Root Cause Analysis in Business Processes

Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is control flow and uses the respective model barely as a reference for brain-storming about improvement opportunities. This approach heavily relies on the intuition of the participants and misses a clear description of steps to identify root causes of problems. In contrast to that, this paper introduces a systematic methodology to detect and document the quality dimension of a business process. It builds on the definition of softgoals for each process activity, of correlations between softgoals, and metrics to measure the occurrence of quality issues. In this regard our contribution is a foundation of root-cause analysis in business process modeling, and a conceptual integration of goal-based and activity-based approaches to capturing processes

Improving the Semantics of Conceptual-Modeling Grammars: A New Perspective on an Old Problem

A core activity in information systems development involves understanding the conceptual model of the domain that the information system supports. Any conceptual model is ultimately created using a conceptual-modeling (CM) grammar. Accordingly, just as high quality conceptual models facilitate high quality systems development, high quality CM grammars facilitate high quality conceptual modeling. This paper seeks to provide a new perspective on improving the quality of CM grammar semantics. For the past twenty years, the leading approach to this topic has drawn on ontological theory. However, the ontological approach captures just half of the story. It needs to be coupled with a logical approach. We show how ontological quality and logical quality interrelate and we outline three contributions of a logical approach: the ability to see familiar conceptual-modeling problems in simpler ways, the illumination of new problems, and the ability to prove the benefit of modifying CM grammars

A Cognitive Perspective on How Experts Develop Conceptual Models in Complex Domains

Conceptual models are important in understanding the domain which is to be reflected in the information systems. Development of such models involves experts in conceptual modeling techniques (ISDK experts) and experts in the domain application (ISAK experts). This paper focuses on understanding how these two types of experts interact and develop conceptual models jointly. Using an exploratory study, it was identified that in the early phase of development of conceptual models, the experts focus on understanding concepts of the domains that they are not familiar with. Later, when the experts had shared information on the concepts of the domains then they focus on developing the conceptual model. The study also indicates that the groups of experts that have high shared information are most likely to create high quality conceptual models

The Effects of Decomposition Quality and Multiple Forms of Information on Novices’ Understanding of a Domain from a Conceptual Model

Individuals can often use conceptual models to learn about the business domain to be supported by an information system. We investigate the extent to which such models can help novices (i.e., individuals who lack knowledge in the business domain and in conceptual modeling) to obtain an understanding of the domain codified in the model. We focus on two factors that we predict will influence novices’ understanding: (1) decomposition quality: whether the conceptual model manifests a good decomposition of the domain, and (2) multiple forms of information: whether the conceptual model is accompanied by information in another form (e.g., a textual narrative). We hypothesize that both factors will have positive effects on understanding and that these effects depend on whether the individual seeks a surface or deep understanding. Our results are largely in line with our predictions. Moreover, our results suggest that while novices are generally aware that having multiple forms of information affects their understanding, they are unaware that decomposition quality affects their understanding. Based on these results, we recommend that practitioners include complementary forms of information (such as a textual narrative) along with conceptual models and be careful to ensure that their conceptual models manifest a good decomposition of the domain

A Framework for Developing the Structure of Public Health Economic Models

Background: A conceptual modeling framework is a methodology that assists modelers through the process of developing a model structure. Public health interventions tend to operate in dynamically complex systems. Modeling public health interventions requires broader considerations than clinical ones. Inappropriately simple models may lead to poor validity and credibility, resulting in suboptimal allocation of resources. Objective: This article presents the first conceptual modeling framework for public health economic evaluation. Methods: The framework presented here was informed by literature reviews of the key challenges in public health economic modeling and existing conceptual modeling frameworks; qualitative research to understand the experiences of modelers when developing public health economic models; and piloting a draft version of the framework. Results: The conceptual modeling framework comprises four key principles of good practice and a proposed methodology. The key principles are that 1) a systems approach to modeling should be taken; 2) a documented understanding of the problem is imperative before and alongside developing and justifying the model structure; 3) strong communication with stakeholders and members of the team throughout model development is essential; and 4) a systematic consideration of the determinants of health is central to identifying the key impacts of public health interventions. The methodology consists of four phases: phase A, aligning the framework with the decision-making process; phase B, identifying relevant stakeholders; phase C, understanding the problem; and phase D, developing and justifying the model structure. Key areas for further research involve evaluation of the framework in diverse case studies and the development of methods for modeling individual and social behavior. Conclusions: This approach could improve the quality of Public Health economic models, supporting efficient allocation of scarce resources

Understanding understandability of conceptual models - what are we actually talking about? - Supplement

Investigating and improving the quality of conceptual models has gained tremendous importance in recent years. In general, model understandability is regarded one of the most important model quality goals and criteria. A considerable amount of empirical studies, especially experiments, have been conducted in order to investigate factors in-fluencing the understandability of conceptual models. However, a thorough review and reconstruction of 42 experiments on conceptual model understandability shows that there is a variety of different understandings and conceptualizations of the term model understandability. As a consequence, this term remains ambiguous, research results on model understandability are hardly comparable and partly imprecise, which shows the necessity of clarification what the conceptual modeling community is actually talking about when the term model understandability is used. This contribution represents a supplement to the article „ Understanding understandability of conceptual models – What are we actually talking about?” published in the Proceedings of the 31st International Conference on Conceptual Modeling (ER 2012) which aimed at overcoming the above mentioned shortcoming by investigating and further clarifying the concept of model understandability. This supplement contains a complete overview of Table 1 (p. 69 in the original contribution) which could only be partly presented in the conference proceedings due to space limitations. Furthermore, an erratum concerning the overview in Table 2 (p. 71 in the original contribution) is presented

Ontological Clarity, Cognitive Engagement, and Conceptual Model Quality Evaluation: An Experimental Investigation

When analysts build information systems, they document their understanding of users’ work domains via conceptual models. Once a model has been developed, analysts should then check it has no defects. The literature provides little guidance about approaches to improve the effectiveness of conceptual model quality evaluation work. In this light, we propose a theory in which two factors have a material impact on the effectiveness of conceptual model quality evaluation work: (a) the ontological clarity of the conceptual models prepared, and (b) the extent to which analysts use a quality evaluation method designed to cognitively engage stakeholders with the semantics of the domain represented by a conceptual model. We tested our theory using an experiment involving forty-eight expert data modeling practitioners. Their task was to find as many defects as possible in a conceptual model. Our results showed that participants who received the conceptual model with greater ontological clarity on average detected more defects. However, participants who were given a quality evaluation method designed to cognitively engage them more with the semantics of the domain did not detect more defects. Nonetheless, during our analysis of participants’ protocols, we found that those who manifested higher levels of cognitive engagement with the model detected more defects. Thus, we believe that our treatment for the level of cognitive engagement evoked by the quality evaluation method did not take effect. Based on our protocol analyses, we argue that cognitive engagement appears to be an important factor that affects the quality of conceptual model evaluation work

A Scale-Explicit Framework for Conceptualizing the Environmental Impacts of Agricultural Land Use Changes

Demand for locally-produced food is growing in areas outside traditionally dominant agricultural regions due to concerns over food safety, quality, and sovereignty; rural livelihoods; and environmental integrity. Strategies for meeting this demand rely upon agricultural land use change, in various forms of either intensification or extensification (converting non-agricultural land, including native landforms, to agricultural use). The nature and extent of the impacts of these changes on non-food-provisioning ecosystem services are determined by a complex suite of scale-dependent interactions among farming practices, site-specific characteristics, and the ecosystem services under consideration. Ecosystem modeling strategies which honor such complexity are often impenetrable by non-experts, resulting in a prevalent conceptual gap between ecosystem sciences and the field of sustainable agriculture. Referencing heavily forested New England as an example, we present a conceptual framework designed to synthesize and convey understanding of the scale- and landscape-dependent nature of the relationship between agriculture and various ecosystem services. By accounting for the total impact of multiple disturbances across a landscape while considering the effects of scale, the framework is intended to stimulate and support the collaborative efforts of land managers, scientists, citizen stakeholders, and policy makers as they address the challenges of expanding local agriculture

Changes in students’ mental models from computational modeling of gene regulatory networks

Background: Computational modeling is an increasingly common practice for disciplinary experts and therefore necessitates integration into science curricula. Computational models afford an opportunity for students to investigate the dynamics of biological systems, but there is significant gap in our knowledge of how these activities impact student knowledge of the structures, relationships, and dynamics of the system. We investigated how a computational modeling activity affected introductory biology students’ mental models of a prokaryotic gene regulatory system (lac operon) by analyzing conceptual models created before and after the activity. Results: Students’ pre-lesson conceptual models consisted of provided, system-general structures (e.g., activator, repressor) connected with predominantly incorrect relationships, representing an incomplete mental model of gene regulation. Students’ post-lesson conceptual models included more context-specific structures (e.g., cAMP, lac repressor) and increased in total number of structures and relationships. Student conceptual models also included higher quality relationships among structures, indicating they learned about these context-specific structures through integration with their expanding mental model rather than in isolation. Conclusions: Student mental models meshed structures in a manner indicative of knowledge accretion while they were productively re-constructing their understanding of gene regulation. Conceptual models can inform instructors about how students are relating system structures and whether students are developing more sophisticated models of system-general and system-specific dynamics