International Journal of Information Management Data Insights最新文献

While many digital technologies provide opportunities for creating business models that impact sustainability, some technologies, especially blockchain applications, are often criticized for harming the environment, e.g. due to high energy demand. In our study, we present a novel approach to identifying sustainability-focused blockchain companies and relate their level of engagement to location factors and entrepreneurial ecosystem embeddedness. For this, we use a large-scale web scraping approach to analyze the textual content and hyperlink networks of all US companies from their websites. Our results show that blockchain remains a niche technology, with its use communicated by about 0.6% of US companies. However, the proportion of blockchain companies that are committed to sustainability is significantly higher than in the overall firm population. Additionally, we find that such sustainability-engaged blockchain companies have, at least quantitatively, a more intensive embedding in entrepreneurial ecosystems, while infrastructural and socio-economic location factors hardly play a role.

The rapid evolution of mobile health applications (mHealth apps) has become increasingly important in enhancing healthcare delivery, especially during the COVID-19 pandemic. Despite the critical role of such technologies, however, acceptance and adoption rates among physicians in developing countries, particularly Saudi Arabia, have been relatively low. This highlights the need to explore the determinants of acceptance. In response to this call, this study aimed to identify the factors that influence Saudi physicians’ acceptance and adoption of mHealth apps during the COVID-19 pandemic using the unified theory of acceptance and use of technology. Data were collected using an online survey, after which the responses were analyzed via structural equation modeling. The analysis assessed the influence of four primary constructs, namely, performance expectancy, effort expectancy, social influence, and facilitating conditions, on the physicians’ behavioral intention to adopt these technologies. The results indicated that while all factors significantly affected the intention to adopt the apps, facilitating conditions were the most influential. These findings punctuate the necessity of investing in infrastructure and implementing training programs focused on integrating mHealth technology into medical practice. By drawing attention to influencing factors, this research provides critical insights for policymakers and healthcare managers to enhance the adoption of mHealth apps. This enhancement, in turn, can help improve healthcare delivery and patient outcomes during and beyond health crises. Finally, this study not only sheds light on the adoption dynamics prevalent in a developing context but also serves as a valuable guide for implementing similar technologies in other global regions.

The aim of this paper was to analyse the current applications of Artificial Intelligence in professional development and talent management within the corporate world with a focus on corporate training. By means of a Systematic Literature Review based on the PRISMA 2020 reporting criteria this paper highlights the current applications of AI along with the main benefits and drawbacks associated with its implementation. The findings show that AI is being used to enhance recruitment processes, to identify individual training and development skills and needs, to develop personalised training paths, to retain talent and predict attrition, and to detect future workforce skills development needs. It has been outlined that there is a need for automated talent management processes within companies and that talent intelligence should be implemented along with facing the challenges this will entail, such as minimising the risk of bias and hiring high-skilled qualified personnel.

Depression has become a major mental health problem in Thailand and can lead to suicidal ideation. As suicidal ideation may vary in intensity and lead to suicide attempts, early detection of suicidal ideation severity should be implemented. This research presents text classification models for the prediction of suicidal ideation severity. A dataset of Twitter messages in Thai was used to develop several classification models. A web application prototype was also developed to predict suicidal ideation severity and introduce self-therapy based on Cognitive Behavioral Therapy to its users for managing negative automatic thoughts. The application prototype received satisfactory feedback during the user experience assessment. The results of this research highlight the importance and need for socio-technical systems to help with early suicidal ideation detection and early therapy in the social environment where mental health support is inadequate.

The rapid evolution of technology has fundamentally transformed business operations. Therefore, companies are increasingly leveraging technology to enhance their processes and gain a competitive edge. In this context, the adoption of artificial intelligence (AI) in e-commerce has become a crucial area for business development. However, there is currently a lack of understanding regarding the key factors that determine the adoption of AI in e-commerce by small and medium-sized enterprises. Thus, to fill this gap, this study aims to investigate the factors influencing the adoption of AI tools in e-commerce for SMEs. This study will also explore how the adoption of AI by SMEs contributes to the business performance of these organizations. To achieve this, the study proposes an integrated model based on the dynamic capabilities framework, entrepreneurial orientation, and customer-centric systems. Empirical data for the current study were collected using a digital survey, which was disseminated to a purposive sample of SMEs in Saudi Arabia. Analysis of the collected data was performed using structural equation modeling (SEM), and the results support the role of both dynamic capabilities and entrepreneurial orientation in facilitating the adoption of AI in e-commerce. The study confirms of the significant role of AI adoption in enhancing the business performance of SMEs. This study seeks to make several theoretical contributions and implications for practice. This will also provide small and medium-sized companies with valuable insights that help in making decisions and building strategies. However, it is important to acknowledge the limitations of this study, which will be discussed later in the paper.

Many healthcare organisations have extensive documentation detailing the processes behind their various research and innovation projects. Analysing this data can provide valuable insights into why some projects succeed without major issues, others encounter and overcome problems, and some ultimately fail. This study introduces an approach that combines narrative interviews and Natural Language Processing (NLP) to identify patterns associated with innovation project outcomes. We analysed 618 documents from 67 projects provided by ZonMw, a major Dutch healthcare research funder, and conducted 32 narrative interviews across seven cases of healthcare innovation projects. By using narrative interviews to inform and pre-train a text embedding model, we demonstrate the potential to create a proxy for human judgement, allowing for a more natural identification of contextual patterns in project documentation. The findings indicate that successful projects are more likely to adopt a proactive approach to role changes and uncertainty (due to ambiguous laws and regulations) and to allow flexibility, which enhances stakeholder engagement, compared to failed projects. However, while we were able to conduct descriptive analysis to gain these insights, significant interpretation is still required to fully understand the findings. Our study makes two primary contributions: first, it offers a new approach for future research on the factors that determine project success or failure, closely aligning with Structuration Theory. Additionally, it suggests potential efficiency improvements in theory development by enabling multiple pattern configurations within Grounded Theory. Second, it offers practical strategies for organisations to more effectively capture and use contextual information in their project documentation for future success.