of Ease of Use, Usefulness, and Trust to Online Consumers: An Examination of the Technology Acceptance Model with Older Consumers[END_REF]. This feature needs additional consideration in cyberspace, as customers and e-vendors are not face-to-face. However, the development of s-commerce and SNS can help improve customers' trust. Customers on SNS can support each other virtually with their ratings, reviews, and recommendations. In the context of s-commerce, some studies have found that trust has a significant positive effect on behavioural behavioural intention [START_REF] Hajli | An Integrated Model for E-commerce Adoption at the Customer Level with the Impact of Social Commerce[END_REF][START_REF] Hajli | Social Commerce for Innovation[END_REF], [START_REF] Kim | Effects of Various Characteristics of Social Commerce on Consumers' Trust and Trust Performance[END_REF][START_REF] Ng | Intention to Purchase on Social Commerce Websites Across Cultures: A Cross-Regional Study[END_REF]. Accordingly, the following hypothesis has been formulated: H7: Trust will have a significant positive effect on behavioural intention to continue to use social networking sites.

Methodology

This study utilized scale items derived from the literature of technology adoption in order to measure the selected variables; i.e. Perceived Enjoyment and Trust, in addition to other variables adopted from the TAM. To do that, several items were used in order to measure the variables of the TAM which were adapted from [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[END_REF]. Additionally, perceived enjoyment items have been adopted from [START_REF] Brown | Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle[END_REF]. Furthermore, Trust items were selected from [START_REF] Gefen | Trust and TAM in Online Shopping: An Integrated Model[END_REF], [START_REF] Jarvenpaa | Consumer Trust in an Internet Store: A Cross-Cultural Validation[END_REF], [START_REF] Kim | Effects of Various Characteristics of Social Commerce on Consumers' Trust and Trust Performance[END_REF], [START_REF] Noh | Effects of Collectivism on Actual S-commerce Use and the Moderating Effect of Price Consciousness[END_REF]. The degree of responses was estimated using the seven-point scale, ranging from 'strongly agree' to 'strongly disagree'. In regard to the language of the data collection tool, the questionnaire was translated into Arabic to overcome cultural and linguistic differences [START_REF] Brislin | Comparative Research Methodology: Cross-Cultural Studies[END_REF]. A pilot study was then conducted using 15 questionnaires that were distributed to Facebook users in Saudi Arabia who were asked to give their feedback in case they faced any difficulties in answering the questionnaire [START_REF] Hair | Multivariate Data Analysis[END_REF]. Accordingly, the questionnaire's items were rechecked in terms of clarity, language simplicity, and length.

When it came to the sampling, this study implemented a convenience sampling, as the researcher does not have a list of Facebook users. Moreover, convenience sampling is cost-effective [START_REF] Dwivedi | Development of a Survey Instrument to Examine Consumer Adoption of Broadband[END_REF], [START_REF] Franzosi | From Words to Numbers: Narrative, Data and Social Science[END_REF], and the results of a convenience sample can be generalised more appropriately since it allows for the presence of a variety of profiles and characters of potential users [START_REF] Franzosi | From Words to Numbers: Narrative, Data and Social Science[END_REF]. The survey questionnaires were distributed as online web links using the online survey software Qualtrics. The web link was sent to different Saudi Arabian e-commerce groups on Facebook as this was the most popular SNS to examine users' intentions rather than non-users. Due to space constraints, scale items cannot be provided in the paper but will be available upon request.

Data Collection

The demographic details show that 59.2% of the respondents were female, while 40.8% were male respondents. In regard to the respondents' age, the descriptive statistics demonstrate that the largest age population was within 21-29 years old with 62.5%, followed by the age group of >=18-20 with 18.8%. The rest of the respondents were divided among the age groups of 30-39 (11.8%) and 40-49 (6.9%), with no respondents aged 50 and above. In regards to educational level, the majority of the respondents hold a Bachelor's degree, representing 49.0% of the total sample. The second largest group were high school graduates (34.5%) followed by 10.2% postgraduates and 5.9% diploma holders. A very small percentage of respondents held less than high school qualification (0.3%).Table 1 shows the demographic details of the respondents in the survey sample.

Results and Discussion

Validation of the Measurement Model

A confirmatory factor analysis was conducted to assess the validity of the measurement model using AMOS 22.0. To examine the overall model fit for the CFA and SEM, GOF indices were reported for this study; each of them describes the model fit from a different perspective. In the first run, most of the GOF indices fit the data well. The normed chi-square was (2.115), the GFI (.910), the AGFI (.877), NFI (.907), and RMSEA (.061): all in the acceptable benchmarks; yet, the CFI was slightly lower than the acceptable range (.948). To improve the model fit, the modification indices were checked in AMOS 22.0. Accordingly, PEOU2 and PEOU4 were correlated, and PEOU3 and PEOU4 were correlated as well. Additionally, TR1 and TR2, TR2 and TR3, and TR3 and TR4 were correlated. This indicated a good fit in the second run (X²/df: 1.705; GFI: .929; AGFI: .899; CFI: .968; NFI: .928; RMSEA: .048). Table 2 shows the results. .928 .o48 χ²: chi-square, df: degrees of freedom, χ²/df: normed chi-square, GFI: goodness-offit, AGFI: adjust goodness-of-fit, CFI: comparative fit index, NFI: normed fit index, RMSEA: root mean square error of approximation.

Item reliability, internal consistency, and discriminant validity

After the validation of the measurement model, to evaluate the reliability and validity of the research model, item reliability, internal consistency, and discriminant validity were examined [START_REF] Hair | Multivariate Data Analysis[END_REF]. Item reliability was measured using factor loadings. Item reliability is considered practically significant if each factor loading exceeds 0.5 [START_REF] Hair | Multivariate Data Analysis[END_REF]. The results of the item reliability show that all the examined items exceeded the threshold ranging from .640 (PEOU3) to .904 (PE2). This suggests that the survey tool was appropriate for evaluating each construct individually. The researcher used Cronbach's alpha for evaluating internal consistency. [START_REF] Hair | Multivariate Data Analysis[END_REF] suggested that Cronbach's alpha should exceed 0.7 for sufficient construct reliability. The results of the analysis show that Cronbach's alpha exceeds this threshold, ranging from .779 to .889. Table 3 shows the results for item reliability and internal consistency. .889

Valid N (listwise) 304

Lastly, discriminant validity was measured using the average variance extracted (AVE).

For sufficient discriminant validity, the square root of each of the AVE's constructs should exceed its correlation with any other construct [START_REF] Hair | Multivariate Data Analysis[END_REF]. Table 4 reveals sufficient discriminant validity by indicating that the correlation of each construct does not exceed the square root of its AVE.

Structural Model Assessment

Structural Equation Modeling (SEM) was utilized for testing hypotheses using AMOS 22.0. This approach produces two vital pieces of information that are used to indicate how well the structural model can predict the hypothesized relationships. Namely, the standardized path coefficients estimate (β) assesses the strength of the causal relationship between two constructs, and the squared multiple correlations (R 2 ) measure the percentage of the predictive power of the dependant construct [START_REF] Hair | Multivariate Data Analysis[END_REF]. As shown in Table 5, The results of the structural model indicate a good fit for all indices (X² :224.978; df: 125; X²/df :1.800; GFI: .923; AGFI: .895; CFI: .963; NFI: .921; RMSEA: .051). As the structural model indicated a good fit, the proposed hypotheses were examined using standardized path coefficients (β). As presented in Table 6, all the proposed hypotheses are supported except H2. First, PU had a significant positive effect on Behavioural Intention (β = .28, p < 0.001), providing support for H1 and confirming the effect of the PU of SNS on consumers' continuous intention to use the technology. Second, PE had a significant positive effect on behavioural intention. The path coefficient between PE and BI was .27 (p<0.001), providing support for H3 and suggesting that consumers who enjoy using SNS for business purposes intend to continue to use the technology in the future. Third, Trust had significant positive effects on behavioural intention to continue to use SNS. The path coefficient between TR and BI was .34 (p<0.001), providing support for H4 and suggesting that consumers who trust SNS for business purposes intend to continue to use the technology in the future. Furthermore, PEOU had significant positive effects on PU, PE, and TR. The standardized path coefficients between perceived ease of use and these three constructs were 0.62 (p<0.001), 0.67 (p<0.001), and 0.43 (p<0.001), respectively. As a result, H5, H6, and H7 are supported. The hypothesis suggests that consumers who finds SNS easy to use for business purposes are more likely to perceive it as useful, enjoyable, and trustworthy. Finally, PEOU had no significant effect on BI (β = .11, p = .237), providing no support for H2. This finding suggests that consumers are already familiar with using SNS as a business tool. Accordingly, ease of use dose not predict behavioural intention to continue to use the technology.

PU, PE, and TR explained approximately 56% of the variance in behavioural intention to continue to use SNS for business purposes. Furthermore, PEOU explained 45% of the variance in PE, and 39% of the variance in PU. Finally, PEOU explained 18% of the variance in TR. Table 6 summarizes the results of the tested hypotheses.

Research Contribution

The results of this study provide both academic and practical implications. In fact, the current study makes a significant contribution for academics by proposing the TAM model for examining the adoption of s-commerce technologies, which is a novel modern technology. Furthermore, the study also expanded the applicability of TAM by focusing on a new cultural context (Saudi Arabia). Finally, this study is able to extend the theoretical horizon of the TAM by including other external factors from the technology adoption literature, including Perceived Enjoyment and Trust. In practice, the proposed model provides business owners, managers, and marketers in developing countries understanding of continuous intention to use s-commerce technologies. It provides a suitable approach to define which factors require further attention in order to obtain the highest benefits from the adoption of s-commerce technologies by ensuring that consumers continue to accept the modern way of interacting via social media platforms to conduct business. Based on the proposed model, business owners, managers, and marketers needs to pay closer attention to some issues, particularly the finding that the most significant predicted constructs of behavioural intention to continue to use SNS is Trust. This means that business owners, managers, and marketers need to pay closer attention to build online trust with consumers, by connecting with them and providing online privacy and security policy, as well as encouraging user ratings, reviews, and recommendations on SNS to develop user generated content, which will lead to consumer trust. Furthermore, perceived ease of use was found to be a strong predictor of perceived enjoyment as well as perceived usefulness. This means that business owners, managers, and marketers needs to ensure that the SNS they are using to connect with their consumers are easy to use, and as a result, consumers will enjoy using the SNS to conduct business, and find them useful in their daily lives.

Conclusion

This study aims to identify the important factors that influence customer behavioural intention to continue to use social networking sites. The TAM has been identified as a suitable theoretical foundation for proposing a conceptual model. The study has added two further significant factors, perceived enjoyment and trust, along with TAM constructs to formulate the model. In order to accomplish the study's objectives, a quantitative field survey was circulated to obtain data from a convenience sample of Saudi customers and a self-administered questionnaire used for data collection, before the respondents' profile and characteristics were presented. Next, the study results and discussion was offered, including validation of the measurement model, item reliability, internal consistency, and discriminant validity for the investigated variables. Finally, structural model assessment and hypothesis testing was conducted. The findings indicated that trust, perceived usefulness, and perceived enjoyment play a significant role in behavioural intention to continue to use social networking sites.

Limitations and Future Research Directions

This study is limited in that the researcher views the behavioural intention of consumers of SNS rather than the consumers actual use. Future research should be conducted on consumers' actual usage behaviour within the context of social networking sites. Additionally, this study could be further strengthened to better understand the relationships between variables. Future research could explore new additional constructs to better predict the intention and actual use of SNS. Furthermore, the population of the study limits this research. As the respondents resided in Saudi Arabia, the results might not be generalizable to other populations. Accordingly, future research needs to investigate other cultures. Finally, this study did not measure how frequently the respondent consumers made use of SNS. Therefore, future research should aim to determine how often consumers participate in SNS and to what extent it motivates their purchasing decisions.

Table 1 . Respondents' Profile and Characteristics

 1

	Variable	Group	Frequency	Percent
	Gender	Male	124	40.8
		Female	180	59.2
		Total	304	100.0
	Age	>=18-20	57	18.8
		21-29	190	62.5
		30-39	36	11.8
		40-49	21	6.9
		50 and above	0	0.0
		Total	304	100.0
	Education	Less than High School	1	0.3
		High School	105	34.5
		Diploma	18	5.9
		Bachelor's degree	149	49.0
		Postgraduate	31	10.2
		Total	304	100.0

Table 2 . Summary of Fit Indices for the Measurement Model

 2

		X²	Df	X²/df GFI	AGFI CFI	NFI RMSEA
	Criteria			3:1	≥ 0.9 ≥ 0.8 ≥ 0.95	≥ 0.9 < 0.07
	1 st run	264.341	125 2.115 .910	.877	.948	.907 .061
	2 ed run	204.553	120 1.705 .929	.899	.968

Table 3 . Reliability test for investigated constructs (Cronbach's alpha)

 3

	Construct	Item	Mean	Std. Devi-	Factor	Cronbach's
				ation	Loading	alpha
	Perceived	PU1	5.56	1.368	.730	.779
	Usefulness	PU2	5.61	1.277	.766
		PU3	5.34	1.419	.702
	Perceived	PEOU1	5.40	1.463	.683	.789
	Ease Of Use	PEOU2	5.26	1.370	.729
		PEOU3	5.50	1.442	.640
		PEOU4	5.36	1.509	.712
	Perceived	PE1	5.60	1.468	.810	.867
	Enjoyment	PE2	5.65	1.395	.904
		PE3	5.54	1.444	.785
	Trust	TR1	4.50	1.774	.643	.843
		TR2	4.59	1.581	.664
		TR3	4.46	1.709	.800
		TR4	4.43	1.628	.756
		TR5	5.05	1.501	.671

Table 4 . Results for Discriminant Validity

 4 Square root of AVE is shown on the diagonal in bold. ** Correlation is significant at the 0.01 level (2-tailed).

		PU	PEOU	PE	TR	BI
	PU	0.812
	PEOU	.431 **	0.798
	PE	.446 **	.527 **	0.910
	TR	.381 **	.286 **	.332 **	0.830
	BI	.510 **	.469 **	.550 **	.513 **	0.925

Table 5 . Summary of Fit Indices for the Structural Model

 5

		X²	Df	X²/df GFI	AGFI CFI	NFI RMSEA
	Criteria			3:1	≥ 0.9 ≥ 0.8 ≥ 0.95	≥ 0.9 < 0.07
	Model	224.978	125 1.800 .923	.895	.963	.921 .051
	GOF
	χ²: chi-square, df: degrees of freedom, χ²/df: normed chi-square, GFI: goodness-of-
	fit, AGFI: adjust goodness-of-fit, CFI: comparative fit index, NFI: normed fit in-
	dex, RMSEA: root mean square error of approximation.

Table 6 . Summary of Hypotheses

 6

				Test
	H# Hypothesis		β	S.E.	C.R.	P Supported
		Dependent Var-	Independent
		iable	Variable
	H1	PU	BI	.28	.090	3.643	***	YES
	H2	PEOU	BI	.11	.110	1.182	.237	NO
	H3	PEOU	PU	.62	.083	7.630	***	YES
	H4	PEOU	PE	.67	.096	8.402	***	YES
	H5	PEOU	TR	.43	.096	5.647	***	YES
	H6	PE	BI	.27	.074	3.591	***	YES
	H7	TR	BI	.34	.056	5.607	***	YES