Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

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

Engaging Students With Course Content Using Scheduled and Unscheduled Emails and Text Messages

This study addressed college students’ acceptance of push communication (i.e., email and SMS messaging) as a means of receiving course-related content, and modified the Unified Theory of Acceptance and Use of Technology by including Scheduled Message as an independent variable. Surveys of 301 students’ perceptions of instructor-sent email and SMS texts directing them to materials in six instructors’ 10 courses were analyzed by PLS-PM for their impact on the students’ intention to use these push communication technologies. In contrast to previous studies on technology acceptance, we evaluated actual usage patterns for both the scheduled and unscheduled push communication. Scheduled emails did not yield higher average duration times or unique visitors than unscheduled ones, yet click-through rates and return visits were higher. Scheduled SMS messages did yield higher average duration times, unique visitors, and click-through rates than unscheduled SMS messages, yet unscheduled SMS messages yielded more return visits. We argue that the differences in the results for email vs. SMS may have been due to email’s slower delivery time. We also consider implications for faculty wishing to facilitate distributed learning among students via push communication

Engaging Students With Course Content Using Scheduled and Unscheduled Emails and Text Messages

This study addressed college students’ acceptance of push communication (i.e., email and SMS messaging) as a means of receiving course-related content, and modified the Unified Theory of Acceptance and Use of Technology by including Scheduled Message as an independent variable. Surveys of 301 students’ perceptions of instructor-sent email and SMS texts directing them to materials in six instructors’ 10 courses were analyzed by PLS-PM for their impact on the students’ intention to use these push communication technologies. In contrast to previous studies on technology acceptance, we evaluated actual usage patterns for both the scheduled and unscheduled push communication. Scheduled emails did not yield higher average duration times or unique visitors than unscheduled ones, yet click-through rates and return visits were higher. Scheduled SMS messages did yield higher average duration times, unique visitors, and click-through rates than unscheduled SMS messages, yet unscheduled SMS messages yielded more return visits. We argue that the differences in the results for email vs. SMS may have been due to email’s slower delivery time. We also consider implications for faculty wishing to facilitate distributed learning among students via push communication

Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods

Massive Open Online Courses (MOOCs) offer unprecedented opportunities to learn at scale. Within a few years, the phenomenon of crowd-based learning has gained enormous popularity with millions of learners across the globe participating in courses ranging from Popular Music to Astrophysics. They have captured the imaginations of many, attracting significant media attention - with The New York Times naming 2012 "The Year of the MOOC." For those engaged in learning analytics and educational data mining, MOOCs have provided an exciting opportunity to develop innovative methodologies that harness big data in education.Comment: Preprint of a chapter to appear in "Data Mining and Learning Analytics: Applications in Educational Research

The Empirical Turn In Family Law

Historically, the legal system justified family law’s rules and policies through morality, common sense, and prevailing cultural norms. In a sharp departure, and consistent with a broader trend across the legal system, empirical evidence increasingly dominates the regulation of families. There is much to celebrate in this empirical turn. Properly used, empirical evidence in family law can help the state act more effectively and efficiently, unmask prejudice, and depoliticize contentious battles. But the empirical turn also presents substantial concerns. Beyond perennial issues of the quality of empirical evidence and the ability of legal actors to use it, there are more fundamental problems: Using empirical evidence focuses attention on the outcomes of legal rules, discouraging a debate about contested and competing values. Reliance on empirical evidence overlays a veneer of neutrality on normative judgments. And uncritically adopting evidence about present conditions without interrogating the role of historical discrimination that continues to disadvantage some families can replicate that discrimination. Given the promise and peril of the empirical turn in family law, this Essay proposes a framework to guide the use of this evidence. The framework preserves space for debating multiple values and advises decisionmakers when to use empirical evidence, with particular attention to the dangers for nondominant families. The framework also recommends strengthening evidentiary gatekeeping and elevating the potential for legal scholarship to serve as a bridge from the broader research base to the courts. With this guidance in place, empirical evidence can take its rightful place as a useful but cabined tool in the legal regulation of families