TAXONOMY RESEARCH IN INFORMATION SYSTEMS: A SYSTEMATIC ASSESSMENT

Today’s world is changing at unprecedent speed and scale becoming more complex to understand. Taxonomies represent an important tool for understanding and analyzing complex domains based on the classification of objects. In the Information Systems (IS) domain, Nickerson et al. (2013) were the first to propose a taxonomy development method, addressing the observation that many taxonomies have been developed in an ‘ad-hoc’ approach. More than five years after Nickerson et al.’s (2013) publication, we examined to what extent recently published taxonomy articles account for existing methodological guidance. Therefore, we identified and reviewed 33 taxonomy articles published between 2013 and 2018 in leading Information Systems journals. Our results were sobering: We found few taxonomy articles that followed any specific development method. Although most articles correctly understood taxonomies as conceptually or empirically derived groupings of dimensions and characteristics, our study revealed that the development process often remained opaque and that taxonomies were hardly evaluated. We discuss these findings and potential root causes related to method design, method adoption, and the general positioning of taxonomy research in the IS domain. Our study proposes stimulating questions for future research and contributes to the IS community’s progress towards methodologically well-founded taxonomies

Developing a Solution to the TRADOC Analysis Center’s Big Data Problem: A Big Data Opportunity

As data production, collection, and analytic techniques grow, emerging issues surrounding data management and storage challenge businesses and organizations around the globe. The US Army Training and Doctrine Command’s Analysis Center (TRAC) is no exception. For example, among TRAC's many tasks is the evaluation of new materiel solutions for the Army, which typically necessitates the use of computer simulation models such as COMBAT XXI. These models are computationally expensive, and they generate copious amounts of data, straining TRAC's current resources and forcing difficult, suboptimal decisions regarding data retention and analysis. This paper addresses this issue directly by developing "big data" solutions for TRAC and evaluating them using its organizational values. Framed in the context of a use case that prescribes system requirements, we leverage Monte Carlo simulation to account for inherent uncertainty and, ultimately, focus TRAC on several high potential alternatives

Beyond Mere Compliance — Delighting Customers by Implementing Data Privacy Measures?

The importance of customer data for business models is increasing, as is the relevance of customers’ concerns regarding privacy aspects. To prevent data privacy incidents and to mitigate the associated risks, companies need to implement appropriate measures. Furthermore, it is unclear whether their implementation – beyond mere compliance – has the potential to actually delight customers and yields competitive advantages. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on the literature, legislative texts, and expert interviews. Next, we leverage the Kano model via an Internet-based survey to analyze the measures’ evaluation by customers. As a result, most measures are considered basic needs of must-be quality. Their implementation is obligatory and is not rewarded by customers. However, delighters of attractive quality do exist and have the potential to create a competitive advantage

An Update for Taxonomy Designers

Taxonomies are classification systems that help researchers conceptualize phenomena based on their dimensions and characteristics. To address the problem of ‘ad-hoc’ taxonomy building, Nickerson et al. (2013) proposed a rigorous taxonomy development method for information systems researchers. Eight years on, however, the status quo of taxonomy research shows that the application of this method lacks consistency and transparency and that further guidance on taxonomy evaluation is needed. To fill these gaps, this study (1) advances existing methodological guidance and (2) extends this guidance with regards to taxonomy evaluation. Informed by insights gained from an analysis of 164 taxonomy articles published in information systems outlets, this study presents an extended taxonomy design process together with 26 operational taxonomy design recommendations. Representing an update for taxonomy designers, it contributes to the prescriptive knowledge on taxonomy design and seeks to augment both rigorous taxonomy building and evaluation

Comparing Methods and Defining Practical Requirements for Extracting Harmonic Tidal Components from Groundwater Level Measurements

The groundwater pressure response to the ubiquitous Earth and atmospheric tides provides a largely untapped opportunity to passively characterize and quantify subsurface hydro-geomechanical properties. However, this requires reliable extraction of closely spaced harmonic components with relatively subtle amplitudes but well-known tidal periods from noisy measurements. The minimum requirements for the suitability of existing groundwater records for analysis are unknown. This work systematically tests and compares the ability of two common signal processing methods, the discrete Fourier transform (DFT) and harmonic least squares (HALS), to extract harmonic component properties. First, realistic conditions are simulated by analyzing a large number of synthetic data sets with variable sampling frequencies, record durations, sensor resolutions, noise levels and data gaps. Second, a model of two real-world data sets with different characteristics is validated. The results reveal that HALS outperforms the DFT in all aspects, including the ability to handle data gaps. While there is a clear trade-off between sampling frequency and record duration, sampling rates should not be less than six samples per day and records should not be shorter than 20 days when simultaneously extracting tidal constituents. The accuracy of detection is degraded by increasing noise levels and decreasing sensor resolution. However, a resolution of the same magnitude as the expected component amplitude is sufficient in the absence of excessive noise. The results provide a practical framework to determine the suitability of existing groundwater level records and can optimize future groundwater monitoring strategies to improve passive characterization using tidal signatures