The prolonged COVID-19 pandemic, economic stress, and geopolitical tensions have caused market disruptions and other forces that have likely increased organizational agility. This article focuses on the antecedents of organizational agility under such business uncertainty in the noninformation technology (IT) sectors. The research model stems from the uncertainty reduction theory and the following three frameworks: (1) dynamic capabilities; (2) decision making; and (3) business intelligence and analytics (BI&A) competitive advantage maturity model. It considers intelligence (risk and opportunity) and aligned decision making as agility predictors. It lists employee capability and IT flexibility as antecedents of intelligence, aligned decision making, and organizational agility. The results indicate that employee capability affects agility through the mediating variables of intelligence and aligned decision making. IT flexibility impacts agility only through intelligence. Both intelligence and aligned decision making have significant direct effects on agility.

This research note suggests five research challenges when conducting quantitative studies on large-scale agile methodology (LSAM). First, the LSAM empirical literature, which is mainly characterized by qualitative studies primarily focusing on coordination issues, provides limited background. Second, the notion of “large” in LSAM needs to be clarified because the existing research seems to have focused on “very large” or outlier projects. Third, the popular LSAM methods suggest broad and general maxims that may result in difficulty in operationalizing dependent variables, especially in innovation adoption studies. Fourth, the researcher may get overwhelmed when selecting independent variables from the plethora of suggested constructs. Finally, some of the problems associated with large-scale agile are mostly challenges of using conventional agile during a time-period when LSAM had not formally emerged. Researchers should take a balanced approach considering both benefits and challenges of using LSAM and focusing on project-level dependent measures such as success and acceptance.

Business surveys indicate that fewer than 30% of data warehousing and business intelligence (DW/BI) projects meet the stated goals of the budget, schedule, and quality. Agile methods have been suggested as a possible solution, but because of the large size of the typical DW/BI project, it may be difficult to apply the agile values and principles. In this article, the following research questions are raised: Can agile practices be adapted for DW/BI development? What factors influence agile DW/BI development? Six semi-structured interviews were conducted using a questionnaire. The interview transcripts were coded using the grounded theory approach. Eight categories emerged from the analysis: business value, project management, agile development, shared understanding, technological capability, top management commitment, complexity, and organizational culture. Based on the categories, a research framework is proposed. The findings reveal that agile methods are suited for only certain aspects of DW/BI projects and need to be augmented with project management practices.

Agile software development generally refers to popular practices that are supposed to adhere to the Agile Manifesto with its values and principles. Empirical studies on agile software development have mainly focused on organizational adoption and impacts of agile practices. Furthermore, the literature on agile software development has mostly centered on small, co-located projects. However, agility is needed for software development projects of varied sizes in different organizations across industries. The general nature of agile values and principles and the procedure-driven nature of specific agile methods make it difficult for organizations to determine what they can do to effectively facilitate agility in their software development process. To bridge that literature gap and based on an evolved grounded-theory approach, this study identifies nine agility facilitators and their corresponding dimensions that extend beyond small, co-located projects to software projects of any size and distribution. These agility facilitators are further grouped into two categories: organizational foundation and project processes. In addition, the authors identify four dimensions of agility. The authors propose a framework that describes the organizational mechanisms through which the nine categories of facilitators lead to software development agility.

Previous information systems (IS) research has established that novice database designers experience cognitive overload when faced with many interacting entities in modeling relationships. The authors contend that this problem occurs mainly when events are involved. Results of an initial study provide support that novice database designers indeed have difficulty recognizing and modeling events. They propose two techniques that can address the difficulties encountered by novices when modeling events using the entity-relationship model. Entity-relationship event network (EREN) is a top-down and template-driven technique. Event-based logical relational design (ELRD) is a bottom-up and heuristic-driven technique. Employing the cognitive load theory (CLT) to guide the hypotheses, the authors compare the usability of EREN and ELRD for novice designers. Results indicate that both techniques facilitate satisfactory designer performance when modeling events. Overall, the ELRD technique leads to better designer performance. There is an interaction effect between technique and task complexity as the significant performance advantage of ELRD at the lower-complexity task gets mitigated at the higher-complexity task. The two techniques do not differ significantly on the constructs of behavioral intention to use, perceived usefulness, perceived ease of use, and self-efficacy. Overall, the ELRD technique is recommended for modeling events.

The Unified Modeling Language (UML) has become the de facto standard for object-oriented software development. It has been widely adopted in both training and practice. However, UML has often been criticized for being overly complex and difficult to learn for novice analysts. Although some research studies have identified specific novice difficulties in learning UML, there is little research proposing viable techniques for addressing these difficulties. In particular, there is a lack of research evaluating the usability of the sequence diagram (SD), which models the interactions among objects of a software application. This paper reports a research study that proposes a technique called “CHOP” (CHunking, Ordering, Patterning), which is designed to improve novice analyst performance in modeling an SD. The CHOP technique is based on the Cognitive Load Theory (CLT) and was developed by addressing the three types of cognitive load encountered by novices. An experimental study testing the efficacy of the CHOP technique in comparison to the worked-example approach indicated that the CHOP technique significantly improves novice analyst’s ability to model interactions among objects; however, the worked-example technique was the more efficient during training. The study also found that subjects using the CHOP technique rated its perceived usefulness significantly higher than subjects using the worked-example approach.

The Resource-Event-Agent (REA) model has been proposed as a data modeling approach for representing accounting transactions. However, most business events are not transactions; thus, the REA formulation is incomplete. Based on the Conceptual Semantics theory, this paper discusses the entity-relationship event network (EREN) model, which extends the REA model and provides a comprehensive data template for a business event. Specifically, the notions of resource, event, and agent in the REA model are extended to include more discriminating entity types. The EREN technique can be used to identify events, sketch a network of events, and develop a data model of a business application by applying the EREN template to each event. Most extant techniques facilitate only the descriptive role whereas the EREN technique facilitates both the design and descriptive role of data modeling.

The article evaluates the feasibility of extending agile principles to larger, dynamic, and possibly distributed software development projects by uncovering the theoretical basis for agile values and principles for achieving agility. The extant literature focuses mainly on one theory – complex adaptive systems – to support agile methods, although recent research indicates that the control theory and the adaptive structuration theory are also applicable. This article proposes that at least three other theories exist that are highly relevant: transaction cost economics, social exchange theory, and expectancy theory. By employing these theories, a rigorous analysis of the Agile Manifesto is conducted. Certain agile values and principles find theoretical support and can be applied to enhance agility dynamic projects regardless of size; some agile principles find no theoretical support while others find limited support. Based on the analysis and the ensuing discussion, the authors propose a framework with five dimensions of agility: process, design, people, outcomes, and adaptation.

The tremendous demand for software productivity has led to the idea of reuse of solutions that have worked successfully in the past. The notion of a design pattern is now well accepted in software design, and research in the area of data modeling has also begun. Although two books have explicitly attempted to cover this area, the representations provided in the books seem to be focused on specific applications and do not provide a generic and comprehensive set of templates. Another book attempts to address the problem but provides patterns at a level of granularity too small to be useful. This paper teases out underlying structures that tend to occur frequently in these books and provides patterns at an abstract and more useful level of granularity. It describes 11 data modeling patterns commonly found in business scenarios. The patterns are then validated by checking the frequency of occurrence of each pattern in the data representations included in three comprehensive texts of reference models. Two of these sources are targeted mainly at practitioners, and the third is academic oriented and targeted at students learning data modeling. Results indicate that although certain patterns are used more frequently than others, most of the 11 structures occur with adequate frequency to qualify as patterns. A comparison reveals that the frequency distribution of patterns is different among these sources. Further, the academic-oriented source distinctly focuses on different patterns as compared to the other two sources. The paper discusses the differences and provides specific recommendations on improving pedagogy in conceptual data modeling.