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Intersectionality addresses complex avenues of oppression that emanate at the intersections of one’s identities. However, the intersectional framework assumes static identities, which are increasingly being acknowledged for their fluidity. This research explored the extent of the fluidity of social identities to draw implications for the application of the framework in research.

27 participants from a post-graduate elective course on diversity and inclusion identified their significant social identities, and submitted a write-up using hermeneutic phenomenology in which the participants shared their lived experiences of the fluidity of their social identities in different spaces they occupy or find themselves in.

Fluidity-triggering stimuli in different environments and their associations with identity-related motives were uncovered using thematic analysis. Stimuli operating at micro-, meso- and macro-levels rationally explained identity fluidity. However, in addition to types, intensity and frequency of stimuli, psychological factors, such as identity status, were decisive in determining the degree of generalization of stimuli across individuals and spaces that significantly influenced identity fluidity.

This research explored the extent of the fluidity of social identities to draw implications for the application of the intersectional framework in research. The findings contribute to future research by identifying limitations of the intersectional framework based on the fluidity of social identities arising from environmental stimuli that operate at micro-, meso- and macro-levels, and the extent of psychological generalization of these stimuli across spaces.

The purpose of this paper is to investigate the factors influencing the development of social media marketing strategy in an international context. We specifically look at the potential drivers and barriers throughout the social media marketing strategy development process and how cultural differences shape social media marketing strategy decision-making among firms in international markets.

The study is conducted with an inductive research approach involving in-depth interviews with 32 firms from Finland, China and Brazil. Using inductive data analysis, we identify both internal and external factors that drive and hinder the development of firms’ social media marketing strategies. Moreover, we explore the essential elements in social media marketing strategy development based on the key practices observed among these firms, which enables us to conduct a comparative analysis of how cultural values influence the development of social media marketing strategies.

Our findings underscore the importance of both internal (i.e. resources and capabilities) and external (i.e. market-level and country-level) factors that influence the development of social media marketing strategy. Our analysis also unveiled four key practices throughout the social media marketing strategy development process: social selling, content marketing, risk management and relationship management. Additionally, we identified three distinct mindsets regarding firms’ social media selling objectives across companies in the three countries.

The comparative approach provides novel insight into firms' international social media marketing strategy. Our proposed conceptual model shows the development process of social media marketing strategy in the international context. The research propositions highlight the role of cultural values and open up new avenues for future research.

The purpose of this study is to investigate the factors facilitating and influencing the adoption of DevOps practices specifically tailored to mobile software development, with a focus on understanding the influence of mobile-specific requirements on DevOps integration.

The study employs a qualitative methodology, including a literature review, exploratory case research and partial quantitative assessments through DORA metrics and survey applications. This approach, guided by the Technology-Organization-Environment (TOE) framework, prioritizes in-depth insights into the adoption of DevOps practices and explores strategies for integrating DevOps in mobile software development.

The research identifies several key themes specific to Mobile DevOps adoption, including tool integration issues, testing complexities, deployment challenges and security concerns. These findings underscore the necessity for tailored DevOps solutions that can effectively address the unique demands of mobile software development. The study also proposes actionable strategies to overcome these challenges, thereby enhancing the efficiency, quality and security of mobile applications.

The insights gained from this study provide valuable guidance for practitioners in the mobile software development sector. By understanding and addressing the specific challenges of Mobile DevOps, organizations can improve their DevOps practices and achieve better outcomes in terms of project delivery speed, quality and security. For example, implementing robust testing strategies, investing in compatible tools and developing well-defined rollback procedures can significantly enhance Mobile DevOps effectiveness. Furthermore, incorporating continuous security measures and improving cross-functional collaboration can lead to more secure and efficient mobile application deployments.

This study offers valuable starting points for researching Mobile DevOps in real-world settings, based on insights from practical DevOps implementations in a single-case organization. Organizations can use this information to compare their own DevOps approaches with those of the studied organization, and can facilitate self-assessment and improvement.

This study contributes to the limited literature on Mobile DevOps adoption and proposing actionable strategies. By incorporating the TOE framework, it provides a comprehensive guide that enhances understanding and management of DevOps practices throughout the mobile application development lifecycle and offers significant value to practitioners and researchers alike.

Nowadays, designing environmentally compatible buildings with acceptable performance in terms of cost, materials, and energy efficiency is considered crucial for developing sustainable cities. This research aims to identify and rank the most influential factors in the application of Building Information Modeling (BIM) systems in the smartification of green and sustainable buildings.

The present research is applied and descriptive. In this study, we identified the most influential factors in the application of Building Information Modeling (BIM) systems through library studies and expert opinions. Data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy VIKOR method was employed for analysis.

The results show that the most influential factors in the application of Building Information Modeling (BIM) systems in the Smartification of green and sustainable buildings, in order, are: “Energy saving and consumption reduction,” “Increased productivity and efficiency,” “Life-cycle assessment (LCA),” “Eco-friendly design,” “Integration with IoT and other technologies.”

In this study, while addressing the intersection of BIM technology, green building principles, and smart building objectives to optimize the performance of buildings during their life cycle, the most influential factors in the use of this system were ranked based on the criteria of “impact level,” “importance level,” and “availability of necessary tools” for implementation in Kerman. Moreover, solutions for more effectively utilizing this system in the smartification of green and intelligent buildings were proposed.

This study aims to propose iStage, i.e. an intelligent hybrid deep learning (DL)-based framework to determine the stage of the disaster to make the right decisions at the right time.

iStage acquires data from the Twitter platform and identifies the social media message as pre, during, post-disaster or irrelevant. To demonstrate the effectiveness of iStage, it is applied on cyclonic and COVID-19 disasters. The considered disaster data sets are cyclone Fani, cyclone Titli, cyclone Amphan, cyclone Nisarga and COVID-19.

The experimental results demonstrate that the iStage outperforms Long Short-Term Memory Network and Convolutional Neural Network models. The proposed approach returns the best possible solution among existing research studies considering different evaluation metrics – accuracy, precision, recall, f-score, the area under receiver operating characteristic curve and the area under precision-recall curve.

iStage is built using the hybrid architecture of DL models. It is effective in decision-making. The research study helps coordinate disaster activities in a more targeted and timely manner.

This systematic review aims to identify the key determinants of knowledge transfer (KT) activities among researchers.

This study systematically reviewed KT literature in academic settings from 1995–2023. The authors searched Web of Science and Scopus using predefined keywords, following PRISMA guidelines for screening and eligibility assessment. From 158 selected articles, the authors extracted data and conducted a descriptive analysis to map KT activities’ evolution. A narrative synthesis approach categorized determinants of researchers’ KT activities.

The systematic review findings revealed a general conceptual framework that categorizes the identified determinants of KT into four categories. At the individual level, the factors are related to the sociodemographic characteristics of the researcher (e.g. gender, age, experience), their psychological aspects (e.g. attitude, intrinsic motivation, intention) and personal characteristics (e.g. self-efficacy, communication skills). At the research team level, leadership style and team dynamics. At the organizational level, the findings emphasize university characteristics (e.g. size, structure and ranking), KT culture installed and university resources. At the inter-organizational level, the key determinants were funding sources, network strength and trust.

The studies included in our database were different in terms of contexts, country of the study, the disciplines of KT and the types of KT activities examined. This variety restricts the direct comparison of research findings thus the generalizability of our conclusions. Future research should focus on specific contexts, disciplines, countries or types of KT activities to provide generalizable findings.

A better understanding of all the factors influencing KT among university researchers is essential for several reasons. First, it will enable the government to develop effective policies to promote KT ecosystems. Second, universities can create strategies, policies and programs to support researchers’ engagement in KT activities. Finally, researchers can be more strategic in their KT efforts.

This systematic review contributes to the literature by providing a comprehensive conceptual framework that identifies KT determinants at different levels and fills a gap in the existing literature that only addresses specific aspects of KT determinants. This framework can be a theoretical reference for future empirical studies. Furthermore, it practically provides recommendations for different actors including, government, universities and researchers.

Security in large-scale agile is a crucial aspect that should be carefully addressed to ensure the protection of sensitive data, systems and user privacy. This study aims to identify and characterize the security practices that can be applied in managing large-scale agile projects.

A qualitative study is carried out through 18 interviews with 6 software development companies based in Portugal. Professionals who play the roles of Product Owner, Scrum Master and Scrum Member were interviewed. A thematic analysis was applied to identify deductive and inductive security practices.

The findings identified a total of 15 security practices, of which 8 are deductive themes and 7 are inductive. Most common security practices in large-scale agile include penetration testing, sensitive data management, automated testing, threat modeling and the implementation of a DevSecOps approach.

The results of this study extend the knowledge about large-scale security practices and offer relevant practical contributions for organizations that are migrating to large-scale agile environments. By incorporating security practices at every stage of the agile development lifecycle and fostering a security-conscious culture, organizations can effectively address security challenges in large-scale agile environments.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

In socio-technical transition theory, resistance by existing technology and regime resistance plays a key role. The resistance is in the form of intentional improvements; eventually, the regime destabilizes and adopts the new technology, referred to as the sailing-ship effect. Researchers used a structural view and examined it as a strategic action and its relationship with new technology (competitive/symbiotic) in non-fast-changing sailing systems. This study uses a microlevel view and examines it in a fast-changing where products/services are developed by integrating existing technology with new product innovations; their success depends on addressing technical/market uncertainty. This study examines the sailing-ship effect in a fast-changing system and contributes to the socio-technical transition theory.

The authors need to examine the phenomena of the sailing-ship effect in its setting, and a case-study method is appropriate. The selected case provided diverse analytic and heuristic perspectives to examine the phenomena; therefore, it was a single case study.

In an IT scenario, the strategic actions decide and realize agility and competitive advantage by formulating appropriate goals with required budgets and coevolutionary changes to resources at product, process and organizational levels, addressing technical/market uncertainty. Moreover, the agility displayed by strategic actions determines the relationship with new technology, which is interspersed. Finally, it provided insights into struggle, navigation and negotiations, forming strategic actions to display the sailing-ship effect.

The study selected a Banking Financial Services and Insurance product of an IT Services company. As start-ups exhibit inherent (emergent) agility, the authors can examine agility as a combination of emergent and strategic actions by selecting a start-up.

The study highlights the strategic actions specific to an IT services company. It developed its product and services by steering clear from IT innovations such as native cloud and continuous deployment. It improved its products/services with necessary organizational changes and achieved the desired agility and competitive advantage. Therefore, organizations devise appropriate strategic actions to combat the sailing-ship effect apart from setting goals and selecting IT innovations.

The study expands the socio-technical transition theory by selecting a fast-changing system. It provided insights into the relationship between existing and new technology and the strategic actions necessary to manage technical and market uncertainty and achieve the desired competitive advantage, or the sailing-ship effect.

The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection.

“Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review.

Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news.

The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.