Systems, Volume 7, Issue 2 (June 2019) – 13 articles

An analytics-empowered enterprise system looks to many organizations to be a far-fetched target, owing to the vast amounts of factors that need to be controlled across the implementation lifecycle activities, especially during usage and maintenance phases. On the other hand, advanced analytics techniques such as machine learning and data mining have been strongly present in academic as well as industrial arenas through robust classification and prediction. Correspondingly, this paper is set out to address a methodological approach that works on tackling post-live implementation activities, focusing on employing advanced analytics techniques to detect (business process) problems, find and recommend a solution to them, and confirm the solution. The objective is to make enterprise systems self-moderated by reducing the reliance on vendor support. The paper will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach. Employing an advanced analytics engine has the potential to support post-implementation activities. Our research is innovative in two ways: (1) it enables enterprise systems to become self-moderated and increase their availability; and (2) the IT artifact i.e., the analytics engine, has the potential to solve other problems and be used by other systems, e.g., HRIS. This paper is beneficial to businesses implementing enterprise systems. It highlights how enterprise systems could be safeguarded from retirement caused by post-implementation problems. Full article

Urban areas can be considered the ground for the challenges related to the UN’s Sustainable Development Goals (SDGs). The objective of shaping cities as human settlement that will see a more inclusive, safe, resilient, and sustainable future is often argued in literature as an issue dependent on behavioral change of inhabitants in urban areas. In this paper, the authors question if experimental applications based on gamification can co-produce more sustainable neighborhoods through an impact evaluation method that departs from individual choices within the complex of urban mobility. This investigation is carried out within MUV (Mobility Urban Values), an EU research and innovation project, which aims to trigger more sustainable urban mobility in six pilot cities. This article describes the critical method of validation, an impact assessment of the MUV experimental gamification in the pilot cities, in order to represent a proof for future urban strategies. This methodological approach is based on an evaluation structured on indicators of both impact and process suitable for urban contexts. As based on six pilot cities, with possibilities for transferability to other contexts and scalability to other cities, the method represents a reference work for the evaluation of similar experimental applications. Full article

The present paper proposes a model for evaluating environmental, social, and economic impacts exerted by the diffusion of electric vehicles (EVs), which is a phenomenon that can significantly affect the achievement of some of the objectives set by the Sustainable Development Agenda. The impact evaluation is carried out through the System Dynamics methodology, combined with scenario analysis. Considering the Piedmont region (Italy) as a case study, the model forecasts the impacts of EV diffusion using a simulation timeframe of 12 years and leveraging eight EV diffusion scenarios. According to the model, an increase in the number of EVs results in less air pollution and, therefore, minor public health expenditure. These cost savings can be turned into incentives for purchasing new EVs, which make the fleet increasingly greener as part of a self-reinforcing loop. Despite the fact that the model could be improved through additional research on some variables’ definitions, this ex ante evaluation tool represents a valuable instrument for policy-makers. In fact, it provides a comprehensive picture of EV diffusion in view of the triple sustainability principles: System Dynamics, in particular, allows singling out causal relationships among variables, thus anticipating possible effects of planned policy actions. Full article

Examining past research using content analysis can provide detailed information about the trends a discipline has displayed over years or decades. Disciplines such as software engineering and hospitality management have used methods for analyzing topic frequencies in the past. This paper uses similar methods to study the presence of systems engineering topics in literature to understand the occurrence of approaches and metrics in the community over time. This paper reviews the trends of systems engineering topics over the years 1998–2016 by performing a content analysis of four systems engineering and design engineering journals. A mathematical analysis of the trends is performed using rank ordering and a rank correlation metric. The study shows that model-based approaches have grown significantly since the early 2000s, as advocated in the INOCSE MBSE initiative, while other approaches such as Taguchi’s method and axiomatic design have been seen less frequently in literature. Systems engineering metrics that have been frequently seen in literature include complexity, reliability, and quality. The results from this research provide a portrait of the systems engineering landscape. Full article

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research. Full article

This paper presents the development of a novel system dynamics (SD) model for better understanding the interrelation and feedback mechanism between the wastewater collection (WWC) and wastewater treatment plant (WWTP) systems. Causal loop diagrams (CLDs) are developed and discussed to depict and understand feedback and inter-connections between physical, financial, and consumer sectors. The developed SD model is then extended to include the greenhouse gas (GHG) emissions, as a proxy for the environmental sector and for an environmental sustainability assessment of strategic decisions related to asset management planning of wastewater infrastructure system. It also adds new policy levers, such as population growth and urban densification in the social sector, and minimum fee-hike rates in the finance sector to enhance the representation of real-world conditions in the asset management planning. This new SD model will enable decision-makers to assess the sustainability impacts of their strategic decisions on wastewater systems, find synergistic cost-saving opportunities, and improve the sustainability performance of their asset management plans. Full article

There is growing interest in studying processes of human sensemaking, as this strongly influences human and organizational behavior as well as complex system dynamics due to the diverse lenses people use to interpret and act in the world. The Cognitive Edge SenseMaker^® tool is one method for capturing and making sense of people’s attitudes, perceptions, and experiences. It is used for monitoring and evaluation; mapping ideas, mind-sets, and attitudes; and detecting trends and weak signals. However, academic literature describing the tool-set and method is lacking. This introduction aims to guide researchers in choosing when to use SenseMaker and to facilitate understanding of its execution and limitations. SenseMaker can provide nuanced insight into system-level patterns of human sensemaking that can provide insight to nudge systems towards more desirable futures, and enable researchers to measure beyond what they know. Full article

The aim of the paper is to examine commonality in liquidity indices across emerging European stock markets. Five markets are included in the study: Hungarian, Czech, Polish, Russian and Turkish, in the period from 2008 to 2017. We propose liquidity indices that are based on low-frequency liquidity proxies and capture both the dynamics coming from volume and price changes. We find strong commonality of the liquidity indices across all examined markets which is robust to the choice of liquidity proxy. The dependence between indices enhances in times of crisis and large market declines, and weakens when markets become stable. We also examine the interdependency between liquidity and volatility estimates and find that liquidity on the European emerging markets is related to CBOE Volatility Index (VIX). Liquidity in the whole region decreases when VIX increases, and vice versa. The liquidity indices based on the extreme market movements show that there are no differences in commonality in time of extreme negative and positive returns. Full article

Digital innovation is a key success factor for business enterprises and organizations concerned with public safety. Increasingly shorter technology cycles produce a stream of highly promising gadgets and smart devices and this innovative provision opens a gap between what is currently in use for the value-creation processes of an organization and what could potentially be used. The presented framework provides guidance on how to implement dynamic capabilities needed for business model and service innovation within a complex socio-technical system. A way to combine technology and use-case sensing with the ultimate aim of creating innovative artifacts for organizations is presented. While Business Model Innovation (BMI) literature mainly focuses on a strategic top-down process, we propose a bottom-up process-driven approach to complement business frameworks. Based on these insights, new service artifacts can be designed and analyzed in a systemic way. The applied research methodology is based on the design science research concept. A qualitative approach with focus groups was used to gather user requirements and facilitate participatory and user-centered design of information systems. In this paper, we provide a framework that supports business executives as well as IT experts on how to cope with and integrate new technologies into organizations, their processes, and their business models. In addition to a comprehensive theoretical overview of the proposed framework, we also provide practical results, since this framework was applied in the course of a service design and engineering research project. A use case of alpine rescue missions serves as an example to demonstrate the practical application of the proposed framework. Full article

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. “Static analyses” (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the “dynamic analyses”, based on five coarse-grained descriptors related to variability, the degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature enough to shift from a metaphorical to a fully quantitative status. Full article

Despite “cyber” being in the name, cyber–physical systems possess unique characteristics that limit the applicability and suitability of traditional cybersecurity techniques and strategies. Furthermore, vulnerabilities to cyber–physical systems can have significant safety implications. The physical and cyber interactions inherent in these systems require that cyber vulnerabilities not only be defended against or prevented, but that the system also be resilient in the face of successful attacks. Given the complex nature of cyber–physical systems, the identification and evaluation of appropriate defense and resiliency strategies must be handled in a targeted and systematic manner. Specifically, what resiliency strategies are appropriate for a given system, where, and which should be implemented given time and/or budget constraints? This paper presents two methodologies: (1) the cyber security requirements methodology and (2) a systems-theoretic, model-based methodology for identifying and prioritizing appropriate resiliency strategies for implementation in a given system and mission. This methodology is demonstrated using a case study based on a hypothetical weapon system. An assessment and comparison of the results from the two methodologies suggest that the techniques presented in this paper can augment and enhance existing systems engineering approaches with model-based evidence. Full article

The emergence of knowledge economy and knowledge management revealed the need for reconsidering the concept of knowledge in a larger framework than that created by philosophers from ancient times. While the epistemology as a theory of knowledge and justification considers knowledge as a justified true belief, experts in knowledge management consider knowledge as a strategic resource. The new economic interpretation of knowledge as a strategic resource and a key contributor to achieving a competitive advantage generated a search of new metaphors to supply the attributes needed in constructing the new framework of understanding and operating with a working concept of knowledge in management. The most widespread knowledge metaphors are based on analogies with stocks, flows, and stock-and-flows. These metaphors induce, beyond some useful attributes, the Newtonian mechanics paradigm which is limited by the properties of linear spaces and reversible processes. The purpose of this paper is to show how we can enrich the theory of knowledge by introducing the concepts of knowledge fields and knowledge dynamics based on metaphorical thinking and the thermodynamics principles. The focus of our research is the energy metaphor which considers energy as a source semantic field. The main outcome of the present research is that knowledge can be considered as a field, which is manifesting in different forms like energy. This thermodynamics framework opens new directions for research in knowledge management, decision-making and leadership. Full article

Some authors suggest that transitioning requirements engineering from the traditional statements in natural language with shall clauses to model-based requirements within a Model-Based Systems Engineering (MBSE) environment could improve communication, requirements traceability, and system decomposition, among others. Requirement elements in the Systems Modeling Language (SysML) fail to fulfill this objective, as they are really a textual requirement in natural language as a model element. Current efforts to directly leverage behavioral and structural models of the system lack an overarching theoretical framework with which to assess the adequacy of how those models are used to capture requirements. This paper presents an approach to construct true model-based requirements in SysML. The presented approach leverages some of SysML’s behavioral and structural models and diagrams, with specific construction rules derived from Wymore’s mathematical framework for MBSE and taxonomies of requirements and interfaces. The central proposition of the approach is that every requirement can be modeled as an input/output transformation. Examples are used to show how attributes traditionally thought of as non-functional requirements can be captured, with higher precision, as functional transformations. Full article