Assessment of Factors Influencing Intent-to-Use Big Data Analytics in an Organization: A Survey Study

The central question was how the relationship between trust-in-technology and intent-to-use Big Data Analytics in an organization is mediated by both Perceived Risk and Perceived Usefulness. Big Data Analytics is quickly becoming a critically important driver for business success. Many organizations are increasing their Information Technology budgets on Big Data Analytics capabilities. Technology Acceptance Model stands out as a critical theoretical lens primarily due to its assessment approach and predictive explanatory capacity to explain individual behaviors in the adoption of technology. Big Data Analytics use in this study was considered a voluntary act, therefore, well aligned with the Theory of Reasoned Action and the Technology Acceptance Model. Both theories have validated the relationships between beliefs, attitudes, intentions and usage behavior. Predicting intent-to-use Big Data Analytics is a broad phenomenon covering multiple disciplines in literature. Therefore, a robust methodology was employed to explore the richness of the topic. A deterministic philosophical approach was applied using a survey method approach as an exploratory study which is a variant of the mixed methods sequential exploratory design. The research approach consisted of two phases: instrument development and quantitative. The instrument development phase was anchored with a systemic literature review to develop an instrument and ended with a pilot study. The pilot study was instrumental in improving the tool and switching from a planned covariance-based SEM approach to PLS-SEM for data analysis. A total of 277 valid observations were collected. PLS-SEM was leveraged for data analysis because of the prediction focus of the study and the requirement to assess both reflective and formative measures in the same research model. The measurement and structural models were tested using the PLS algorithm. R2, f2, and Q2 were used as the basis for the acceptable fit measurement. Based on the valid structural model and after running the bootstrapping procedure, Perceived Risk has no mediating effect on Trust-in-Technology on Intent-to-Use. Perceived Usefulness has a full mediating effect. Level of education, training, experience and the perceived capability of analytics within an organization are good predictors of Trust-in-Technology

A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises

AbstractManufacturing enterprises are currently facing substantial challenges with regard to disruptive concepts such as the Internet of Things, Cyber Physical Systems or Cloud-based Manufacturing – also referred to as Industry 4.0. Subsequently, increasing complexity on all firm levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop them. In this paper we propose an empirically grounded novel model and its implementation to assess the Industry 4.0 maturity of industrial enterprises in the domain of discrete manufacturing. Our main goal was to extend the dominating technology focus of recently developed models by including organizational aspects. Overall we defined 9 dimensions and assigned 62 items to them for assessing Industry 4.0 maturity. The dimensions “Products”, “Customers”, “Operations” and “Technology” have been created to assess the basic enablers. Additionally, the dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for including organizational aspects into the assessment. Afterwards, the model has been transformed into a practical tool and tested in several companies whereby one case is presented in the paper. First validations of the model's structure and content show that the model is transparent and easy to use and proved its applicability in real production environments

Assessing Hospital Information Systems Processes: A Validation of PRISE Information Systems Success Model in Healthcare

Although there is limited research and evidence base, it is reasonable to expect that high quality information technology is an integral factor in the success of today’s health care sector. However, the health care sector is considered to be low level investor in Information Technology (IT) when compared to other sectors. There are studies that look at the sums spent on health IT as a basis for determining how effective the IT systems are. We support the idea that the effectiveness of IT systems, is not an exact measure and a more systematic approach needs to be taken when evaluating success of an IT system. In this study, we have evaluated an assessment method, which is, “Process Based Information Systems (IS) Effectiveness (PRISE)” based on a novel model of IS effectiveness in the health care sector. The results of our case series provide specific implications concerning the applicability of a general “IS assessment” approach, in the medical context

HOW AGILE IS YOUR IT DEPARTMENT? – DEVELOPMENT AND APPLICATION OF AN FRAMEWORK-INDEPENDENT AGILE SCALING MATURITY MODEL

Many IT departments seek to capitalize on the benefits of agile development by scaling agile practices. To manage the complex scaling, established approaches and frameworks promise guidance. However, although existing works envision a clear target state, they lack relevant capabilities along the scaling process, especially for vertical agile scaling. Managers need these capabilities to assess their company’s status quo and develop a clear scaling roadmap. Thus, within this work, we use the Design Science Research paradigm to build and evaluate a framework-independent agile scaling maturity model that provides management with a tool for ex-ante identification and evaluation of agile scaling capabilities in five maturity stages. To evaluate our model, we applied it at KUKA IT, the IT department of an international provider of automation solutions. As a result, this work provides insights into the application and outlines how IT departments can operationalize and utilize our model to guide agile scaling