Amit Kumar Kushwaha

Arpan Kumar Kar
email: arpan_kar@yahoo.co.in

P Vigneswara Ilavarasan

Predicting Information Diffusion on Twitter A Deep Learning Neural Network Model using Custom Weighted Word Features

Keywords: Information Diffusion, Twitter analytics, Deep Learning, Convolutional Neural Network, Linguistic

Researchers have been experimenting with various drivers of the diffusion rate like sentiment analysis which only considers the presence of certain words in a tweet. We theorize that the diffusion of particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibit that the proposed approach is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. Through this paper, we propose a Custom Weighted Word Embedding (CWWE) to study the degree of diffusion of content (retweet on Twitter). Our framework first extracts the words, create a matrix of these words using the sequences in the tweet text. To this sequence matrix we further multiply custom weights basis the presence index in a sentence wherein higher weights are given if the impactful class of tokens/words like nouns, adjectives are used at the beginning of the sentence than at last. We then try to predict the possibility of diffusion of information using Long-Short Term Memory Deep Neural Network architecture, which in turn is further optimized on the accuracy and training execution time by a Convolutional Neural Network architecture. The results of the proposed CWWE are compared to a pre-trained glove word embedding. For experimentation, we created a corpus of size 230,000 tweets posted by more than 45,000 users in 6 months. Research experimentations reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE) from the tweet there is a significant improvement in the overall accuracy of Deep Learning framework model in predicting information diffusion through tweets.

Introduction

Microblogging website Twitter has emerged as one of the primary online social media platforms across the globe for sharing opinions, interests, and points of view on events and issues varying from education, sports, online content to politics. Users on Twitter can associate their share of opinions related to a subject matter by writing a simple tweet and posting it in real-time. The selection of right expletives and positioning the same in a tweet sentence plays a critical role in attracting the attention of right receivers (users of Twitter) of this tweet with similar interests inside the Twitter platform.

Sharples [START_REF] Sharples | How We Write: Writing as Creative Design[END_REF] has claimed that readers on any platform of communication should treat writing as a creative act of writers using which they deliberately select phrases and position them to impose a certain interpretation through lexical and linguistic preferences. This hypothesis was first developed by Maun [START_REF] Maun | Text as design, writers as designers[END_REF], showing how diverse set of writers from various backgrounds, are extremely cognizant of articulated aspects of writing, positioning the expletives with opinions that we should hypothesize as writing practice and as an act of thoughts representation. The idea of sentence writing includes the design of the factual or non-factual information of a topic, person, event or domain, followed by opinion generation through experience or knowledge expressed in words, within a framework requiring the expertise of linguistics as well as the nature of genres and domains in which the communication is taking place. The sentence then reaches the receiver (also referred to diffusion in modern world) to whom the original writer/sender was planning to convey the message to. The receiver in turn again uses the knowledge of linguistics to interpret the meaning which original sender was trying to convey. Hence linguistic competence is central to understand the degree of diffusion of information in any communication channel. Social media application Twitter bring users with similar opinion across the globe on the same platform to communicated, cascade the information to a wider set of users beyond their group referred to as information diffusion (also referred to as retweet on Twitter) in a real-time basis. This, in turn, creates mass information on Twitter. The ability of Twitter to elevate the exposure of any information through a simple tweet is contained in various aspects of the same tweet. Like the number of users connected to the account from which the tweet got generated, how active the user has been in the past, how active the followers in the network of the user are on Twitter.

To achieve effective diffusion of information, other than the previously listed factors, one more aspect that plays a key role is: the sentence used in a tweet should be conveying the appropriate information with a factual point of view. Prior research appears a little apprehensive about the accurate enriching relationship between the selection of words, placing in a certain order, and about how the thoughts get reconstructed when we alter the sequence of words. The area of linguistics has well-described literature on how a well-formed sentence structure can guide the communication of inference and opinion expressed and at the same time increase the penetration of the sentence at the user level. Aligning the sentences for an appropriate theme and rheme and the idea of linearization of the words in which 'what an author places first will drive the interpretation and penetration of rest that has to follow' [START_REF] Brown | Discourse Analysis[END_REF] are known ideas to linguist researchers. Also is the goal of concluding remarks in a sentence [START_REF] Leech | A Communicative Grammar of English[END_REF]: because the initial vocable or noun in a phrase gains more importance, and professional writers will change the order of the words to engage the reader's attention to the factual information using the active mode of writing a sentence. These are design alternatives which the writer of a sentence or a tweet on Twitter has to learn and there are options within this learning that require attention, be it direct or indirect, that a verbally spoken sentence is not the same as a written one. Using the linguistic aspect of feature formation is not entirely explored in the area of research of feature extraction from verbatim of tweet [START_REF]ICML, ACM International Conference Proceeding Series[END_REF][START_REF] Shi | Hash kernels for structured data[END_REF][START_REF] Ganchev | Small statistical models by random feature mixing[END_REF][START_REF] Colmenares | HEADS: headline generation as sequence prediction using an abstract feature-rich space[END_REF].

Through this paper, we are presenting and contribute to the literature an original and creative approach combined with the deep learning neural network architecture and the branch of linguistics for feature extraction. We assign custom weights to the word vector matrix calculated by the index of these words in the neighborhood of the sequence of the rest of the words in the same sentence. We are further defining this feature engineering framework as Custom Weighted Word Embedding (CWWE). We then try to combine this feature engineering framework with the state of the art Long Short Term Memory(LSTM) to train an initial model that is further optimized by adding convolutional layer 1D on top of embedding to make it more time-efficient while training and improve the accuracy as well. For benchmarking the results of classification using the proposed architecture, we compare the overall accuracy with a CNN model running on the same architecture using a pre-trained glove word embedding instead of the experimented Custom Weighted Word Embedding (CWWE). In the scope of current research, we hypothesize that the proposed framework works better in classifying a tweet as retweetable or not if we take into consideration the linguistic art of sentence formation. The ideology behind the research in the current paper is to create the features in such a way that these are not only geography independent but also independent of any theme of any event which makes the proposed framework easily generalizable in any situation.

The rest of the research paper is structured as: in Section 2, prior literature is discussed for defining information diffusion, social media analytics and deep leaning architecture, in section 3, we describe the proposed CWWE(Custom Weight Word Embedding). To begin the experimentation phase, we first train a base LSTM model, record the results and define as "baseline results1". We then introduce our novel framework with Convolutional Neural Network and record the results as "proposed framework results". With the same CNN architecture used to test the proposed features but predict the results using a pre-trained glove model of 10,000-word embedding instead of CWWE and define the results as "baseline results2". Finally, we discuss the findings by comparing "baseline results1" vs. "baseline results2" vs. "proposed framework results" and conclude with proposing future scope of research in Section 4.

Prior Literature

The literature review has been designed to follow the following flow: we start with defining the information diffusion, how social media analytics has helped the researchers' community to gain insights about the domain, followed by discussing the established vectorization process as one of the techniques from the social media analytics toolkit and concluding with deep learning architectures used for unstructured data like tweet text.

Information Diffusion and Social Media Analytics

With the internet opening the barriers of communication among people across the globe, there is a need to perform research on effective ways of communication. When there is more communication there is more information diffusion too [START_REF] Goel | The structural virality of online diffusion[END_REF][START_REF] Myers | Clash of the contagions: Cooperation and competition in information diffusion[END_REF][START_REF] Romero | Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on Twitter[END_REF]. We define information diffusion as the travel process of a piece of knowledge having factual or non-factual details about a person, event, domain or place getting initiated from a sender and reaching a set of receivers through a channel or carrier. In the case of Twitter [START_REF] Stieglitz | Emotions and information diffusion in social media Sentiment of microblogs and sharing behavior[END_REF][START_REF] Yoo | Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises[END_REF], the carrier is the tweet posted on the channel Twitter, the sender is the user posting the tweet and receivers are followers of the user posting the tweet. There are three aspects of information diffusion: innovation in communication practice, factual information and right audience(user-network) which plays an important role in predicting the degree of information diffusion.

With the increase of internet penetration, social media platforms have started to gain importance as a channel for message penetration and propagation. Twitter is constantly explored by organizations and individuals for multiple communications which creates an abundance of information and hence researchers have started exploring various analysis frameworks to gain insights and one such framework is Social Media Analytics (SMA). SMA as an evolving cross-domain research area for gaining insights through social media has made the usage of Twitter for information communication and diffusion across multiple domains possible. Many researchers from their work have proved the potential of SMA across different academic disciplines and business domains. Prior work strongly shows that SMA can provide important insights around disciplines such as marketing [START_REF] Aswani | Search Engine Marketing is not all gold: Insights from Twitter and SEOClerk[END_REF], in deliberation on political [START_REF] Grover | The untold story of USA presidential elections in 2016 -Insights from Twitter Analytics[END_REF] and social concerns [START_REF] Mohan | Demonetization and its Impact on the Indian Economy -Insights from Social Media Analytics[END_REF], in emergency [START_REF] Starbird | Pass it on? Retweeting in mass emergency[END_REF], for building public relation [START_REF] Grover | Impact of Corporate Social Responsibility on Reputation -Insights from Tweets on Sustainable Development Goals by CEOs[END_REF] or even ruling out possibility of polarization of any major event [START_REF] Grover | Polarization and Acculturation in US Election 2016 outcomes -Can Twitter Analytics predict changes in Voting Preferences? Technology Forecasting and Social Change[END_REF]. Social media platforms like Twitter can also help us to gain insights into any specific domain [START_REF] Grover | Technology enabled Health" -Insights from Twitter Analytics with a Socio-Technical Perspective[END_REF] or technology [START_REF] Grover | Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactionsinsights from user-generated content on Twitter[END_REF]. However, in the domain of social media, penetration and diffusion of communication has three strong pillars, firstly managing and controlling misinformation [START_REF] Aswani | Experience: Managing Misinformation in Social Media -Insights for policy makers from the Twitter Analytics[END_REF], secondly use machine learning to automatically predicting the information(tweet) diffusion speed, scale, range and trend [START_REF] Yang | Predicting the Speed, Scale, and Range of Information Diffusion in Twitter[END_REF] and thirdly use frameworks to evaluate the credibility of a tweet, i.e. whether to trust the tweet and the person writing the tweet.

Word Features and Deep Learning Architecture

The most popular model in SMA for predicting the information diffusion (retweet on Twitter) is to convert sentences to words and then to vectors and finally use these vectors as features in classifiers to predict the probability of reweet. Most of the prior experimentation performed in the retweet prediction is based on the opinion similarity analysis of the words used in the tweet or at the user account and network level. Xu and Yang [START_REF] Xu | Analyzing user retweet behavior on Twitter[END_REF] have proposed term frequency-inverse document frequency hinged BOW(bag of words) for every tweet in a content-based model for retweet prediction. The research by [START_REF] Nguyen | Analyzing information sharing strategies of users in online social networks[END_REF] presented the work of information-sharing approach and trend of users on social media platforms. The assessment of the importance of these variables in the same research presented that the term frequency-inverse document frequency weighted BOW homogeneity of retweeted tweets performing as the highest significant variable in predicting user retweet trend. Brief research also exists around computational linguistic research [START_REF] Lerman | Reading the markets: forecasting public opinion of political candidates by news analysis[END_REF] to predict the impact of any information, but there certainly exist a gap for thorough research which has motivated us to take up this research.

Many people try to reword the original sentence in their own words with the intent of communicating the original message in a much better way. However, as such, any literature or verbatim synopsis is already abstractive in nature and has very little possibility of reproduction of the original sentence. With all deep learning architectures getting noticed by researchers in the field of unstructured data, it has become a more practical option for any NLP analysis, researchers have begun to consider these frameworks as a fully data-driven alternative to abstractive text summarization. The possibility of storing the meaning of a sentence with abstract features extracted is not completely explored. In the latest research, Konstantin Lopyrev [START_REF] Riedhammer | Long story short-global unsupervised models for key phrase based meeting summarization[END_REF] relates an application of an encoder-decoder RNN(recurrent neural network) with LSTM units and notices that the framework starts creating headlines from the actual text of news articles. In a slightly different but related work, Alexander M. Rush [START_REF] Zhang | DE convolutional paragraph representation learning[END_REF] presented a fully data-driven framework for sentence summarization. His approach makes use of an attention driven algorithm that creates each word from the input sentence as summary transformed. Sumit Chopra [START_REF] Li | A hierarchical neural auto encoder for paragraphs and documents[END_REF] proposes a constraint-based RNN (recurrent neural network) which can easily create an abstract of any input phrase. The transformation has been done by a unique approach in which a convolutional encoder is implemented to make sure that the corresponding decoder centers around the proper input words at each phase of generation. In an alternate paper that sharply resonates with our proposed framework, Ramesh Nallapati [START_REF] Rush | A neural attention model for abstractive sentence summarization[END_REF] has proposed a summary-based text abstraction using Attentional Encoder-Decoder RNN (Recurrent Neural Network), and show that these models achieve state-of-the-art performance on two different corpora [START_REF] Gu | Incorporating copying mechanism in sequence-to-sequence learning[END_REF][START_REF] Zhong | Convolutional neural network: Deep learning-based classification of building quality problems[END_REF][START_REF] Yu | Deep learning for real-time social media text classification for situation awarenessusing Hurricanes Sandy, Harvey, and Irma as case studies[END_REF]. Our work starts with a similar-looking framework but with the focus of extracting and storing the original sense of a sentence (tweets on Twitter) in very abstract features(CWWE) and assigning custom weights which will be further used to predict the diffusion(retweet) of the same sentences (tweets on Twitter).

CWWE-based LSTM-CNN model experimentation

Data collection

The first step in developing a classifier is to create a training corpus. In our study, we are focusing on collecting data from Twitter. The reason behind selecting Twitter is its flexible architecture that allows one-to-many communication and is less restricted when compared to other social networking sites like Facebook.

We specifically targeted to collect tweets for "Game of Thrones season8" because there will not be any sponsored posts and this series (particularly season8) was liked, watched and hated by viewers across the globe from different ethnicity. Hence the process of data extraction, gave us tweets originated at different parts of the world with varied writing styles. To achieve this, we used the following tags: [#GOTS08, #Game of Thrones Season8, #Game of Thrones Final Season, # Game of Thrones Last Season, #GOT Season8]. Through streaming API of Twitter, we can scrap (free) one percent of tweets posted every day. Using Python as a tool, we used streaming API to collect tweets during the period: [April 14 to May 19, 2019]. This helped us to create a corpus of 300,000 tweets relevant to our analysis. Out of 300,000 tweets, a corpus of 234,884 tweets was created after data cleaning and removing the tweets which were posted by major official accounts. Finally, 234,884 tweets were labeled as "Viral" vs "Non-Viral" basis if the number of retweets of the tweet was greater than '10' referred to as "Viral" and the tweets for which the retweets was less than '10' was referred to as "Non-Viral" tweets. Table1 below represents the exploratory description of the tweet corpus.

Data cleaning

The pretreatment of the tweet text is a mandated step as it cleans and converts the actual tweet to structured features matrix, ready for analysis, i.e., it becomes easier to extract rich and factual information from the tweet and apply machine learning algorithms to it. If we bypass this data preparation step, then there is a highly likely a chance that we are dealing with noisy and incompatible data. The goal of this data cleaning exercise is to not only smoothen out the noise, the ones that are much less relevant to uncover the sentiment of tweets together with punctuation, special characters, numbers, and phrases that do not convey a lot of weight in context to the text. In the next steps of data cleaning, we are converting unstructured data of text to more structured data by generating the matrix of words extracted from the original tweet. If the initial data cleansing of tweets is done diligently, then the resulting feature space will also be of good quality.

Initial data cleaning makes every tweet to go through the following phases: Firstly, we know that owing to security concerns, Twitter never reveals the actual Twitter handle posting the tweet. Hence as part of the data cleaning process, we clear the special character sign of "@" which masks the Twitter handle. Secondly, we also know from the linguistic literature that special characters, alphanumeric, numeric and lingos do not convey any specific factual information hence, we cleaned any other special characters, alphanumeric, numeric and lingos from the actual tweets. Thirdly we have replaced most of the shorter words with the root words like: "haven't":" have not", "doesn't": "does not". Once every tweet loops through the above 3 steps we extract words from all the tweets and create a matrix with all the words from the corpus as columns. However, we have a huge corpus of tweets and hence the size of the matrix of words also becomes large. To reduce the dimension, we reduced same versions of the words to its original root word, like: {like, likable}: {like}.

Build a preliminary LSTM model with standard word embedding

For simplicity, we start with the retweet column which represents how many retweets a tweet has received. Tweets that have more than ten ('10') as the retweet value are considered as the tweets which got diffused easily and strongly and will be labeled as "Retweeted" tweets for our research. The tweets for which the retweet column has less than ten ('10') as value will be labelled as the tweets which were not able to diffuse (were not retweeted) on twitter. We define this new column as "Retweet Identifier" which will have binary values and will be treated as the class label in our research analysis. Because we have a class label classifier (retweet identifier) to label each tweet, the research problem can now be defined as supervised learning and the theories of any classifier algorithm can be implemented to train, validate and test our model. We start our feature engineering with first tokenizing the text and convert them to word vectors. For feature dimension control and reduction, we are placing a constraint of 50 words on each tweet. This means that tweets having texts shorter than 50 words are padded with zeros, and longer texts are truncated. We now start with the model training.

The architecture of the neural network starts with an embedding layer wherein each word from a lower dimension is projected to a higher dimension, which in turn helps the network to learn every word vector in a more descriptive manner. The layer takes 20000 as the first argument, which is the size of our vocabulary, and 100 as the second input parameter, which is the dimension of the embedding. The third parameter has been constrained at 50 for the maximum size of every tweet sentence. We have also divided the entire tweet corpus into three sections: training (on which we let the model to train and optimize the parameters by minimizing the loss function), validation (on which the model validates the new parameters selected after every iteration) and test data (on which the trained model is tested for its performance on an unseen data). Model results at the end of training at this step are recorded and represented in table2.

Test Data Set

Build a custom weight word embedding matrix

With the hypothesis of establishing information diffusion as a function of the weights derived from position based linguistic features [START_REF] Mohan | Demonetization and its Impact on the Indian Economy -Insights from Social Media Analytics[END_REF], we pursue with the first step of our proposed CWWE framework. To establish the relative position index, we extract each word of a tweet as an independent feature and locate the relative presence of this word feature in the presence of other words in the respective tweet. We also try to see the context in which the word is used in the tweet while conveying a message. We try to explain the above concept with a basic illustration below: James played brilliantly [manner] in the match [place] on Saturday [time] evening.

vs. The match [place] played on Saturday [time], James performed comparatively good from his last game good [manner].

In the first sentence, the writer gives more emphasis by using the word brilliantly at the beginning of the sentence and the reader might not read the entire sentence to interpret that James performed brilliantly in the match. On the contrary in the second sentence, how the player played is being placed at last, which might leave less emphasis on the player James and some of the readers might even skip that part. Considering the tweet was about player James, we hypothesize that if the word brilliantly is used at the beginning there is highly likely chance that this tweet will diffuse (will be retweeted) in the Twitter network faster as compared to the second sentence. With this hypothesis, we will be testing the proposed framework by assigning the custom weights depending upon the relative position of the words. The details of the framework are explained in the following steps below.

Step1: After the cleaning the tweet text, performing the text to Word2Vector matrix

Tweet text

James played brilliantly match Saturday good James played brilliantly in the match on Saturday evening

1 1 1 1 1 0
The match played on Saturday, James performed comparatively good from his last game

1 1 0 1 1 1 Table 3.
Step1 of building a custom weight word embedding matrix

Step 2: After generating the Word2Vector matrix, we now try to find the length of each tweet text, mid-point index, first quartile and third quartile basis the length of text

Tweet text

Length Mid-point index First quartile Third quartile James played brilliantly in the match on Saturday evening 9 5 3 6

The match played on Saturday, James performed comparatively good from his last game 13 7 4 9

Table 4. Step2 of building a custom weight word embedding matrix

Step 3: We now look at the position index of each word from the Word2Vector matrix in the tweet text and check if the index falls in the first quartile, close to mid-point, in the third quartile or beyond the third quartile. Step 4: In the last step we assign a weight of 0.75 to the word which appears close to the first quartile, nothing to the words which are close to the middle part of the sentence and 0.25 to the words which are close to the third quartile of the tweet text. We further multiply these weights for the respective words in the respective tweets to the matrix from step1. The resulting matrix for the illustration looks like below.

Build a CNN model with CWWE

The LSTM model worked well with the initial Word2Vec matrix. However, it took forever to train one epoch. CNN is known to work well in image processing domains but the potential usage of CNN architecture in unstructured data like tweet analysis is still a fairly new area for research. Literature suggests that to increase the response time of the models while training on the data, we can add a convolutional layer, by doing so, uses a "filter" over the input feature matrix and calculates a higher-level representation. We then perform max pooling through a kernel of the input dimensions which in turn reduces the parameters required and this, in turn, reduces the training time. While doing the same we also introduce the model to the new CWWE features to test if the new derived features help to increase the accuracy of the classification. Model results at the end of training at this step are captured and represented in table7. to 80%. We also see that precision and recall of the model with the proposed framework outperforms the preliminary model at 0.90 and 0.76 as compared to 0.80 and 0.40 respectively for the concerned class which is -will a tweet retweet (retweet >10). The CNN model with the proposed framework has also outperformed pre-trained established glove word-based CNN model experimentation with the same architecture with the latter's values as (accuracy: 0.70, precision:0.8, recall:0.65). Hence we can state that our initial hypothesis of a writer's deliberate choice of placing the more influential words (like nouns, adjectives) at the first half of the tweet puts more emphasis on the intent interpretation by the readers and helps to cascade the same at a faster rate on social media platform as compared to sentences/tweets where these words are used at the latter half is correct.

Discussion

The primary contribution of our research paper is CWWE with a combination of CNN in using the linguistic features. With the outcome of our experimentation set-up we confirm our hypothesis that degree and rate of any information diffusion on twitter (tweet getting retweeted) can be established as a function of how a user has designed the sentence with the constraint limitation of the size of the tweet (140 characters). Intentionally placing more impactful words related to the context of discussion can increase of potential retweet faster and hence for it is important to also understand the linguistic aspect of writing English literature. We were also able to bring down the training time of the model knowing that unstructured data will have higher dimension of features set as compared to other regular analysis. We have proved through our experimentation and research that the proposed CWWE can outperform any other established word-embedding framework.

Conclusion

Every tweet contains both micro and macro-level information about the information conveyed that can be extracted and then can be further used to represent a tweet. With this motivation we chose the semantics which represents the micro level information of a tweet and developed a framework of embedding matrix representing a writer's habit of placing the part of speech (Nouns and adjectives). We further used the rules of semantic information with the help of deep neural network framework to predict if a tweet will be retweeted on not. We proved that the diffusion of a particular content on Twitter can be driven by a sequence of nouns, adjectives, adverbs forming a sentence. We exhibited that the proposed approach in this paper is coherent with the intrinsic disposition of tweets to a common choice of words while constructing a sentence to express an opinion or sentiment. With this experimentation we have established that if a writer uses an adjective or a noun at the beginning of the sentence, there is highly likely a chance that the sentence/text/information is going to diffuse at a faster rate. Motivated by the positive results of the current experiment, as next steps for future research work, we plan to extend the CWWE along with the other features like size of the network structure, the user is part of and understand the relation with social influence on social media platform Twitter. The framework should be able to predict the exact social influence in terms of number of retweets any user will get while posting any tweet.

Table 1 .

 1 Descriptive Statistics of Data Set

	Tweet	Dis-	Viral	Non-	Average	Average	Average
	corpus	tinct	tweets	Viral	age of the	number of	number of
	size	users		tweets	twitter ac-	tweets per	followers
					count in the	user per	per user
					corpus	day
	# 234,884 46,302 56,936 177,948 6 years	9	7200

Table 2 .

 2 Model results for a preliminary LSTM model

		Accuracy
	LSTM 0.52
	Confusion Matrix
	Model	Retweet
		Yes	No
	Yes 10482 15539
	No	2561	9000
	Precision =	0.8
	Recall	=	0.4

Table 5 .

 5 Step3 of building a custom weight word embedding matrix

	Tweet text	James played	brilliantly	match	Saturday	good
	James played brilliantly in the	First	First	Third	Third	Third	0
	match on Saturday evening
	The match played on Saturday,	Mid	First	0	First	First	Third
	James performed compara-
	tively good from his last game

Table 6 .

 6 Final step of building a custom weight word embedding matrix

In this step, we use the same network architecture from 3.5 above, but instead of using CWWE, we now use the pre-trained glove 100-dimension word embedding as input features and consider the results as a benchmark to compare against the results recorded from step 3.5. If the results from step 3.5, outperform the results of the current step then our original hypothesis is correct. The glove model used in this sub-section is pre-trained on four hundred thousand (400,000) words over a billion tokens. The glove has embedding vector sizes, including 50, 100, 200 and 300 dimensions. We chose the 100-dimensional version. We also want to see the model behavior in case the learned word weights do not get updated. We, therefore, set the trainable attribute for the model to be False. The results of this step are recorded and represented in table8 below.

Summarization of the results

A summarization of the results reveal that using the proposed framework of Custom Weighted Word Embedding (CWWE), if the word features extracted from the tweet are assigned custom weights basis the relative position of these words in the sentence, we record a significant improvement in the overall accuracy of CNN model from 53%


Ruan Spies

Adele Botha

A Scoping Review of the Application of the Task-Technology Fit Theory

Keywords:

The Task-Technology Fit (TTF) theory provides a means of quantifying the effectiveness of technology in a system by assessing the relationship between the technology and the tasks the technology aims to support. The theory is widely recognized and has been applied in various ways, but little work has been done to summarize and synthesize the application of TTF in literature. The aim of this study is to identify and summarize the focus areas of studies that applied TTF, the environment in which it was applied, and the technologies which were considered by conducting a scoping review. It was found that applied studies focused primarily on generating theory or assessing certain real-world phenomena; was applied in a wide range of environments with the majority being in healthcare; and considered various technologies, with an increasing number of studies focusing on mobile technology. The findings of this study contribute to the understanding of TTF applications and assists in framing future research to further analyze TTF studies.

Introduction

Technology has become a fundamental aspect of our society and is embedded into everyday life. The way individuals and organizations work and think continues to be shaped by, and shape, various technologies [START_REF] Orlikowski | Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations[END_REF]. In an organization, technology is typically applied to generate value by improving or supporting individual and collective tasks but do require a lot of resources for the acquisition, implementation and usage of the various technologies. Therefore, a common question is how much value the technology creates for an organization [START_REF] Melville | Review: Information Technology and Organizational Performance[END_REF].

Due to the complexity of the interactions between factors such as the technology, users, systems, tasks and process it is extremely difficult to directly measure the value that technology creates in a system [START_REF] Orlikowski | Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations[END_REF][START_REF] Goodhue | Task-technology fit and individual performance[END_REF]. Alternative measures have therefore been proposed to quantify the impact of technology. One such measure, widely accepted in Information System (IS) research [START_REF] Lee | An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences[END_REF], is the Task-Technology Fit (TTF) theory which has been suggested to be "one of the most important developments in information system theory" [START_REF] Melchor-Ferrer | Financial information management for university departments, using open-source software[END_REF]. TTF provides a means to quantify the effectiveness of technology in an organizations [START_REF] Goodhue | Development and Measurement Validity of a Task-Technology Fit Instrument for User Evaluations of Information System[END_REF]. In 2012, Furneaux [START_REF] Furneaux | Task-Technology Fit Theory -A Survey and Synopsis of the Literature[END_REF] identified the "notable increase in the use of TTF theory". This trend has continued over the years, with both an increase in the yearly number of documents published as well as in the range of environments which the theory is used, as discussed in this paper.

Despite the wide use of the theory, only two publications by Cane and McCarthy [START_REF] Cane | Analyzing the factors that affect information systems use: A tasktechnology fit meta-analysis[END_REF] and the work by Furneaux [START_REF] Furneaux | Task-Technology Fit Theory -A Survey and Synopsis of the Literature[END_REF] was found that summarizes and synthesizes TTF related studies. The article by Cane and McCarthy [8] is limited to a small number of studies, all of which were conducted before 2006, and only considered how the studies defined TTF and its surrounding components. Furneaux [START_REF] Furneaux | Task-Technology Fit Theory -A Survey and Synopsis of the Literature[END_REF] considered a much larger sample of studies where TTF was used, all of which were conducted before 2010, to synthesize the conceptualization, methodologies and research contexts. Where both of the former studies focused primarily on how TTF was applied, this study is concerned with why, where and on what TTF was applied.

The aim of this paper is to conduct a scoping review (SR) aims to explore the application of the Task-technology fit theory by (1) identifying the scope and range of the available literature that applies the specific components of TTF; and (2) summarizing and disseminating the research findings, specifically, (a) the purpose of applying TTF, (b) the environments where TTF has been applied, (c) the technologies that TTF has been applied on. Based on these objectives, the research questions to be answered in this paper are: [START_REF] Orlikowski | Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations[END_REF] What are the intentions for applying TTF? (2) Which environments has TTF been applied in? (3) What technologies has TTF been applied on?

2

Conceptualization of Task-Technology Fit

In this section, the TTF model, proposed in the seminal article by Goodhue and Thompson [START_REF] Goodhue | Task-technology fit and individual performance[END_REF], is discussed. Goodhue and Thompson [START_REF] Goodhue | Task-technology fit and individual performance[END_REF] The basic TTF model, depicted in Fig. 1, therefore provides a lens of technology usage and the value that it creates [START_REF] Goodhue | Task-technology fit and individual performance[END_REF]. In a setting where technology is used by individual's to perform certain tasks, or sets of tasks, the model's premise is that the value/performance of technology is created by the alignment, or fit, of the task requirements and the technology characteristics that allow a user to perform the tasks [START_REF] Goodhue | Development and Measurement Validity of a Task-Technology Fit Instrument for User Evaluations of Information System[END_REF][START_REF] Goodhue | User evaluations of IS as surrogates for objective performance[END_REF].

Fig. 1. Basic Task-Technology Fit Model

Tasks refer to the totality of physical and/or cognitive actions and processes done by individuals in a given environment. Task characteristics are considered specifically in relation to the technology that supports the tasks and are broken down to different levels of detail, depending on the complexity of the tasks performed [START_REF] Goodhue | Task-technology fit and individual performance[END_REF][START_REF] Ammenwerth | IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study[END_REF].

Applying the TTF theory in different environments will result in different specific task characteristics. However, in literature, the process followed to identify the task characteristics are similar and typically involve analyzing the tasks performed in an environment and creating various task categories and subcategories that can be related to TTF [START_REF] Lee | An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences[END_REF][START_REF] Goodhue | Development and Measurement Validity of a Task-Technology Fit Instrument for User Evaluations of Information System[END_REF][START_REF] Barki | System Use-Related Activity: An Behavioral Conceptualization of Expanded Information System Use[END_REF][START_REF] Wang | Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system[END_REF][START_REF] Ali | Focus Section Health IT Usability: Applying a Task-Technology Fit Model to Adapt an Electronic Patient Portal for Patient Work[END_REF].

Technology is defined as the tools that are used by individuals to execute, or assist in executing, their tasks [START_REF] Goodhue | Task-technology fit and individual performance[END_REF][START_REF] Ammenwerth | IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study[END_REF][START_REF] Yang | How the internet of things technology enhances emergency response operations[END_REF]. Similar to the discussion of task characteristics, different technologies will have different characteristics which are defined by the researcher with consideration to the environment which it used in and the tasks it aims to support [START_REF] Gebauer | Success factors and impacts of mobile business applications: Results from a mobile e-procurement study[END_REF].

TTF is defined as the extent to which a technology assists an individual in performing his or her tasks [START_REF] Goodhue | Task-technology fit and individual performance[END_REF][START_REF] Ammenwerth | IT-adoption and the interaction of task, technology and individuals: A fit framework and a case study[END_REF][START_REF] Yang | How the internet of things technology enhances emergency response operations[END_REF][START_REF] Dishaw | Assessing software maintenance tool utilization using tasktechnology fit and fitness-for-use models[END_REF][START_REF] Howard | Refining and extending task-technology fit theory: Creation of two task-technology fit scales and empirical clarification of the construct[END_REF][START_REF] Fuller | Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks[END_REF] and is consequently affected by the interaction between the characteristics of the task and the functionalities of the technology [START_REF] Lee | An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences[END_REF]. Typical dimensions that are considered when measuring fit, as defined by Goodhue and Thompson [START_REF] Goodhue | Task-technology fit and individual performance[END_REF], are data quality, data locatability, authorization to access data, data compatibility, ease of use/training, production timeliness, systems reliability, information system relationship with users.

The purpose of the study by Goodhue and Thompson [START_REF] Goodhue | Task-technology fit and individual performance[END_REF] was to show that a positive impact of technology requires a good TTF. Thus, when technology fits the task characteristics it aims to support, it should result in improved performance [START_REF] Goodhue | User evaluations of IS as surrogates for objective performance[END_REF]. The improved performance is typically due to the smooth execution of the task, reducing the cost of performing the task, or making the task easier to accomplish [START_REF] Lee | An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences[END_REF].

Methodology

In 2005, Arksey and O'Malley [START_REF] Arksey | Scoping studies: Towards a methodological framework[END_REF] published the influential article in which it was discussed that there exists ambiguity in the "plethora of terminology" which is used to describe various reviews of literature. The article makes the case that literature review methodologies provide a set of tools which may be used by researchers and that there does not exist an "ideal type" of literature review.

The article argues for SRs to be used to develop an overview of domain knowledge. This is achieved by identifying the quantity of literature and analyzing key features of the literature which differs slightly from traditional systematic reviews which aim to Task Characteristics Technology Characteristics

Task-Technology Fit

Performance Impacts comprehensively synthesize literature. The SR does however follow a similar methodological approach as traditional systematic reviews and can also be considered to be replicable and reliable. These features of the SR make it a suitable methodology to answer this study's research questions.

The SR conducted in this study follows the first five stages of the methodology proposed by Arksey and O'Malley [START_REF] Arksey | Scoping studies: Towards a methodological framework[END_REF], namely, to (1) identify the research question; (2) identify relevant studies; (3) select studies; (4) chart the data; (5) collate, summarize and report the results.

The first stage is completed in the Section 1 of this study and stages 2-5 are discussed in the following sections and subsections.

Search Strategy

To identify relevant studies that can be used to answer the research questions, a search was conducted on the Scopus research database. Scopus was chosen due to the large number of indexed journals covering a wide range of disciplines. Relevant studies were considered articles published in peer-reviewed journals that apply the TTF theory as part of the primary aim of the study and includes any studies that summarizes work done. In literature, it was found that the term application, in relation to TTF, is used in a broad manner, extending to the use of smaller concepts, adaptations and findings of TTF as part of the studies. In this paper, application (of TTF) is used in a specified manner, to only consider where TTF is used as conceptualized in Section 2.

An exploratory search was conducted with the terms "Task-technology fit" OR "technology-to-performance". The titles and abstracts of the top ten cited articles and top ten most recent articles were examined to identify relevant keywords to be included to ensure the comprehensiveness of the search. Variations of the terms evaluate, apply, diagnose and assess were found to be relevant and included in the search. The search was conducted on 04 October 2019 with the search criteria including the terms ("Tasktechnology fit" OR "technology-to-performance") AND (evaluat* OR appl* OR diagnos* OR assess*), limited to journal articles, which yielded 173 results.

Various measures were taken to ensure that all influential articles were included in the study. Bibliometrix, an R-tool for comprehensive science mapping analysis [START_REF] Aria | bibliometrix: An R-tool for comprehensive science mapping analysis[END_REF], was used to analyze the most cited articles out of the local 173 results, resulting in one article being added. The tool was also used to determine the top locally cited authors. A further search was done on Scopus and ResearchGate to determine whether any further important studies has been published by these authors, resulting in two articles being added. As a final measure, as full-text articles were screened, a list was compiled of the literature each study used as a theoretical foundation. All of the items on the list were however already included and therefore no further studies were added.

Study Selection

The research questions were used to define the inclusion criteria, and inversely the exclusion criteria, as shown in Table 1. As discussed in the previous section, a large number of studies uses certain concepts of TTF without discussing or applying the theory as conceptualized in Section 2, the three exclusion criteria was aimed to specifically eliminate these articles.

The titles and abstracts of the 176 studies were screened and 43 studies were excluded from the review based on EC 1. The online availability of the remaining 133 was checked and 5 were found to be unobtainable. The available full-text studies were downloaded, and the data of the studies was exported from Scopus into MS Excel. The criteria were applied to the abstracts and the full-text articles and 91 studies were excluded due to the content being irrelevant to the study, based on EC 2 and EC 3.

The entire process followed in selecting relevant studies, based on the PRISMA statement for reporting systematic reviews [START_REF] Moher | Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement[END_REF], is depicted in Fig. 2. Fig. 2. Study selection process. Adapted from [START_REF] Moher | Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement[END_REF].

The study selection process resulted in a total of 37 studies, that applies or discusses TTF as conceptualized in Section 2, being included in the review. The process followed to identify and extract the data is discussed in the following section.

Identification

Included

Article included in the review (n = 37)

Charting the Data

The charting phases required the articles included in the review to be re-read thoroughly with the aim of extracting the data relevant to the research questions. A charting form was collectively defined by the researchers to ensure credibility of the data to be extracted. The full-text articles were imported into ATLAS.ti, software used for qualitative data analysis and the charting form was used to define the categories for the coding process. The full-text studies were then read through and coded as applicable.

Results

In this section, the results obtained from analyzing the studies are discussed to answer the research questions.

Study Categories

The coded data, identified as part of the process discussed in Section 3.3, was grouped together, and different levels of categories were defined, as depicted in Fig. 3. The first high-level category was defined as studies that apply TTF with the primary aim of generating theory, termed "Generate Theory". The theory generated was found to be primarily concerned with defining or refining the measurements used for the TTF components or the TTF model; summarizing existing studies; testing the components and/or relationships of the TTF model.

The second high-level category was defined as studies that apply TTF in order to assess certain real-world phenomena, termed "Assess Phenomena". These applications were concerned with two major aspectsthe components, such as the technology characteristics, that are antecedents to TTF and the assessing effects of TTF. These studies concerned with assessing the effects were further broken down into those that assess the impact or benefit of TTF and those that assess factors relating to the use, or intention to use, of the technology based on TTF.

Relevant TTF Studies

Generate Theory

Measurements [START_REF] Goodhue | Development and Measurement Validity of a Task-Technology Fit Instrument for User Evaluations of Information System[END_REF][START_REF] Goodhue | User evaluations of IS as surrogates for objective performance[END_REF][START_REF] Gebauer | Success factors and impacts of mobile business applications: Results from a mobile e-procurement study[END_REF] Model [START_REF] Goodhue | Task-technology fit and individual performance[END_REF][START_REF] Howard | Refining and extending task-technology fit theory: Creation of two task-technology fit scales and empirical clarification of the construct[END_REF][START_REF] Dishaw | Supporting software maintenance with software engineering tools: A Computed task-technology fit analysis[END_REF][START_REF] Gebauer | Applying the Theory of Task -Technology Fit to Mobile Technology: The Role of User Mobility[END_REF][START_REF] Liu | Evaluating the effects of task-individual-technology fit in multi-DSS models context: A two-phase view[END_REF][START_REF] Yang | Are all fits created equal? a nonlinear perspective on task-technology fit[END_REF] Summarize [START_REF] Cane | Analyzing the factors that affect information systems use: A tasktechnology fit meta-analysis[END_REF] Test [START_REF] Staples | Testing the technology-to-performance chain model[END_REF][START_REF] Zigurs | A Test of Task-Technology Fit Theory for Group Support Systems[END_REF] Assess Phenomena Antecedents [START_REF] Melchor-Ferrer | Financial information management for university departments, using open-source software[END_REF][START_REF] Ali | Focus Section Health IT Usability: Applying a Task-Technology Fit Model to Adapt an Electronic Patient Portal for Patient Work[END_REF][START_REF] Gebauer | Exploring the black box of task-technology fit[END_REF][START_REF] Hsiao | An Investigation on Fit of Mobile Nursing Information Systems for Nursing Performance[END_REF][START_REF] Pelzer | Planning Support Systems and Task-Technology Fit: A Comparative Case Study[END_REF][START_REF] Rivera | Developing mobile services: A look at first-time and repeat visitors in a small island destination[END_REF] Effects [START_REF] Arksey | Scoping studies: Towards a methodological framework[END_REF] Impact/Benefit [START_REF] Bere | Applying an extended task-technology fit for establishing determinants of mobile learning: An instant messaging initiative[END_REF][START_REF] Cady | Task-Technology Fit of Video Telehealth for Nurses in an Outpatient Clinic Setting[END_REF][START_REF] Lepanto | Assessing task-technology fit in a PACS upgrade: Do users' and developers' appraisals converge?[END_REF][START_REF] Chen | Applying Task-Technology Fit Model to the Healthcare Sector: a Case Study of Hospitals' Computed Tomography Patient-Referral Mechanism[END_REF][START_REF] Glowalla | ERP system fit -An explorative task and data quality perspective[END_REF][START_REF] Ioimo | Police Field Mobile Computing: Applying the Theory of Task-Technology Fit[END_REF][START_REF] Kim | Sequential patient recruitment monitoring in multi-center clinical trials[END_REF][START_REF] Mcgill | A task-technology fit view of learning management system impact[END_REF][START_REF] Raven | Digital video presentation and student performance: A task technology fit perspective[END_REF][START_REF] Tam | Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective[END_REF][START_REF] Yi | The influence of smartphones on academic performance: The development of the technology-to-performance chain model[END_REF] (Intention to) Use [START_REF] Lee | An empirical study of mobile commerce in insurance industry: Task-technology fit and individual differences[END_REF][START_REF] Wang | Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system[END_REF][START_REF] Dishaw | Assessing software maintenance tool utilization using tasktechnology fit and fitness-for-use models[END_REF][START_REF] Vongjaturapat | Application of the Task-Technology Fit Model to Structure and Evaluation of the Adoption of Smartphones for Online Library Systems[END_REF][START_REF] Chang | Intelligent agent's technology characteristics applied to online auctions' task: A combined model of TTF and TAM[END_REF][START_REF] Huang | What factors satisfy e-book store customers? Development of a model to evaluate e-book user behavior and satisfaction[END_REF][START_REF] Pendharkar | Development and testing of an instrument for measuring the user evaluations of information technology in health care[END_REF][START_REF] Sheehan | A comparison of usability factors of four mobile devices for accessing healthcare information by adolescents[END_REF] The two high level categories were used to determine the trends in the type of studies published over time. The cumulative number of studies published for the two highestlevel categories was calculated over time and depicted in . From the figure, it can be seen that a larger number of studies were initially published that aimed to generate theory and that as time progressed the focus shifted to the application of TTF to assess phenomena.

As the theory gained recognition, different aspects of the theory was refined and the impact of applying the theory for assessing different phenomena was recognized, the focus of studies is expected to shift from predominantly theory based to applying it, as seen in the increasing trend in the figureeventually exponentially overtaking the theoretical. The assessment of TTF antecedents was only introduced in 2009, 11 years after the first application of the theory to assess the impact/benefit and is still an emerging area of research which does hold a lot of potential. Since the antecedents have an effect on TTF, applying the theory to assess these factors has shown to be beneficial in identifying improvements that can be made to ultimately improve TTF.

Application Environment

For each of the relevant studies, the specific environment in which the TTF was applied was identified, collectively categorized by the researchers and is summarized in Fig. 5.

The majority of the studies were applied in either a healthcare setting or in various different, but not explicitly identified, environments. Although the tasks performed in these different environments will vary greatly, the wide range of application environments provides evidence that the TTF can applied in diverse settings. Furthermore, the diverse environments create the opportunity for interesting future research to be done in the similarities and differences in the way in which TTF is applied.

Assessed Technology

The technology used in each of the relevant studies was also identified, collectively categorized by the researchers and is summarized in Fig. 6. Fig. 6. Divisions of the technology assessed in the relevant studies.

Since the TTF theory is mainly part of IS research, it was expected that the application of TTF would primarily be concerned with IS. An interesting observation was the growing research interest in mobile technology. The first two relevant studies applying TTF to mobile technology [START_REF] Gebauer | Success factors and impacts of mobile business applications: Results from a mobile e-procurement study[END_REF][START_REF] Ioimo | Police Field Mobile Computing: Applying the Theory of Task-Technology Fit[END_REF] was published in 2004 after which the majority of future published articles included in this study (40.7%) was focused on mobile technology. This finding makes sense in considering the rapid uptake, improved capabilities and gradual dependence on smartphone technology since 2004.

Conclusion

The TTF theory has been an important development in IS research and has been widely applied in various environments and on a range of technologies. This study aimed to identify and analyze relevant literature that applies the specific components of TTF to answer three research questions concerned with why, where and on what TTF was applied.

As part of the chosen SR methodology, a structured search and screening approach was followed to identify relevant studies, the detailed discussion on the process followed ensuring the reliability and replicability thereof. These studies were then analyzed, the data extracted, and the findings synthesized to answer the research questions. The implementation and discussion of the methodology ensured the validity and reliability of the study. The first research question is answered in the analysis and synthesis of the intentions for applying TTF where various levels of categories were defined for the intentions. The categories were further used to determine specific trends over time, revealing how the focus of studies shifted from predominantly theory based to the application of TTF to assess various phenomena.

The analysis and synthesis of the application environment and assessed technology revealed the wide range of environments and technologies associated with the application of TTF, consequently answering both the second and final research questions. These findings reveal the wide range of applicability of the TTF theory and also the potential that it holds for both industry and academia in applying TTF in different environments and with new technologies.

The results of the scoping review are dependent on the studies included for analysis. The initial search for relevant articles is limited in that only one database was used to search for relevant articles. This may have resulted in further relevant works being excluded from this study. Various measures were however taken to ensure that all influential studies potentially missed in the search were included in this study. Although the intention was to define screening criteria in a manner that would ensure reliability and replicability of the screening process, the authors acknowledge that Despite the limitations of the study, the findings of this study are valuable and further assists in framing future research on the application of TTF. The different studies which apply TTF may be further analyzed to determine trends in the methodologies followed within and/or across the different application categories, environments and technologies. This in turn may be used to provide guidelines in the quantitative measurement of different TTF constructs, given a specific intention for, and context of, use. The suggested future research may assist in the classic pursuit to better understand, account for and explain the impact of technologies within a system.

  articles excluded: Unobtainable (n = 5) Content not relevant (n = 91)

Fig. 3 .

 3 Fig. 3. Categorization of relevant TTF studies.

Fig. 4 .

 4 Fig. 4. Cumulative number of studies over time for each study category.

Fig. 5 .

 5 Fig. 5. Divisions of TTF application environments.

Table 1 .

 1 Inclusion and Exclusion Criteria

	Inclusion Criteria	Exclusion Criteria
	IC 1 Application of the TTF	EC 1 No mention of TTF in abstract
	IC 2 Thorough discussion of theory	EC 2 Isolate TTF concepts
		EC 3 TTF not central to the study


Mahmood Hussain

Raza Muhammad

Nisreen Ameen

Cybersecurity Readiness of E-tail Organisations: A Technical Perspective

Keywords: Cybersecurity, Technical Readiness, Organisational Readiness, Network Security, Cyber Threats and Risks

Cybersecurity readiness is a challenging issue for online retail businesses which are losing billions of dollars due to cyber-crimes and a lack of readiness to manage these. Therefore, research into cybersecurity readiness in the online retail industry is needed. Technical tools are the foremost measures of defence against these attacks. This study investigates cybersecurity readiness from the technical perspective in some UK online retailers. This research adopted a qualitative case study approach with semi-structured interviews for collecting data. A total of 15 interviews were conducted with an online retail company's staff and management who had responsibility for managing cybersecurity. A thematic analysis method was used to analyse the qualitative data. The research findings show that the company is facing internal and external threats to their information systems and their technical defences are not very effective at present. The company should consider investing more resources in the technical controls to prevent these attacks.

Introduction

Cybersecurity has received significant attention from researchers and professionals. It has become an integral part of business activities in all organisations regardless of their size and nature and has become particularly important for online businesses. Cybersecurity readiness can be achieved by implementing a resilient culture against cyber related threats and attacks. This culture would be useful for organisations to mitigate the impact of cyber-attacks. However, these businesses are facing cybersecurity threats/attacks from both internal and external sources [START_REF] Manhart | Protecting organizational knowledge: A structured literature review[END_REF]. Cyber threats/attacks and frauds are increasing and posing many challenges for online organisations. These challenges include externals/ internals threats accidental damage and technical/organisational weaknesses.

Cyber threats include identity theft [START_REF] Humaidi | Indirect effect of management support on users' compliance behaviour towards information security policies[END_REF] and unauthorised access to an organisational network [START_REF] Da Veiga | A cybersecurity culture research philosophy and approach to develop a valid and reliable measuring instrument[END_REF]. Denial of Service (DoS) attacks, malicious insiders, web-based attacks [START_REF] Clark | Unlike chess, everyone must continue playing after a cyber-attack[END_REF], human error [START_REF] Da Veiga | A cybersecurity culture research philosophy and approach to develop a valid and reliable measuring instrument[END_REF], phishing emails and inadequate security monitoring [START_REF] Safa | An information security knowledge-sharing model in organizations[END_REF] are also documented threats. There are some reasons which contribute to the success of these attacks, for example, preventative equipment failures [START_REF] Reason | Managing the risks of organizational accidents[END_REF], lack of technical awareness [START_REF] Pieters | Security-by-experiment: Lessons from responsible deployment in cyberspace[END_REF], unauthorised access [START_REF] Da Veiga | A cybersecurity culture research philosophy and approach to develop a valid and reliable measuring instrument[END_REF] and malicious employees [START_REF] Clark | Unlike chess, everyone must continue playing after a cyber-attack[END_REF]. Some basic security controls such as encryption, anti-virus software, firewalls and intrusion detection systems (IDS) suggested by Sen, Ahmed, and Islam (2015) [START_REF] Sen | A study on e-commerce security issues and solutions[END_REF] could be used to prevent cyberattacks. Unified Threat Management Systems (UTMS) provide more security to the network layer, hardware and software than standard security methods [START_REF] Kent | How South African SMEs address cyber security: The case of web server logs and intrusion detection[END_REF]. Secure authentication and authorisation systems are useful in preventing ID theft [10]. Regular assessment of security controls and monitoring of internal and external security systems may reduce the risk of cyber-attacks [START_REF] Taylor | Mobile payment technologies in retail: A review of potential benefits and risks[END_REF]. The aim of this research is to investigate the cybersecurity readiness, from a technical perspective, in an online retail organization, to assess how resilient its security infrastructure has been built to mitigate cyber-attacks. To achieve this aim, a qualitative case study was conducted and a total of 15 semi-structured interviews were conducted at an E-tail company in the UK. The semi-structured interviews provided an opportunity for face-toface interactions with managers and other relevant staff. Additionally, policy documents were utilised in order to better understand the security processes in the company. Collected data was analysed using thematic analysis.

Literature Review

Cybersecurity readiness includes security policies, processes and procedures that are employed in the organisation to manage cyber threats. Furthermore, a review of cybersecurity readiness includes examinations of security functions, to check whether these functions operate in line with relevant policies, standards or procedures [START_REF] Pereira | A security audit framework to manage information system security. Global security, safety, and sustainability[END_REF]. The importance of cybersecurity readiness has been increasingly recognised worldwide. Many leading countries have invested in their cybersecurity and have published official strategy documents for their cybersecurity; these include USA, UK, Canada, Australia, Japan, Germany and Russia [START_REF] Klimburg | National cyber security framework manual[END_REF].

Cybersecurity breaches are becoming increasingly common against companies regardless of their size and nature [START_REF] Waly | Improving organisational information security management: The impact of training and awareness[END_REF]. Cyber-attacks are malicious acts usually originating from an anonymous source that either steals, alters or destroys a specified target by hacking into a susceptible system. According to [START_REF] Uma | A survey on various cyber attacks and their classification[END_REF] [START_REF] Uma | A survey on various cyber attacks and their classification[END_REF], several dimensions of cyber-attacks can be found in existing literature, but the primary objective of such attacks is to compromise the confidentiality, integrity and availability of information resources. These cyber-attacks tend to be successful due to weaknesses in technical infrastructure [START_REF] Shinde | Cyber security analysis using vulnerability assessment and penetration testing[END_REF]. Due to a lack technical awareness, people become victims of cyber-attacks [START_REF] Pieters | Security-by-experiment: Lessons from responsible deployment in cyberspace[END_REF]. Therefore, this research focuses on cybersecurity readiness in the technical perspective to aid the online retail company in mitigating against potential cyber risk.

With the advancement in the technology, new methods of cyber-attacks are also emerging [START_REF] Waly | Improving organisational information security management: The impact of training and awareness[END_REF]. It is the responsibility of management to perform risk analyses and highlight flaws and vulnerabilities in the information systems, as neglecting these tasks can increase the likelihood of successful cybersecurity attacks. Therefore, online retail organisations must maintain update infrastructure to reduce the impacts of cyber-attacks in the organisation. Ultimately, these attacks affect organisations in the form of significant financial losses and reputational damage.

There are many technical threats that are possible reasons for cybersecurity breaches in online retail organisations. These include: Malware, Spam, Phishing, Spear-Phishing Attack, Denial of Service (DoS) attack, Distributed Denial of Service (DDoS), Man in Middle Attack, Hacking, Social Engineering, Spoofing, Keylogging, Cookies, Backdoor Trojan, SQL Injection and Identify Theft.

Impact of Cyber-attacks in Online Retail Organisations

Cybersecurity threats are a growing concern for online retail organisations. Organisations considered cyberattacks to be the biggest threat to businesses. A recent study by Hui, Kim, and Wang (2017) [START_REF] Hui | Cybercrime deterrence and international legislation: Evidence from distributed denial of service attacks[END_REF] indicated that many DDOS attacks targeted banks (24%), telecommunications companies (23%) and financial services organizations (20%), indicating they were likely financially motivated. Another survey conducted by the PWC (2018) [START_REF]Revitalizing privacy and trust in a data-driven world: Key findings from the global state of information security® survey 2018[END_REF] indicates that the average financial cost of cybersecurity incidents (including costs relating to business operations and data) is £857,000. The same report also pointed out that UK organisations are more reluctant in combating against cyber-attacks than peer organisations in the other countries.

Countermeasures

The above discussion was about cybersecurity threats and attacks that affect online businesses in various forms. Effective counter measures are needed to prevent cybersecurity threats from materialising. There are several factors such as technical, organisational and human which increase the success rate of these attacks, for example, Uma and Padmavathi (2013) [START_REF] Uma | A survey on various cyber attacks and their classification[END_REF] state that there is a lack of proper understanding and technical awareness of the nature of cyber-attacks. By implementing security measures and controls, companies can help mitigate against these attacks.

Legitimate antivirus or endpoint security software along with user awareness regarding threats posed by clicking on suspicious links would be useful in mitigating cyber attacks. Organisations also use anti-spam software to limit the spam attacks, coupled with other countermeasures such as two factor authentication, web application scans, firewalls, access control, encryption and unified threat management appliances. However, the focus of this study is only cybersecurity readiness from a technical perspective and this can be achieved by proper implementation of technical controls to safeguard organisational infrastructure to mitigate the potential cyber-attacks.

Methodology

This study employs a qualitative case study approach, and focuses predominately on the perspectives conveyed by respondents, for instance, how they undertake their job roles to manage cybersecurity readiness in the online retail organisation. This approach allows the investigator to study real life events and managerial processes and it examines an existing phenomenon in depth within its real life situation [START_REF] Yin | Case study research: Design and methods[END_REF]. This case study has been carried out in close interaction with practitioners who deal with managerial situations, so this approach is suitable to create relevant knowledge [START_REF] Walsham | Interpretive case studies in IS research: Nature and method[END_REF].

Participant Selection and Data Gathering

The study takes a case study based approach, using semi-structured interviews as the primary data collection method. The interview questionnaire was designed with the help of existing literature relevant to the field of information security. A pilot study was conducted with 10 academic staff in the relevant area of study and as a result, some amendments were made to the interview questions according to their suggestions. Moreover, participants were selected on the basis of their job nature and experience in the management of online security in the online retail company in the UK. A total of 15 interviews were conducted with professionals including the IT Manager, Fraud Prevention Manager and members of the Network Security Team. Face-to-face interviews lasted around 45-60 minutes. All interviews were audio recorded in a hand-held device to be transcribed and analysed later..

Data Analysis

The semi-structured interviews were transcribed manually. Furthermore, The Nvivo software was also used for the categorisation and coding of themes. This software is useful in transcribing, grouping, and coding the data. The thematic analysis method used to analyse the semi-structured interviews was proposed by [START_REF] Braun | Using thematic analysis in psychology[END_REF] [START_REF] Braun | Using thematic analysis in psychology[END_REF]. Transcribed interviews were analysed using the inductive thematic analysis. This technique provides a facility to identify a set of emergent topics in the data. Each theme was carefully developed based on the analysis of transcribed text from interviews.

Results and Discussions

For this research, we conducted case study research at one large organisation. For confidentiality reasons, the case company will be referred henceforth as Company A. Company A a leading multi-brand online retailer in the UK and Ireland, selling thousands of different brands supplied by others as well as its own brand of retail goods. In this section, we bring together the various observations from the data collected using semi-structured interviews at Company A to manage cybersecurity readiness in the technical context. Cybersecurity readiness can be achieved by implementing the following security controls (themes) which emerged from the analysis of collected qualitative data.

a) Access Control and Authentication

Usernames and passwords are treated commonly as authentication, but it is not a secure form of authentication because anyone can use these details and gain easy access to the systems. It is difficult for systems to recognise whether the user who has accessed system is genuine or fraudulent. Company A has two-factor authentication system to prevent unauthorised access. However, two-factor authentication is not always a secure method as cyber-criminals can violate this [START_REF] Srinivas | Cyber security: The state of the practice in public sector companies in India[END_REF]. To make the authentication system more effective, biometric authentication systems may be used, for example, voice recognition, facial recognition and fingerprint scanning [START_REF] Tan | Developing business analytic capabilities for combating ecommerce identity fraud: A study of trustev's digital verification solution[END_REF]. Therefore, Company A may consider biometric authentication systems to prevent unauthorised access.

b) Information Communication Security (ICS)

ICS helps to secure electronic communication amongst staff, third parties and customers inside and outside the company. Company A encrypts the data of customers and employees when sending it to third parties and registered post is used to send hard copies of data. Using encryption for sending and receiving information is useful for a company because it prevents modification and keeps data in its original form. This encryption method has a weakness as it does not cover the recipient of any data, as this would need Point-to-Point Encryption (P2PE). Therefore, Company A could P2PE to secure its communication channels more effectively [START_REF] Mcgee | Corporate identity theft: A growing risk[END_REF]. Moreover, Public Key Infrastructure (PKI) digital signatures and Secure Sockets Layer (SSL) etc. are useful in preventing cybersecurity attacks [START_REF] Ray | Secure multi-purpose mobile-banking using elliptic curve cryptography[END_REF]. Therefore, the company may use these for enhancing cybersecurity infrastructure.

c) Threat Management

Threat management is an essential component of the organisation's security process. Businesses that depend on ICT remain under threat from cyber-criminals whose motive is to steal sensitive information by infiltrating systems in different ways. Now, it is the responsibility of the organisation to manage such risks cost-effectively and minimise threats to their information systems.

Company A uses various security controls to minimise threats to information system infrastructure. Additionally, the company has professionals who constantly try to manage the security threats. However, some vulnerabilities still exist in these security controls for which Company A should assess regularly to manage security threats. This is also proposed by some researchers such as Taylor (2016) [START_REF] Taylor | Mobile payment technologies in retail: A review of potential benefits and risks[END_REF].

d) Network Security

Network security has a significant role in the information system security of online retail organisations because cyber-criminals attack the network to steal information. Cyber intrusions and attacks occur when unauthorised access is gained to networks, including theft of users' sensitive data, online economic fraud, website destruction, web application attacks and system penetration. Attackers exploit operating system vulnerabilities in web browsers, services and configurations, platform vulnerabilities in web applications, network device vulnerabilities, policy and personnel vulnerabilities in unauthorised devices [START_REF] Zhao | Opportunities and threats: A security assessment of state e-government websites[END_REF]. Company A did face cyberattacks, such as denial of service attacks, because of weaknesses in the network. However, encryption, anti-virus, firewalls, Intrusion Detection Systems (IDS) are useful in preventing cyber-attacks [START_REF] Sen | A study on e-commerce security issues and solutions[END_REF]. Therefore, the company should configure its firewalls to prevent denial of service attacks on the network. Firewalls are useful in preventing unauthorised access to the network and in blocking unwanted traffic into the company's network [START_REF] Sen | A study on e-commerce security issues and solutions[END_REF]) [START_REF] Sen | A study on e-commerce security issues and solutions[END_REF]. A firewall is a shield that works against dangerous communications from disseminating across any network, either from the outside world into a local system, or from one part of a local network to another. It is a useful element in network access because it can prevent unauthorised access at the boundary of a network and infrastructure.

Network Monitoring

ICT enables business processes to operate electronically and provides facilities to conduct business activities more effectively and efficiently in the digital environment. The monitoring activities of ICT allow the company to detect vulnerabilities and respond to these appropriately by enhancing security controls. Despite such active monitoring systems, some information breaches still occur at Company A. Company A are monitoring activities inside the company and whenever unusual activity is found, they aim to resolve it immediately. The company also performs a test for vulnerabilities in its computing systems and try to fix it in the first instance. Further, the company has different teams whose work is to monitor the security processes of the company's infrastructure and prevent loopholes from being exploited. For network security, Unified Threat Management Systems (UTMS) provide more security to the network layer, hardware and software than standard detection methods because this is the combination of firewalls, pattern recognition and user authentication methods [START_REF] Kent | How South African SMEs address cyber security: The case of web server logs and intrusion detection[END_REF]. Therefore, Company A should use UTMS for effective security.

Conclusion

This paper presented the findings of the case study on cybersecurity readiness from a technical perspective, for instance, if ICT security and risk assessments are managed effectively; cybersecurity incidents in the organisation could be reduced. Therefore, the organisation should proactively asses their technical factors rather than only assessing these after an incident has occurred. A qualitative case study approach was adopted and 15 semistructured interviews were conducted for data collection. The interviews were analysed using thematic analysis.

The key areas of selected themes were discussed and confirmed by the managers and other staff members.

This research provides unique value through investigating the determinants of technical readiness. The study suggests that organisation readiness in the cybersecurity domain can be achieved by taking proactive measures such as ICT security and risk management. The results show that online organisations are lagging behind, especially in the effective implementation of up to date technical tools and measures. Although the case organisation has implemented some measures, there is still a need to explore their functionality within the organisational structure.

Like other studies, this research also has some limitations. Firstly, only a single case study was conducted in this research to evaluate the technical readiness. Secondly, only a qualitative case study approach was used for data collection. Therefore, there is a need to conduct multiple case studies to learn more about technical aspects of cybersecurity in other technical aspects, using both qualitative and quantitative methods. This study was conducted in the UK so repetition of this research in other countries would improve the research and extend results to compare and contrast the outcomes of online retailers.