In this article, we introduce a framework that can be used by organizations as a positioning instrument to think of business-ICT alignment decisions in light of the strategic importance of ICT (Information and Communication Technology) in their organization. We make a distinction between organizations where ICT is of high strategic importance and those where ICT is of low strategic importance. Based on this difference we argue that heavily investing in business-ICT alignment processes, structures and roles (PSRs) will not necessarily always be beneficial when ICT is of low strategic importance to the business. Furthermore, organizations that have a minimalist approach to the use of ICT do not necessarily need to invest in business-ICT alignment PSRs. We explain the dynamics and possible migration scenarios of our proposed framework after testing the statistical significance of the relationship between the strategic importance of ICT and the investment in business-ICT alignment. We end this article with a short empirical study which combines survey and case study results. Both the framework and framework dynamics still need further empirical validation, preferably with longitudinal data. Therefore, we stress and acknowledge that many of the discussions in this article are still explorative in nature. However, this article illustrates the possibilities and the need for a more fine-grained approach to business-ICT alignment.
The personnel scheduler constructs a deterministic personnel roster that determines the line-of-work for each personnel member. When unexpected events disrupt this roster, the feasibility needs to be restored by constructing a new workable roster. The scheduler must reassign the set of employees in order to cover the disrupted shift such that the staffing requirements and the time-related personnel constraints remain satisfied. In this paper, we propose an evolutionary meta-heuristic to solve the nurse rerostering problem. We show that the proposed procedure performs consistently well under many different circumstances. We test different optimisation strategies and compare our procedure with the existing literature on a dataset that is carefully designed in a controlled and varied way.
This research explores the implications for risk management of “People Risk.” In particular how online digital behaviors, particularly from young people entering the workplace for the first time, might impact on the work setting and how risk management might mitigate impact on the employee and organization. A mixed methods approach was used to consider these implications and draws from a number of data sources in the United Kingdom including a database of self-review data around online safety policy and practice from over 2000 schools, a survey of over 1000 14–16 year olds and their attitudes toward sexting, and a survey of over 500 undergraduate students. In addition the work considers existing risk management approaches and the models therein and how they might be applied to people risk. The dataset analyzed in this exploration show an education system in the United Kingdom that is not adequately preparing young people with an awareness of the implications of digital behavior in their lives and the survey data shows distorted social norms that might have serious consequences in the workplace.
Ashby, Simon; Phippen, Andy (Incisive Financial Publishing, 2016)
Operational Risk Perspectives: Cyber, Big Data, and Emerging Risks covers key topics related to operational risk currently on the minds of practitioners. The book is comprised of chapters written by both industry professionals and academic experts who provide an overview of the current state of this discipline.
Big data is at the pinnacle of its hype cycle, offering big promise. Everyone wants a piece of the pie, yet not many know how to start and get the most out of their big data initiatives. We suggest that realizing benefits with big data depends on having the right capabilities for the right problems. When there is a discrepancy between these, organizations struggle to make sense of their data. Based on information processing theory, in this research-in-progress we suggest that there needs to be a fit between big data processing requirements and big data processing capabilities, so that organizations can realize value from their big data initiative.
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