Abstract The extended unified theory of acceptance and use of technology (UTAUT2) is less than ten years old and has already garnered more than 6000 citations with extensive usage in information systems and beyond. This research employed cited reference search to systematically review studies that cited UTAUT2 originating article. Based on UTAUT2 usage, the downloaded articles were classified into four categories such as: 1) General citation, 2) UTAUT2 application, 3) UTAUT2 integration, and 4) UTAUT2 extensions. Weber's (2012) theory evaluation framework revealed UTAUT2 as a robust theory on most dimensions except for parsimony arising from the complex model. UTAUT2 extensions emerged as popular UTAUT2 utilization category as researchers extended the model with context specific variables. Finally, UTAUT2 extensions were mapped to Johns' (2006) context dimensions to identify various limitations of the existing technology adoption research and to provide multi-level framework for future researchers with libraries of context dimensions.
We define the relevant information in a signal $x\in X$ as being the information that this signal provides about another signal $y\in \Y$. Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal $x$ requires more than just predicting $y$, it also requires specifying which features of $\X$ play a role in the prediction. We formalize this problem as that of finding a short code for $\X$ that preserves the maximum information about $\Y$. That is, we squeeze the information that $\X$ provides about $\Y$ through a `bottleneck' formed by a limited set of codewords $\tX$. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure $d(x,\x)$ emerges from the joint statistics of $\X$ and $\Y$. This approach yields an exact set of self consistent equations for the coding rules $X \to \tX$ and $\tX \to \Y$. Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.
Abstract Burgeoning research in data sciences demonstrates that big data analytics capability (BDAC) transforms large amounts of data into valuable knowledge and information, enhancing decision processes and improving firm performance. Nevertheless, limited research has theoretically outlined and empirically established the frameworks and constructs through which BDAC impacts the performance of small and medium enterprises (SMEs). This study adds to existing research on the relationship between BDAC and SME performance. Drawing on the dynamic capability theory, it is essential to argue how BDAC influences marketing performance (MP) and financial performance (FP), which is dependent on the intervening role of knowledge management with big data analytics talent capability (BDATC). This study highlighted the mediating role of knowledge Management (KM) and the moderating effect of Big Data Analytics Talent Capabilities (BDATC) in relation to BDAC. Based on the Conceptual model, data was collected from 379 SMEs in China using a well-designed questionnaire. Structural Equation Modeling (SEM) was employed using AMOS and SPSS for data analysis. Findings show that BDAC positively influences the firm’s financial and marketing performance. Furthermore, results confirm that KM mediates the link between a firm’s BDAC and financial and marketing performance. The findings also confirm that BDATLC significantly moderates the relationship between BDAC and financial performance while negatively moderating the relationship between BDAC and marketing performance. This study contributes to understanding the important role of human talent capability in the era of technology and big data (BDATLC), particularly regarding the talent capability for Big Data Analytics (BDA). The findings highlight the strategic significance of nurturing and retaining BDA talent to enhance the performance of SMEs.
History of scholarship and learning. The humanities, Social Sciences
The purpose of this manuscript is to provide general system theory concepts and practical tools for management under complexity. Built environments and infrastructure are produced, operated, and maintained by information systems; they are also integral components of information systems themselves. These systems are self-organized and teleonomic. The complexity inherent in built environments and infrastructure systems poses a challenge to research, hindering forecasting and the implementation of managerial tools. The use of faults, which are complex systems’ responses to penetrating risk, provide us with databases of and windows into complex systems. This manuscript presents an explicatory theory (ToF), develops it mathematically, expands it through numerical experiments, validates it by case studies, and relates it to practice by expert contributions. A statistical analysis provides a phase parameter, descriptive statistics elucidate trending and emergent behaviors, digital signal processing expounds the effects of signals on information overload, and a directed-network analysis portray morphology, entropy, and time effects. The novelty of ToF is in the application of complexity theory to construction to produce data analysis tools and a managerial framework.