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While cigarette use has been addressed as a global health problem, most smokers still find difficulties and barriers in accessing health facilities or finding a suitable method to quitting the habit. This paper will discuss the study exploring a self-help group on an online-based platform, namely Facebook, in Indonesia. This study adopted quasi-experimental, with pre and post-test using the control group as the previous ethical clearance. The respondents consisting of thirty-two smokers were recruited on non-probability sampling with the purposive sampling method. The data were collected by using a valid (r=0,666) and reliable (r=0,846) Richmond Test motivation questionnaire. Wilcoxon and Mann-Whitney tests were used to analyse them further. The study revealed low participation on the online platform with discussion based on a self-help group. There was no significant motivation improvement among the experimental group after the intervention, as well as no significant difference in motivation improvement between the control and the experimental group upon pursuing the program (p>0,05, t=0,058). In terms of low involvement, an online platform was less effective in improving quit-smoking motivation among smokers. Further research is needed to develop more interactive and longer interventions to facilitate cost-effective smoking cessation through social media. [ABSTRACT FROM AUTHOR]

Today, the first thing that worries cyber unlawful acts when we think about cybersecurity, is becoming more prevalent every day. Many different strategies are being used by most governments and service providers to prevent cybercrime. Cybersecurity is still a key concern for many despite several remedies. The issues faced in the realm of cybersecurity using the most cutting-edge technologies are the primary topic of this study. Additionally, it emphasizes the most recent cybersecurity ideologies, practices, and trends that are altering the definition of cybersecurity. The numerous types of cybercrime are discussed in this study, including the significance of social media in cybersecurity. [ABSTRACT FROM AUTHOR]

Social networking application platforms are experiencing considerable development in equally volume and principle. A detailed characteristic of Social networking application platforms is emotion investigation, whereby a person's emotions and feelings know how to be incidental from their posted text. Study connected to emotion examination is getting hold of important attention since it is a excellent organizer that be capable of progress client familiarity and make available innumerable modified services. Twitter is one of the largely accepted Social networking application platforms that encompass consumers commencing dissimilar counties by means of diverse traditions and verbal communications. It gives precious information for miscellaneous and huge quantity of information that is capable to be utilized to get better judgment building. In this document, the emotion direction of documented characteristics and emoji-based mechanisms is deliberated aiming "tweets" and observations posted in Arabic on Twitter for the duration of the 2018 World Cup occasion. The learning furthermore calculated the significance of analyzing texts together with or exclusive of emojis. Information was get hold of on or after thousands of tweets composed to discover emotion examination outcomes for texts and emojis independently. The outcomes demonstrate that emojis maintain the emotion direction of texts along with those texts or emojis balance each other to communicate proposed significance and consequently cannot offer dependable information independently. [ABSTRACT FROM AUTHOR]

Expecting sadness be capable of alleviate catastrophe. Many mechanisms encompass been projected up to now by means of Machine-Learning (ML) methods. The aspire of this learning is to recognize significant changeable utilized in sadness forecast, modern sadness screening implements accepted, and the most modern ML methods exercised. These concerned supplies investigators by means of the primary modules necessary to forecast on sadness. Social media channels, such as Facebook, Twitter, and Instagram, have distorted our humankind everlastingly. Public are currently ever more associated than increasingly and make known a kind of digital character. Even though communal medium positively has numerous extraordinary features, the demerits are indisputable additionally. Modern learning has designated an association connecting far above the ground practice of social media platforms and amplified sadness. The current learning aspire to develop ML methods for distinguish a feasible unhappy Twitter customer based on together, his/her system activities and tweets. For this reason, we skilled and experienced classifiers to differentiate whether a customer is unhappy or not utilizeing characteristics take ousted from his/her behavior in the system and tweets. The consequences demonstrated that the additional characteristics are utilized; the advanced are the accurateness and F-measure achieves in become aware of discouraged customers. This technique is a information ambitious, extrapolative come within attain of for near the beginning discovery of sadness or additional psychological sickness. This learning's major role is the examination division of the characteristics and its collision on notice the sadness stage. [ABSTRACT FROM AUTHOR]

The power of social media in the age of information technology, including Facebook, Twitter, Instagram, and other digital products, has drastically altered how people interact with one another. The creation of a digital public space that allows all individuals, groups, or social communities to interact freely and without restriction as well as expands opportunities for the achievement of goals based on interests such as political, economic, cultural, and ethnic goals also increased the likelihood of social conflicts with more complicated histories. This essay aims to investigate the role social media plays in the dissemination of racism and hate speech, as well as the variables that contribute to these phenomena. Many aspects of the context are impacted by this event. As an illustration, consider sports trolls and hate speech, cyber bullying or online racism, and racism against people or groups based on their religion, culture, or traditions. Posting hate speech, sexism, and racism (write suitably). [ABSTRACT FROM AUTHOR]

Wildlife trading that involves insects as commodity has shifted into using online trading system to engage sellers with their potential buyers. Facebook, aside from being social media platform for daily activity, has been used as the trading medium for various wildlife species. In this paper, we report the preliminary monitoring on the trading of two popular Coleoptera families, horned beetles (Scarabaeidae) and stag beetles (Lucanidae) using Facebook as medium. The study used internet survey by specifically look for groups, pages and personal accounts in Facebook that actively involve with the two beetle families. There are six Facebook groups that dedicate their activities mainly on horned beetles and stag beetles, with two of them housing more than thousands of members and regular posts or adverts. Three are only three Facebook pages found with two of them currently have hundreds to thousands of followers. Meanwhile, there were 90 personal accounts named as 'kumbang tanduk' or Indonesian name for the beetles of interest with possible 20 accounts that involve with either or both horned beetles and stag beetles. This study provides proof on the extensive use of internet for wildlife trading, which previously thought only dominated by the trading of high-profile taxa like mammals, birds or reptiles. If combined with surveying other social media platforms, this online monitoring can be a reliable way to quantify the trading volume for the beetles and other insect species that essential to establish their conservation status. [ABSTRACT FROM AUTHOR]

The wildlife trading is the main driving factor that endangers the existence of wild species, in addition to potentially spread diseases and extend invasive species distribution. The nowadays perpetrators of wildlife trading take advantage of the advancement of communication technology, using online social media platforms as transaction media. In this paper, we report a monitoring on online insect trading activity commenced in a Facebook group between March to April 2022. Trading volume, traded insect taxa, role of trading perpetrators and locality of traded insects are among aspects monitored in this study. We recorded 236 posts during monitoring period with average 3.87±2.31 posts daily. These number was posted by 180 account holders that further defined as curious one-timer (126 accounts), occasional provider (33 accounts) and serious breeder/hunter/seller (21 accounts). There were more than 20 insect families offered during this period, with the members of Scarabaeidae (118 posts, 50.0%), Lucanidae (44 posts, 18.6%), Cerambycidae (17 posts, 7.2%) and Buprestidae (15 posts, 6.4%) became the most significant ones. Xylotrupes gideon (Lucanidae), Chalcosoma spp (Scarabaeidae) and Castiarina spp (Buprestidae) seemed to be adverted the most. West Java Province contributed the most in this group with 60 posts advertised 11 insect families, more than twice over what recorded from East Java (29 posts;>5 families), Central Java (21 posts;>8 families), Jambi (11 posts;>4 families) and Jakarta (10 posts; 4 families). This paper, despite covers only two months of online insect trading and on one single Facebook group, indicates a potential threat for the existence of Indonesian insects. Therefore, further systematic online survey is needed to assess the actual volume of online insect trading. [ABSTRACT FROM AUTHOR]

Preventing dental and oral issues in children involves enhancing their knowledge through health promotion, with early education serving to instill understanding and encourage proper dental health practices. Health education can be delivered through various media, including the popular social platform WhatsApp. This study aims to assess the effectiveness of dental health education delivered through WhatsApp video calls among children aged 8-10. The research employed a quasi-experimental design, specifically a one-group pretest-posttest design, and the target population consisted of children aged 8-10 in RW 08 Jetis Village, Sukoharjo, Central Java, Indonesia. Participants were selected through a total sampling technique based on specific criteria. Those meeting the criteria were designated as respondents and underwent pretests followed by WhatsApp video call education sessions and posttests. The data were analyzed using the Wilcoxon test, with results indicating a significant effect (p < 0.05), signifying the effectiveness of WhatsApp video call education on the dental health knowledge of children aged 8-10. [ABSTRACT FROM AUTHOR]

In Today's internet world, detecting Fake News is facing a difficult task. Data integrity has been of maximum importance in the current news era due to the extensive range of free text. Therefore, generating and developing fraudulent information is more painless than ever. The spreading of such fake news influences society from diverse inappropriate views. Online Social Networks has become increasingly popular to obtain news and information on the internet due to their widespread uses. However, false information has been spread widely due to the internet's rapid growth. As a result, detecting fabricated information is critical in our present culture. As a resolution, in this paper, a Deep learning approach is suggested to confirm the trustworthiness of news. It is also proposed as a Multimodal faked information detection method that utilizes both the textual and graphical content of the data to determine its authenticity. The first step is to pretrain the BERT model to achieve the textual feature representation vector. As a second step, pretraining the VGG-19 model generates a visual characteristic representation vector. Two MCBP (Multimodal Compact Bilinear Pooling) modules have been created based on the proposed methodology. Graphic feature representation vectors with alerts are obtained using the first MCBP module. MCBP's second module combines the visual feature with the attention process and the vector of textual elements. With a second MCBP module, the attention process and a textual feature vector are utilized to input visual quality. You may be able to exploit the linked vector to your advantage in detecting fake information. In this article, a comparison is performed between the proposed method and two standard methods. EANN and Spot Fake approaches have been compared in this work, and it has been found that the proposed strategy in this article outperforms both in terms of accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

This paper studies Health of individuals, an ever needed, relevant, fertile and in addition an interesting study for a human being. It is a study of the health status of individuals using machine learning models-logistic regression, Decision Tree and Neural Networks-by questionnaire-based data collection and found the factors which are major contributing aspects towards an individual's health and lifestyle. The questionnaire used is a simple one which is shared to friends, colleagues and professional acquaintances through social media. The collected data is analyzed using Machine Learning models – Logistic regression, Decision tree and Neural Network – and found factors which are contributing in a major way towards an individual's mental as well as physical health in the long run. The analysis is performed using R platform. [ABSTRACT FROM AUTHOR]

The detection of the community structure in the networks has been the important issue in many domains and disciplines. Especially, in the context of social network, community structure drew attention after the increasing popularity of social media services like Facebook, Twitter, Instagram, LinkedIn, etc. Accordingly, the need to discover these communities that share many features has appeared, and this problem was commonly called community detection problem. To address these issues within the communities, this paper proposes to use similarity between nodes attributes as a weight in the doubly-weighted ℓ1-norm k-medoids procedure, adopted in the convexified modularity maximization (CMM) approach for degree-corrected stochastic block models. The new weight, common neighbors between nodes, is motivated by the idea of "the friend of my friend is likely to be my friend," which is frequently utilized in discussions concerning social networking. Numerically, CMM, with this new weight, is applied to different networks; but it works particularly well in social networks due to its special structure. Experimental results show that the CMM approach improves by increasing the modularity measure, leading to better partitioning. [ABSTRACT FROM AUTHOR]

Online social media platforms such as YouTube have grown in popularity in recent years, providing as critical routes for content creators, advertisers, and platform administrators to access a large worldwide audience. Accurate video popularity prediction is critical for improving content strategy, increasing user engagement, and increasing video visibility. This study intends to improve video popularity prediction accuracy by presenting a fresh methodology that goes beyond traditional analysis. To construct a more effective prediction model, the suggested method combines examining metadata, such as title, description, and channel title, with thumbnail analysis. The project aims to dramatically increase prediction accuracy by extracting relevant information from both metadata and thumbnails. To do this, multiple classification techniques, including Support Vector Machine, Gradient Boosting, Random Forest, Extreme Gradient Boosting, and K-Nearest Neighbors, were used to train and forecast the model. Notably, when it came to feature extraction, the Gradient Boosting and Random Forest algorithms achieved amazing accuracy of 97% when using all extracted and original data, and 82% and 92% when using only extracted features. In comparison, previous studies employing simply extracted features obtained only 74% accuracy. These findings demonstrate the potential benefit of integrating metadata and thumbnail data research to get insights into the elements that drive video popularity and lead to more accurate predictions. [ABSTRACT FROM AUTHOR]

Depression is a mental health disorder that affects a significant portion of the population. According to recent statistics, an estimated 3.8% of the population experiences depression, including 5% of adults. The traditional methods of detecting depression, such as face-to-face interviews, have limitations and are not always effective. However, social media sentiment analysis has the potential to identify people at risk of depression. In this essay, we will discuss the prevalence of depression in society, the limitations of traditional methods for detecting depression, the potential of social media for depression detection, the challenges of analyzing social media sentiment for depression detection, and the effectiveness of social media sentiment analysis for depression detection. [ABSTRACT FROM AUTHOR]

The goal of this research is to catalogue all of the undesirable materials available on rival and popular social media platforms. Tools and methods: The dataset for training and testing the proposed prediction models was constructed using the messages from a number of popular social media messaging platforms, with a minimum of 5 attributes and 150 messages. Random Forest, a machine learning algorithm, was used to design the framework, and it was compared favourably to the Support Vector Machine algorithm. Discussion and Remarks: Accuracy in retrieval is 90.30 percent for the Random forest algorithm and 94.36 percent for the Support vector machine learning algorithm (countvectorizer). The significance level between the two algorithms is high at p=0.02 (p0.05). This study confirms that the SVM (countvectorizer) Machine Learning algorithm has a higher accuracy rate than the Random Forest algorithm and creates a novel framework to identify spam words. [ABSTRACT FROM AUTHOR]

Social media contains robust source information of people's viewpoints because everyone can give their opinion independently. Due to various types of social media, there are different methods for scrapping data because different social media have different regulations. This study aims to build sentiment analysis with data sources in Bahasa Indonesia (Indonesian Language) from various social media platforms such as Twitter, Facebook, and Tiktok. A case study from this research regarding health workers' services for the community on social media with the Support Vector Machine (SVM) classification algorithm. This study used the hashtag #Tenagamedis (in English: health workers). The methodology of this study contains data collecting, filtering, pre-processing, data labeling, and classification. The crawling data get a total of 4223 comments with a composition of 617 from Twitter, 1650 from Facebook, and 2001 from Tiktok. The data from Tiktok get the best accuracy which is 91%. Based on the test results, the amount of data and the balance of the training data determine the classification accuracy. [ABSTRACT FROM AUTHOR]

One of the most issues that is currently being discussed is to minimize the spreading of Covid-19 around the globe. Indonesian government tries its best to deliver the message on how to live a healthy live in order to minimize the spreading of the virus. Using social media and websites were some ways taken to educate Indonesian people. The research was using qualitative method, content analyzes technique from five literatures chosen randomly from websites. The result showed that Indonesian government has done a great number of campaigns to educate all level of society, from lower education to higher one. It is expected that all level of societies could develop awareness to create a new habit of healthy life to minimize the spreading of Covid-19. The result of this research will then be used in the next research as a comparison to the social media campaign done in Pakistan. [ABSTRACT FROM AUTHOR]

The ubiquity and simplicity of the use of SNS have changed the creation and dissemination of knowledge in today's society. Easy access to information through SNS, on the other hand, does not guarantee a greater degree of public awareness. Unlike conventional media outlets, social media platforms enable the propagation of disinformation and misinformation at a quicker and wider rate, notably that media users may choose to distribute information without regard for its veracity. In the context of fake news, this study adds to our knowledge about information verification practices among SNS users. We build a news verification behavior study model and test seven hypotheses using a survey of active 211 SNS users, all foreign students residing in South Korea. This paper concluded that information verification behaviors among SNS foreign users are influenced by views of news sharers' networks (social connection variety, and trust), perceptions of news creators (fake news awareness and perceived media trustworthiness), and inherent intents to share. These findings have implications for possible strategies in countering disinformation in social media. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic has changed people's lifestyle. Many services are facing a drastic change and have shifted to using services online. This is also apparent in the medium of communication among the society. People are more likely to communicate through the media, especially social media than face-to-face communication today. In line with this, Facebook has become one of the notification channels to convey information and advice to the public either using explicit or implicit techniques. Hence, this paperwork is produced to analyse the use of implicature in Facebook postings by the Health Education Unit, District Health Office Kota Setar during the implementation of Conditional Movement Control Order (CMCO). The study of this text analysis uses the implicature framework and is adapted to the context of Malay society [1-2]. The findings showed that the authors of this social site used convoluted methods in conveying information and advice to readers using a relaxed language style so as not to provoke the community, thus the advice was acceptable to the readers of the postings. [ABSTRACT FROM AUTHOR]

Young Muslim generation members are living in a challenging world due to modernisation and exposure to foreign fashion trends, such as social media platforms. Regardless of following the religious values, these young members have the tendency to try new fashion trends, which has led them to look for inspiration who can provide them with the proper fashion guidelines. However, Muslim celebrities nowadays tend to apply modesty in their style and have attracted their followers. Therefore, this study explores the influences of the Hijrah Muslim celebrities towards young Muslim fashion consumers. This study employed a qualitative approach by conducting interviews with fifteen young Muslim women respondents. Findings show that Hijrah Muslim celebrities have impacted young Muslims to stay fashionable yet still follow religious values and have become one of the main ways to guide them on the correct path as a Muslim. [ABSTRACT FROM AUTHOR]

After almost 2 years since the first COVID-19 case outbreak, Malaysia had finally received the COVID-19 vaccines. There are several brands of vaccines from various manufacturers that have been tested and approved by the National Pharmaceutical Regulatory Agency (NPRA). This study is conducted to identify the level of public's awareness and acceptance towards the COVID-19 vaccine. 44 online survey questions were prepared including questions related to demographic background. The Google Form survey was disseminated to family and friends via social media platforms such as WhatsApp, Twitter and Instagram. A number of 405 respondents had answered the survey resulting in a positive result on the awareness (M = 3.60) and acceptance (M = 3.58) of the public towards COVID-19 vaccine. [ABSTRACT FROM AUTHOR]

The four language skills, listening, speaking, reading and writing, are the most vital part to be mastered by ESL or EFL learners. However, speaking is to be said the most anxiety-provoking for the learners. Past studies on the use of Instagram agreed on using this platform as a potential platform to help students learn the English language. This study reviews the literature on using the Instagram application to support second language learners to learn English. As social media applications have become popular platforms of interactions among users, optimising these applications, such as Instagram, into pedagogy is a great way to assist learners in mastering the skills of listening, speaking, reading and writing. Although relatively new, Instagram has gained billions of users worldwide. It appeals to younger generations, making it a platform with high potential to be integrated into the teaching of English as a second language in Malaysia. This review determines to investigate whether the integration of Instagram during English language classrooms is effective in helping students to lower their anxiety and perform better using the language. Past researches mainly focused on the primary and tertiary levels, and none has yet been done in secondary schools in Malaysia, thus creating a gap to be filled. A mixed-method study will later be conducted in a secondary school involving two Form 4 classes consisting of pupils whose language proficiency is intermediate. The experimental group will be exposed to weekly Instagram sessions over a time frame. In contrast, the control group will be taught with the traditional approach. At the end of the sessions, data will be collected via pre-test, post-tests, interviews, and questionnaires on students' anxiety level, which will be analysed descriptively. The study's findings are hoped to match the reviews' suggestions that Instagram positively impacts learning the English language among learners of all age groups. The study aims to provide insights for integrating social media applications in the English language classroom curriculum for secondary schools so that learners of all ages will benefit from it. [ABSTRACT FROM AUTHOR]

Twitter is one of the social media that can see written expressions or tweets from users, both praise tweets or tweets that contain hate speech. From the tweet review, the dataset can be used as research, namely sentiment analysis. In this study, the dataset used was a dataset from previous studies. Currently, the development of Deep Learning is very rapid. One method of Deep Learning is Bidirectional Long Short Term Memory (BiLSTM). BiLSTM is a development method from Long Short Term Memory, called "Bidirectional" because the structure processes data sequences back and forth. This bidirectional is in the RNN and LSTM methods. This study aims to carry out a classification process on Twitter review data whose sentiment classes consist of positive, neutral, and negative using the BiLSTM method. After doing the research, the BiLSTM method obtained an accuracy of 58.56%. The accuracy is obtained from the hyperparameter tuning value. Several parameters produce optimal accuracy, namely word2vec CBOW, number of neurons 200, learning rate 0.00001, number of epochs 25, and softmax activation function. Then, with this parameter value, the accuracy is 58.56%. [ABSTRACT FROM AUTHOR]

Social media users are currently increasing and it is estimated that there will be up to 2.77 billion social media users worldwide. The information found on social media is in the form of videos, photos, and audio, so many kinds of raw data are found on social media it turns out that this raw data can be improved using technology, especially in machine learning on artificial intelligence with this technology enabling data to be processed and turn that raw data into useful information. LSTM added with layer attention can increase accuracy because the role of attention in addition to the LSTM method is to focus the model on an important part of the input. The dataset in this study is 10806 review tweets divided into 3 sentiment classes namely positive, neutral and negative. The parameters used in this study are word2vec with CBOW architecture, the number of neurons is 200, the learning rate is 0.00001, the number of epochs is 25, and the softmax activation function. From these parameters produce the best model with an accuracy value of 60.20%. [ABSTRACT FROM AUTHOR]

The Senior High School Alumni Association from SMA N 1 Tegal, abbreviated as IKASMA, is a platform for alumni of SMA N 1 Tegal who want to keep in touch year after year. Adise from being a place to meet new people, it's also a place to make friends. IKASMA also provides project that can provide continuity of communication as well as contribute in a variety of disciplines, such as participating in the government's greening initiative, the program to build a prayer room for SMA N 1 Tegal, and a variety or other programs. IKASMA will be managed for three years, after which a large deliberation or MUBES will be held to elect the General Chairperson of IKASMA and its management membership. IKASMA, which had previosly primarity communicated through social media (Facebook, Instagram, and its website), held annual face-to-face meetings that were not adequately recorded. As a result, data collecting media and medium for communication between alumni and administrators are required. IKASMA apps based Android may be the best option for collecting data on alumni members from all class years and performing MUBES in 2021. This MUBES includes graduates from alumni SMA N 1 Tegal from all years, and it is the first time election of general chairman IKASMA is hosted online utilizing the Android-base IKASMA apps. There will be numerous home features in this IKASMA application, including Discount, About IKASMA, Information IKASMA's activities, and accounts. Each IKASMA member will receive an email addrees and a have an e-card with barcode, as well as the ability to promote sales through the discount menu. On the MUBES menu, you can elected of general chairman online. The IKASMA election general chairman based on Android is extremely suitable to be executed during this pandemic era. This study will be carry out categorization of voter using Fuzzy mamdani. [ABSTRACT FROM AUTHOR]

This study aims to determine the level of media feasibility, implementation of learning activities, increasing mastery of concepts, knowing student responses to the use of social media telegram Sound Wave App. The research method used is Research and Development (R&D) through research procedures using the ADDIE development model with the type of one group pretest posttest design. The subjects of this research were 32 students of class XI MIPA 1 SMAN 26 Bandung. The instruments used are expert validation sheets, observation sheets, concept mastery tests and student response questionnaires. The results showed that the social media telegram Sound Wave App with a percentage of 94.15% was very feasible to use in learning physics in schools on sound wave material, the implementation of learning on sound waves using social media telegram Sound Wave App got a percentage of 74% with good categories. The results N-gain calculation obtained an increase of 0.66 with a moderate interpretation which indicates the category there is an increase. Based on the N-gain data, hypothesis testing was carried out using a t-test to obtain a value of (11.15)>(0.037), this indicates that there is an increase in the mastery of the concepts of students who use social media telegram Sound Wave App. [ABSTRACT FROM AUTHOR]

The number of women working as engineers in the mechanical and plant engineering sector in Germany has risen to eleven percent in 2023 - remaining at humble levels. In higher positions, the share is even lower. While around 20 % of engineering students at German universities are female, these women do not seem to be entering the sector or they are leaving it again. The question arises as to what companies in the mechanical and plant engineering sector can do to attract female engineers after graduation. This study was conducted in cooperation with the German Engineering Association. The aim was to qualitatively explore the reasons why women enter, stay or leave the engineering sector and to include the industry's perspective to obtain a holistic view of the situation. Therefore, on the one hand the perspective of female engineers was investigated and, on the other hand, combined with the perspective of companies. We provide insights from focus groups and interviews with 49 female engineers across all career levels, from an analysis of 90 online company websites and social media pages as well as from three on-site visits at exemplary companies. The findings point to unresolved issues in recruitment and beyond: highlighting role models of successful women in engineering, the need for companies to actively attract and support women in engineering, e. g. through recruiting strategies focusing on women, insights into the working culture, and transparency regarding career options. Overall, the study suggests that companies should embed gender diversity and group specific recruitment as important issues in their organizations. Consequently, the study provides recommendations for action for companies seeking to become a more inclusive and diverse industry. Limitations are discussed and further implications are presented. [ABSTRACT FROM AUTHOR]