Search results

Cybersecurity competitions can effectively develop skills, but engaging a wide learner spectrum is challenging. This study aims to investigate the perceptions of cybersecurity competitions among Reddit users. These users constitute a substantial demographic of young individuals, often participating in communities oriented towards college students or cybersecurity enthusiasts. The authors specifically focus on novice learners who showed an interest in cybersecurity but have not participated in competitions. By understanding their views and concerns, the authors aim to devise strategies to encourage their continuous involvement in cybersecurity learning. The Reddit platform provides unique access to this significant demographic, contributing to enhancing and diversifying the cybersecurity workforce.

The authors propose to mine Reddit posts for information about learners’ attitudes, interests and experiences with cybersecurity competitions. To mine Reddit posts, the authors developed a text mining approach that integrates computational text mining and qualitative content analysis techniques, and the authors discussed the advantages of the integrated approach.

The authors' text mining approach was successful in extracting the major themes from the collected posts. The authors found that motivated learners would want to form a strategic way to facilitate their learning. In addition, hope and fear collide, which exposes the learners’ interests and challenges.

The authors discussed the findings to provide education and training experts with a thorough understanding of novice learners, allowing them to engage them in the cybersecurity industry.

The purpose of this study is to give an informative map of sustainable architectural education by focusing on publishing trends, prominent publications, prolific contributors, research challenges and future research prospects. As a consequence, an efficient framework for collecting significant knowledge and identifying prevalent topics in sustainable architectural education towards more sustainable environments at the urban and building scales may be provided.

The methodology adopted in this study is based on examining the subjects of many consecutive rounds of the Passive and Low Energy Architecture (PLEA) conference, with an emphasis on the most recent five rounds. The PLEA's official website served as the main source for gathering all proceedings. Earlier rounds from 2016 to 2020, which cover a time span of five years, were utilized to analyze patterns during that time period. The rationale for selecting this time period was the limited availability of data required to observe the trends, area of interest and emerging themes in these fields that could be analyzed qualitatively.

The findings show that the following drive themes emerged around education and research-driven sustainable architecture: emerging design as a core idea; concentration on the way of delivering and developing education; constructing and shaping the building; assessing current practices; acquiring and recruiting knowledge and new concepts; reporting on the current condition; portraying the target audience and ensuring the long-term viability of the architectural educational process. This insight provides academics and professionals a greater grasp of the state of the art, allowing them to direct their research toward developing concerns in education and research-driven sustainable design.

The study results provide an effective framework for collecting relevant content and identifying prominent topics in sustainable, passive and passive low-energy architecture for the creation of more sustainable urban and building environments. Furthermore, this qualitative and exploratory study may serve as a framework for those researching, creating and verifying different research approaches in education and research-driven sustainable architecture.

The present study aimed to develop the competencies of 21st-century learners by considering the characteristics of the education element in the curricula.

The study employed a qualitative research design and a content analysis technique. The research population consisted of 20 curriculum design professors selected via a snowball sampling method until data saturation was reached. The research instrument was semistructured interviewing. The content validity of the interview questions was determined according to 5 curriculum design experts' opinions. Four credibility, transferability, dependability and confirmability criteria were used to increase the accuracy of qualitative data. The findings were analyzed using thematic analysis (structural-interpretive) through open, axial and selective coding.

Education characteristics in competency-based curricula were categorized into knowledge, skills, attitude and educational values. Knowledge includes pedagogical knowledge, content knowledge, pedagogical content knowledge and pedagogical technological knowledge; teaching skills include organization, facilitation, care and flexibility; educational attitudes consist of educational and pedagogical attitudes; and educational values include individual and group-social values.

The present research put three critical dimensions together: the competencies of the new-age learners from the perspective of the curriculum, which is the heart of the education process and is aimed at sustainable development, which is the priority of the countries today.

This study aims to identify specific factors that facilitate engagement and stay intention among Generation Z employees in the Indian banking, financial services and insurance (BFSI) context. Furthermore, using the frequency distribution of the identified factors, this study has ranked them in order of their association with stay intention.

Data were collected from 22 Gen Z employees working in the Indian private BFSI sector using unstructured interviews. Inductive content analysis was applied to identify the factors improving engagement and stay intention. Moreover, quantitative content analysis was applied to calculate the frequency distribution of the identified factors.

The study identified six prominent factors, namely, transformational leadership, employee investment practices, egalitarian practices, work-life balance, job crafting and sustainability, which significantly enhance employee engagement and stay intention among Gen Z employees. Moreover, based on the results of quantitative content analysis, it was found that transformational leadership exhibited the highest frequency in association with employee engagement and stay intention. Following this were employee involvement, egalitarian practices, work-life balance, job crafting and sustainability.

In the coming days, Generation Z will contribute to almost one-third of India’s workforce, of which the BFSI sector will be the major employer. However, the issue with this generation is their retention. Hence, the study identifies factors ensuring engagement and stay intention.

Owing to the paucity of research on stay intention as a variable of interest, this study tries to capture the perceptions of Gen Z towards factors inducing their engagement and stay intention. This study assesses intention to stay (ITS) as compared to intention to leave (ITL) as it is a proactive indicator of turnover. Lastly, this study uses a qualitative approach to identify factors influencing stay intention and engagement based on interactions with employees, which, to the best of the authors’ knowledge, no prior study has attempted.

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

Considering the swift adoption of innovative sustainability practices in businesses to accomplish sustainable development goals (SDGs), research on corporate sustainability has increased significantly over the years. This research intends to analyze the published literature, emphasizing the existing, emerging and future research directions on achieving the SDGs through corporate sustainability.

This research analyzed the growing trends in corporate sustainability by incorporating 2,038 Scopus articles published between 1999 and 2022 using latent Dirichlet allocation (LDA) topic modeling, bibliometrics and qualitative content analysis techniques. The bibliometric data were analyzed using performance and science mapping. Thereafter, topic modeling and content analysis uncovered the topics included under the corporate sustainability umbrella.

The findings indicate that investigation into corporate sustainability has considerably increased from 2015 to date. Additionally, the majority of studies on corporate sustainability are from the United States of America, the United Kingdom and Germany. Besides, the USA has the most collaboration in terms of co-authorship. S. Schaltegger was considered the most productive author. However, P. Bansal was ranked as the top author based on a co-citation analysis of authors. Further, bibliometric data were evaluated to analyze leading publications, journals and institutions. Besides, keyword co-occurrence analysis, topic modeling and content analysis highlighted the theoretical underpinnings and new patterns and provided directions for further research.

This study demonstrates various existing and emerging themes in corporate sustainability, which have various repercussions for academicians and organizations. This research also examines the lagging themes in the current domain.

This study aims to provide an extensive review of the employer brand literature by capturing research trends and proposing a research agenda.

A bibliometric analysis was conducted to study 232 peer-reviewed articles indexed on Scopus from 2004 to 2021. Content analysis is added where appropriate to further explore empirical studies and influential papers.

Based on bibliometric analysis, this study provides data about the volume, growth trajectory, geographic distribution, main authors, three main themes and future research avenues for each of these themes. Content analysis sheds light on research subjects, types of data, methods and most influential papers.

This paper is among the first to conduct a bibliometric analysis along with content analysis focusing on employer brand. An extensive research agenda derived from the studied literature is also provided for interested scholars.

Compared to their counterparts in developed economies, businesses established in emerging economies continuously struggle due to resource and time constraints. Open innovation (OI) allows these firms to bridge the gap and advance towards technological advancements; however, the scholarly knowledge on the subject is not systematized. Thus, this study synthesizes the extant literature, proposes a framework and highlights future research avenues for domain advancements.

Based on the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) framework, the study evaluates 79 journal publications from Scopus and Web of Science (WoS). The bibliometric analysis highlights annual publication trends and research clusters, whereas TCCM analysis provides deep analysis into applied theories, context and characteristics, i.e. OI–performance linkage, barriers, mediators and moderators, followed by employed methodologies in the domain.

The bibliometric results showcase a rising publication trend, significant research clusters and trends, whereas the content analysis via TCCM framework identifies theories, contexts and methodologies employed in the domain. In terms of characteristics, the OI–performance relation and barriers at diverse levels, followed by the moderating and mediating mechanism, are further explained in detail.

This is the first study to examine OI and firm performance in the context of emerging economies and employ rigorous frame-based bibliometric and content analysis measures, establishing the foundations for a comprehensive understanding.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.