Fatores que afetam a adoção de análises de Big Data em empresas

With the total quantity of data doubling every two years, the low price of computing and data storage, make Big Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability of free software, why have some companies failed to adopt these techniques? To answer this question, we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA context, adding two variables: resistance to use and perceived risk. We used the level of implementation of these techniques to divide companies into users and non-users of BDA. The structural models were evaluated by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties companies face in implementing it. While companies planning to use Big Data expect strong results, current users are more skeptical about its performance.Con la cantidad total de datos duplicándose cada dos años, el bajo precio de la informática y del almacenamiento de datos, la adopción del análisis Big Data (BDA) es altamente deseable para las empresas, como un instrumento para conseguir una ventaja competitiva. Dada la disponibilidad de software libre, ¿por qué algunas empresas no han adoptado estas técnicas? Para responder a esta pregunta, ampliamos la teoría unificada de la adopción y uso de tecnología (UTAUT) adaptado para el contexto BDA, agregando dos variables: resistencia al uso y riesgo percibido. Utilizamos el grado de implantación de estas técnicas para dividir las empresas entre: usuarias y no usuarias de BDA. Los modelos estructurales fueron evaluados con partial least squres (PLS). Los resultados muestran que la importancia de una buena infraestructura excede las dificultades que enfrentan las empresas para implementarla. Mientras que las compañías que planean usar BDA esperan muy buenos resultados, las usuarias actuales son más escépticos sobre su rendimiento.Com a quantidade total de dados duplicando a cada dois anos, o baixo preço da computação e do armazenamento de dados tornam a adoção de análises de Big Data (BDA) desejável para as empresas, como aquelas que obterão uma vantagem competitiva. Dada a disponibilidade de software livre, por que algumas empresas não adotaram essas técnicas? Para responder a essa pergunta, estendemos a teoria unificada de adoção e uso de tecnologia (UTAUT) adaptado para o contexto do BDA, adicionando duas variáveis: resistência ao uso e risco percebido. Usamos a nível da implementação da tecnologia para dividir as empresas em usuários e não usuários de técnicas de BDA. Os modelos estruturais foram avaliados por partial least squares (PLS). Os resultados mostram que a importância de uma boa infraestrutura excede as dificuldades que as empresas enfrentam para implementá-la. Enquanto as empresas que planejam usar Big Data esperam resultados fortes, os usuários atuais são mais céticos em relação ao seu desempenho

Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Privacy and Security Concerns Associated with MHealth Technologies: A Social Media Mining Perspective

mHealth technologies seek to improve personal wellness; however, there are stillsignificant privacy and security challenges. With social networking sites serving as lens through which public sentiments and perspectives can be easily accessed, little has been done to investigate the privacy and security concerns of users, associated with mHealth technologies, through social media mining. Therefore, this study investigated various privacy and security concerns conveyed by social media users, in relation to the use of mHealth wearable technologies, using text mining and grounded theory. In addition, the study examined the general sentiments toward mHealth privacy and security related issues, while unearthing how the various issues have evolved over time. Our target social media platform for data collection was the microblogging platform Twitter, which was accessed through Brandwatch providing access to the “Twitter firehose” to extract English tweets. Triangulation was conducted on a representative sample to confirm the results of the Latent Dirichlet Allocation (LDA) Topic Modeling using manual coding through ATLAS.ti. By using the grounded theory analysis methodology, we developed the D-MIT Emergent Theoretical Model which explains that the concerns of users can be categorized as relating to data management, data invasion, or technical safety issues. This model claims that issues affecting data management of mHealth users through the misuse of their data by entities such as wearable companies and other third-party applications, negatively impact their adoption of these devices. Also, concerns of data invasion via real-time data, security breaches, and data surveillance inhibit the adoption of mHealth wearables, which is further impacted by technical safety issues. Further, when users perceived that they do not have full control over their wearables or patient applications, then their acceptance of these mHealth technologies is diminished. While a lack of data and privacy protection policies contribute negatively to users’ adoption of these devices, it also plays a pivotal role in the data management issues presented in this emergent model. Therefore, the importance of having robust legal and policy frameworks that can support mHealth users is desired. Theoretically, the results support the literature on user acceptance of mHealth wearables. These findings were compared with extant literature, and confirmations found across several studies. Further, the results show that over time, mHealth users are still concerned about areas such as security breaches, real-time data invasion, surveillance, and how companies use the data collected from these devices. The findings reveal that more than 75% of the posts analyzed were categorized as depicting anger, fear, or demonstrating levels of disgust. Additionally, 70% of the posts exhibited negative sentiments, whereas 26% were positive, which indicates that users are ambivalent concerning privacy and security, notwithstanding mentions of privacy or security issues in their posts

Uluslararası Ortak Komisyon (JCI) ve Sağlık Bilgi Yönetim Sistemleri Topluluğu (HIMSS) - Elektronik Sağlık Kaydı Benimseme Modelinin (EMRAM) metin madenciliği yöntemi ile karşılaştırmalı analizi

Introduction: Health service quality refers to all efforts to prevent a negative outcome in the health status of individuals. For this reason, measuring and evaluating the quality of health services is important to increase the quality of services provided. Aim: In this study, Joint Commission International’s (JCI) accepted indicator-based health service quality measurement model and the Healthcare Information and Management Systems Society’s (HIMSS)-Electronic Medical Record Adoption Model (EMRAM) are discussed. Method: This research used the bag-of-words model (BoW), a text mining method. Result: As a result of the analysis, the similarity of keywords (as unigrams) used in all of the guides was found to be approximately 33%, the bigram similarity was 6% and the trigram similarity was 3%. Conclusion: The fact that the similarity between the two models is not higher can be explained by the fact that, unlike JCI, the HIMSS EMRAM model handles the quality of health services with a digitalization axis. Text mining opens up new research areas as a method for comparing quality standards with new and interesting results.Giriş: Sağlık hizmet kalitesi, bireylerin sağlık durumlarında olumsuz bir sonucun oluşmasını önlemeye yönelik tüm çabaları belirtmektedir. Bu nedenle sağlık hizmetlerinin kalitesinin ölçülmesi ve değerlendirilmesi verilen hizmetin kalitesinin artırılması açısından önemlidir. Amaç: Bu çalışmada, gösterge tabanlı sağlık hizmeti kalitesi ölçüm modeli Uluslararası Ortak Komisyon (Joint Commission International-JCI) ve Sağlık Bilgi Yönetim Sistemleri Topluluğu (Healthcare Information and Management Systems Society-HIMSS)- Elektronik Sağlık Kaydı Benimseme Modeli (Electronic Medical Record Adoption Model-EMRAM) ele alınmaktadır. Yöntem: Bu araştırmada, bir metin madenciliği yöntemi olan sözcük torbası modeli (bag-of-words/BoW) kullanılmıştır. Bulgular: Analiz sonucunda tüm rehberlerde kullanılan anahtar sözcüklerin tek harfli sözcük (unigram) benzerliği yaklaşık %33, iki harfli sözcük (bigram) benzerliği %6 ve üç harfli sözcük (trigram) benzerliği %3 olarak bulunmuştur. Sonuç: İki model arasındaki benzerliğin fazla olmaması, JCI’dan farklı olarak HIMSS- EMRAM modelinin sağlık hizmet kalitesini dijitalleşme ekseniyle ele almasıyla açıklanabilir. Çalışmada metin madenciliği yönteminin kullanılması, kalite standartlarının yeni ve ilginç sonuçlarla karşılaştırma olanağı sağlamaktadır