Management Implications in Information Systems Research: The Untold Story

In this essay, we take a fresh look at the IS academic community’s enduring concern with the management implications of its research. We examine in particular what we call the “variables-centered” research paradigm, which focuses its attention on co-variance among independent and dependent variables. As the predominant research tradition in the field, the variables-centered paradigm ought to constitute a major platform from which our community can speak to issues of managerial interest. Unfortunately, the variables-centered paradigm appears to distance researchers from the organizational actors, such as managers, to whom they would give advice and counsel. Particularly disturbing is the systematic erasure of those very actors from the domain of inquiry. Erased, too, are their actions and means of acting. Thus, when it comes time to offer useful prescriptions for action, our community attempts to do so on the basis of research in which, ironically, neither actors nor action directly appear. We offer some recommendations that may help to rectify this problem and, thereby, enrich the capacity of variables-centered research to speak in an informative and useful way to issues of practice

Stigmergy-based modeling to discover urban activity patterns from positioning data

Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015

Building a complementary agenda for business process management and digital innovation.

The world is blazing with change and digital innovation is fuelling the fire. Process management can help channel the heat into useful work. Unfortunately, research on digital innovation and process management has been conducted by separate communities operating under orthogonal assumptions. We argue that a synthesis of assumptions is required to bring these streams of research together. We offer suggestions for how these assumptions can be updated to facilitate a convergent conversation between the two research streams. We also suggest ways that methodologies from each stream could benefit the other. Together with the three exemplar empirical studies included in the special issue on business process management and digital innovation, we develop a broader foundation for reinventing research on business process management in a world ablaze with digital innovation

The Convergence of Business Process Management and Digital Innovation

Business process management is a prolific field of research and an area of strong industrial uptake with roots in both management science and information systems engineering. Traditionally, business process management has largely been utilized in an inward-looking way with the aim to improve operations, eliminate waste, and increase efficiency. Recent developments around digital innovation challenge conventional ideas of process reengineering with a strong emphasis on the external market and exploration. In this talk, we will discuss the complementarity of BPM and digital innovation

Stochastic parareal: an application of probabilistic methods to time-parallelisation

Parareal is a well-studied algorithm for numerically integrating systems of time-dependent differential equations by parallelising the temporal domain. Given approximate initial values at each temporal sub-interval, the algorithm locates a solution in a fixed number of iterations using a predictor-corrector, stopping once a tolerance is met. This iterative process combines solutions located by inexpensive (coarse resolution) and expensive (fine resolution) numerical integrators. In this paper, we introduce a stochastic parareal algorithm with the aim of accelerating the convergence of the deterministic parareal algorithm. Instead of providing the predictor-corrector with a deterministically located set of initial values, the stochastic algorithm samples initial values from dynamically varying probability distributions in each temporal sub-interval. All samples are then propagated by the numerical method in parallel. The initial values yielding the most continuous (smoothest) trajectory across consecutive sub-intervals are chosen as the new, more accurate, set of initial values. These values are fed into the predictor-corrector, converging in fewer iterations than the deterministic algorithm with a given probability. The performance of the stochastic algorithm, implemented using various probability distributions, is illustrated on systems of ordinary differential equations. When the number of sampled initial values is large enough, we show that stochastic parareal converges almost certainly in fewer iterations than the deterministic algorithm while maintaining solution accuracy. Additionally, it is shown that the expected value of the convergence rate decreases with increasing numbers of samples

Research challenges in nanosatellite-DTN networks

Current approaches based on classical satellite communications, aimed at bringing Internet connectivity to remote and underdeveloped areas, are too expensive and impractical. Nanosatellites architectures with DTN protocol have been proposed as a cost-effective solution to extend the network access in rural and remote areas. In order to guarantee a good service and a large coverage in rural areas, it is necessary to deploy a good number of nanosatellites; consequentially, for reliability and load balancing purposes, is also needed a large number of ground stations (or hot spots) connected on the Internet. During a data connection, a server on the Internet that wants to reply to the user on rural area, has many hot spot alternatives to whom it can deliver data. Different hot spots can send data to final destination with different delivery delay depending on the number, position and buffer occupancy of satellites with which it comes into contact. The problem of choosing the optimal hot spot becomes important because a wrong choice could lead a high delivery delay