HCI/MIS Workshop Proceedings Format

Lukyanenko

Conceptual Modeling in MIS Curriculum

Crux or curio? Expanding the role of conceptual modeling in the introductory information systems curriculum Roman Lukyanenko University of Saskatchewan [email protected]

ABSTRACT

Historically, conceptual modeling played a central role in the information systems curriculum, but evidence is mounting that its importance is beginning to diminish. As the introductory MIS curriculum offers the students the glimpse of the field and sets them on course for a deeper exploration of the information systems discipline, if this trend deepens, important information systems knowledge may be lost. In this paper, we explore some of the potential causes for the shrinkage of the conceptual modeling content in the introductory MIS curriculum, and also offer several potential solutions. With this work we continue the ongoing debate within the academic community on the role of conceptual modeling in the curriculum and in the discipline, more broadly. Keywords

Conceptual modeling, undergraduate MIS curriculum, systems analysis and design, database design, MIS textbooks INTRODUCTION

Historically, conceptual modeling played a central role in the information systems curriculum. Most, if not all, introductory textbooks in MIS, contained at least one, or at times several chapters on database design, in which conceptual modeling, such as entity relationship diagrams (ERDs), was featured prominently. This was important, as the introductory curriculum offered the students a glimpse of the field, ignited their interest, and set them on course for a deeper exploration of the information systems discipline.

Presently, evidence is growing that the interest in conceptual modeling within information systems is waning. Already in 2005, researchers raised concerns that although the topic was of core importance to information systems, less and less of the research on conceptual modeling had been undertaken in top journals (Bajaj et al. 2005). A 2016 text mining analysis of theoretical constructs in information systems discipline reveals that only 10.2% of theoretical constructs relate to IS development (and of those many relate to non-conceptual modeling topics)(Larsen and Bong 2016, Appendix B). A recent analysis of the “number of papers on conceptual modeling published in top IS journals shows a downward trend of research on conceptual modeling” in the past 15 years (Jabbari et al. 2018, p. 5). Researchers begin to raise the question of whether conceptual modeling, at least the way it was traditionally undertaken (e.g., ERDs) is “becoming obsolete” (Lukyanenko and Parsons 2013). Morbid statements have been made about other activities (e.g., relational, data warehouse design) closely associated with traditional conceptual modeling (Atzeni et al. 2013; Rizzi et al. 2006). These concerns are also supported by evidence from practice. Reports indicate that almost 90% of organizations are moving to agile development methodology, where formal modeling may play a very limited role1. Likewise, some practitioners challenge the value of traditional approaches to conceptual modeling (e.g., UML, ERD), especially when designing databases for the booming NoSQL environments (Frisendal 2016; Hills 2016). Considering these developments, it is not surprising to observe the same trend in the introductory MIS curriculum. A review of popular introductory

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http://www.devopsdigest.com/testing-trends-in2016-fully-agile-or-just-fast-waterfall

Proceedings of the 17th AIS SIGSAND Symposium, Syracuse, NY May 24-25, 2018

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textbooks on MIS shows a decline in the space dedicated to conceptual modeling. Thus, in transitioning from the third to the fourth (most recent as of 2018) edition of the Introduction to Information Systems, Rainer et al. (2013, 2017) moved the details on how to build conceptual models (using ER grammar) to the appendix. The 12th edition of the widely popular Laudon and Laudon’s textbook now presents databases in the context of a broader topic on business intelligence (Laudon and Laudon 2017). Bidgoli’s current edition (Bidgoli 2014, 2017) lacks a dedicated section on entity relationship diagrams or any other kind of conceptual modeling grammar. Indeed, the discussion of the relational model only occupies (and not even fully) three pages in that textbook – 51 to 53. All the above-mentioned textbooks (and many other ones) now discuss nonrelational databases, but without consideration of conceptual design for these new data models. These are seriously concerning trends. Notably, these developments remain at odds with repeated claims made by researchers about the importance of conceptual modeling, and issues related to representation of information requirements and application domain knowledge in information systems (Burton-Jones et al. 2017; Jabbari Sabegh et al. 2017; Rai 2017). The question becomes: does conceptual modeling remain a relevant topic to the introductory information systems curriculum? We believe it does, but argue that the way it is featured in the introductory curriculum needs a major overhaul. CRUX OR CURIO: UNDERSTANDING THE PROBLEM

In order to offer an effective solution to the problem of a potential disappearance of conceptual modeling from the introductory curriculum, we need to understand what could be causing it. Despite remaining core to the essence of information systems, conceptual modeling in the information systems curriculum and, to some extent, in research, has been narrowly focused. Traditionally, conceptual models and conceptual modeling activities have been understood in the context of information systems development (Chen 2009; Jabbari et al. 2018; Recker 2015; Woo 2011). Conceptual modeling has been defined rather broadly - as “the activity of formally describing some aspects of the physical and social world around us for the purposes of understanding and communication” (Mylopoulos 1992). However, in research (and textbooks), it has been mainly understood as a process


of creating models by systems analysts at early stages of IS development to express concepts in the domain as viewed by IS users (e.g., decision makers, data consumers). Conceptual models typically then informed the design of database schema. As the most prevalent data model was relational the assumption was that conceptual modeling was either entity relationship diagrams, or similar grammars (e.g., UML). The process was also geared toward the development of a new database (as opposed to collecting data, assessing its quality, integrating or repurposing data). While the task of building new (relational) databases, remains important in organizations, its relative frequency, compared to other activities, has been gradually diminishing over the years. First, relational databases have been on the steady decline with the introduction of NoSQL and other flexible, scalable or distributed storage technologies (e.g., Hadoop, Semantic web). Many NoSQL databases implement flexible (e.g., schema-less) data models (Chang et al. 2008; DeCandia et al. 2007; Pokorny 2013). Similarly, Semantic Web technologies assume flexible data formats (Decker et al. 2000; Ding et al. 2002; Patel-Schneider and Horrocks 2007). In contrast to the relational databases, and corresponding conceptual modeling, there is no established approach for conducting conceptual modeling for these new, flexible and distributed technologies (Bugiotti et al. 2014; Kaur and Rani 2013; Lukyanenko et al. 2017). This makes it challenging for textbooks to present a single conceptual modeling solution that would be as useful and universal for these technologies, as the entity relationship diagrams were for building databases between 1970s and 2000s. Second, textbooks need to make room for many exciting new developments that transform organizations and societies, and ostensibly have little to do with conceptual modeling. The following are some of the new topics that have been introduced in the introductory MIS textbooks in the past five years:        


business intelligence and analytics cloud computing social networks and social media mobile and social commerce wireless, wearable and mobile computing virtual reality wireless security cryptocurrency and blockchains

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At least on the surface, many of these topics are quite remote from conceptual modeling when it is narrowly understood as an activity focusing on building new (relational) databases. Considering the indisputable social value of these technologies, it is understandable that a textbook author may choose to shrink the section on entity relationship diagrams to make room for social media analytics or wireless security. Considering these developments, it may be reasonable to consider shrinking the space dedicated to conceptual modeling in IS textbooks. We believe, however, that this would miss an enormous opportunity in the information systems discipline. MODELING BROADLY UNDERSTOOD

Before advancing specific arguments, it is important to re-acknowledge the assumption that conceptual modeling, as an activity, remains core to the information systems field. This assumption is important, as we do not wish to cling to something that could very well be a curious relic from the past whose continuing presence in the curriculum comes at the expense of more relevant and progressive topics. We believe, conceptual modeling remains underappreciated in the introductory curriculum, but also, surprisingly, to some degree, in the academic community as well. We see a path forward in communicating with greater clarity the promise of conceptual modeling, and demonstrating this promise in a broader range of applications. A tool that bridges worlds

Even traditionally, conceptual modeling was never an activity only about building new (relational) databases (Bajaj et al. 2005; Borgida et al. 2009; Browne and Parsons 2012; Eriksson et al. 2018; Guizzardi and Halpin 2008; Henderson-Sellers 2015; March and Allen 2012; Mylopoulos 1998; Topi and Ramesh 2002; Wand and Weber 2002). Conceptual models have always been tools that bridged worlds. This broader conceptualization of conceptual modeling is vital for this topic to remain relevant. Building an information technology is a complex undertaking. Many people with different backgrounds, beliefs, expertise and training are involved in this process. Conceptual models always acted as boundary objects (Mark et al. 2007). They were frequently used by non-technical business users as well as the development team to ensure


understanding of what the system needs to do, and more broadly, of the application domain relevant to the system (Dobing and Parsons 2006; Fettke 2009). Conceptual modeling research has demonstrated many benefits of models for domain understanding and comprehension (Burton-Jones et al. 2017; Khatri et al. 2006; Recker et al. 2011; Saghafi and Wand 2014; Samuel et al. 2018; Topi and Ramesh 2002). Conceptual models further structured and organized the domains. They are commonly used to abstract the relevant aspects of the domain focusing the development team (and other users) on the relevant, essential aspects of the application domain (Olivé 2007; Smith and Smith 1977). More recent research empirically demonstrated the power of conceptual modeling assumptions to structure and organize data collection and focus information systems users on the relevant aspect of the domain (Lukyanenko et al. 2014a, 2017). The ability of conceptual models to bridge the worlds and organize reality, remains valuable for most activities related to information systems development and, critically, use. Indeed, research has pointed to the value of conceptual models (or, more generally, approaches to representation of reality) for increasing information quality, including in the trendy domains of social media, crowdsourcing and effective use of information systems within organizations (BurtonJones and Grange 2012; Burton-Jones and Volkoff 2017; Kallinikos and Tempini 2014; Lukyanenko et al. 2014b, 2017; Lukyanenko and Parsons 2018). The field of machine learning and business analytics appears to be quite analogous to IS development in that it too seeks to represent reality, and thus may be supported with conceptual models. Indeed, research on machine learning has been exploring ways to bring domain knowledge into the training and learning process so that (some) rules do not have to be learned from training samples (Jones 2014; Tsymbal et al. 2007; Zhou and Chaovalit 2008). Likewise, a blockchain offers its own, ingenious, way of representing reality. Conceptual modeling research has always been influenced by, and projected influence on accounting information systems (Borgida et al. 2009; Mattessich 2013; Weber 1985; Yu 2002). In this regard, blockchain technology appears to be no different from other artifacts in accounting that have benefited from the application of conceptual modeling techniques and methods.


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Lukyanenko More than just data models

Second, although well appreciated in both research and practice, the introductory curriculum has been consistently ignoring conceptual models used outside of the data management space. In addition to data models, organizations widely use process models, models of business activity and goals, models of enterprise and systems architecture (see, e.g., Azevedo et al. 2015; Burton-Jones and Weber 2014; Davies et al. 2006; Mylopoulos et al. 1999; Pentland et al. 2017; Recker et al. 2011; Soffer et al. 2008; Taghavi and Woo 2017). However, historically, introductory textbooks have not offered detailed exposition of these models, despite some of them (e.g., BPMN) being as popular as entity relationship diagrams. Below are some existing examples of research on the application of conceptual modeling diagrams and conceptual modeling concepts beyond database design:       

  

capturing and representing project or information systems requirements (Castro et al. 2002); tactical and strategic planning (Gu et al. 2015; Monu and Woo 2009; Paja et al. 2016; Woo 2011); business problem-solving (Gu et al. 2015; Woo 2011); business process improvement, process management, change management (Kummer et al. 2016; Recker 2010); knowledge management (Jurisica et al. 2004; Loucopoulos and Kavakli 1999; Maedche et al. 2003); evaluating and increasing quality of data stored in IS (Lukyanenko et al. 2014b; Wand and Wang 1996; Wang et al. 1995); support of machine learning, business analytics, business intelligence and intelligent agents (Castellanos et al. 2017; Gu et al. 2015; Lukyanenko et al. 2018; Monu and Woo 2005); design of data collection processes and interfaces (Lukyanenko et al. 2017; Lukyanenko and Parsons 2018); effective use of information systems (BurtonJones and Grange 2012); data visualization (Frisendal 2016; Nativi et al. 2004);


     

data and systems integration (Jensen et al. 2003; Muñoz et al. 2009; Vergara et al. 2007); structuring and organization of informational resources (Niederman and Lukyanenko 2018; Parsons and Wand 2008); social network analysis (Pereira et al. 2013); design of recommendation technologies (Guizzardi et al. 2007); protecting privacy (Cysneiros and do Prado Leite 2004; Mont et al. 2010); improving digital security (Cysneiros and do Prado Leite 2004; D’aubeterre et al. 2008).

While the list above is not exhaustive, it demonstrates the broad applicability of conceptual models and modeling to the introductory information systems curriculum. The Language of IS

We further argue that conceptual modeling grammars constitute the language of information systems. When disciplines mature, they typically develop their own theories, vocabulary, but also own languages (Hoyningen-Huene 2013; Lukyanenko and Darcy 2016). A poster child example of discipline-specific language is the language of mathematics, which also became the language of physics and other natural sciences. The fields of computing have developed own languages (e.g., mathematics, predicate calculus, C++, Java, Python, R). Assuming the essence of information systems lies at the intersection of people and technologies (Gregor 2006; Lee 1999; Leonardi and Barley 2008), one invariably reaches the conclusion that if our discipline were to have own, unique language, it would have to be a language at the intersection of humans and systems. In other words, it would have to be a language that is capable to bridge the world of humans with the world of machines. Conceptual modeling is that language. Accessibility. Computer science developed many languages that humans use to interact with machines, including the basic binary conversion tables. Most, if not all languages in computer science are not easily accessible to non-technical audiences. In contrast, conceptual modeling languages by design sought to be accessible and intuitive due to their combination of textual and diagrammatic representations (Mayer and Moreno 2003; Moody 2009).


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The second reason why conceptual models are more accessible than typical programming languages is their human orientation. Conceptual models, while typically intended for some information systems task, are the models of reality “as perceived by humans” (Burton-Jones et al. 2017, p. 1310). They are closer to the world of humans (i.e., to human cognitive and conceptual faculties) then to machines – the idea that was recognized in the first conceptual modeling grammars (Chen 1976) and the one that guided conceptual modeling ever since (Browne and Parsons 2012; Moody 2009). Unlike programming code, conceptual models are machine-facing, not machinetouching. Doman-orientation. Conceptual models, while information systems artifacts, are typically models of the application domain, and are typically agnostic of specific implementation issues (Jabbari et al. 2018). Wand and Weber (1995) called conceptual modeling scripts the first information systems-oriented scripts i.e., the first in a series of scripts (e.g., database schema, programming code) that transform human view of reality into machine executable code. Conceptual models are the closest of the formal information systems representations to the application domains. As implementation considerations may introduce additional information into representational scripts (consider the discussion on semantically void elements in Parsons and Cole 2005), conceptual models remain a reliable source of domain knowledge not confounded by other (extraneous to the domain knowledge) elements and concerns. Any inquiries into the application domain knowledge needed during the development or use of information systems, therefore, would be faithfully supported by conceptual models (rather than, for example, database schema, user interface or programming code). These two properties – i.e., accessibility and domain orientation - make conceptual modeling uniquely suitable as the language of information systems. This also implies that an introductory textbook would be incomplete and seriously deficient if this language is not part of it. MODELING AS A WAY OF THINKING

Problems of technology design and use are complex. The complexity stems from the need to devise an innovative solution in the context of multifaceted organizational dynamics and intricate human psychology. Conceptual modeling brings a way of thinking, akin to the kind routinely cultivated in


standard architecture or engineering curriculum. Architectural students incessantly build models of all sorts - large and small scale, models of buildings and their components, large spatial models as well as models of plumbing and other fixtures. In doing so, the students perfect the skill of projecting a complex problem into a more abstract modeling space that then drives concrete subsequent solutions. The same need is very acute in information systems development, design and use. But the appreciation of what conceptual modeling can do as a way of thinking, problem formulation and communication remains less mature compared to other similar disciplines. PATH FORWARD

We do not wish to place the blame on the authors of textbooks. Instead, we believe, the responsibility for better communicating the value of conceptual modeling rests with the research community. As a way forward, we hope to catalyze debate and action in the academic community on ways to enrich the introductory MIS curriculum with conceptual modeling ideas, techniques and methods. The story of conceptual modeling occupied a prominent and glorious chapter in the information systems discipline (Hirschheim and Heinz 2010). But the rapidly exploding landscape of information technologies puts this topic at crossroads. We strongly believe conceptual modeling has much to offer to the new technologies that are being rapidly incorporated into the introductory MIS textbooks. We hope that, guided by more research, these new topics can be enriched with the discussion of conceptual modeling, now broadly understood. ACKNOWLEDGMENTS

The ideas presented in this paper have benefited tremendously from the many long and fruitful conversations with friends and colleagues who are equally passionate about conceptual modeling. I especially wish to thank the reviewers for their excellent suggestions, as well as Jeffrey Parsons, Yair Wand, Binny Samuel, Carson Woo, Jan Recker, Andrew Burton-Jones, Veda Storey, Kafui Monu, Kai Larsen and Dinesh Batra for shaping my opinions expressed in this paper.

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