Assessing architectural evolution: A case study

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2011 SpringerThis paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach

Learning in Strategic Alliances

{Excerpt} Strategic alliances that bring organizations together promise unique opportunities for partners. The reality is often otherwise. Successful strategic alliances manage the partnership, not just the agreement,for collaborative advantage. Above all, they also pay attentionto learning priorities in alliance evolution. The resource-based view of the firm that gained currency in the mid-1980s considered that the competitive advantage of an organization rests on the application of the strategic resources at its disposal. These days, orthodoxy recognizes the merits of the dynamic, knowledge-based capabilities underpinning the positions organizations occupy in a sector or market. Strategic alliances—meaning cooperative agreements between two or more organizations—are a means to enhance strategic resources: self-sufficiency is becoming increasingly difficult in a complex, uncertain, and discontinuous external environment that calls for focus and flexibility in equal measure. Everywhere, organizations are discovering that they cannot “go” it alone and must now often turn to others to survive

Autism research : An objective quantitative review of progress and focus between 1994 and 2015

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH's Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.Peer reviewedFinal Published versio

Human Resources Strategy: The Era of Our Ways

The purpose of this chapter is to discuss some of the main features and trends in human resources (HR) strategy. Inasmuch as people are among the most important resources available to firms, one could argue that HR strategy should be central to any debate about how firms achieve competitive advantage. But this “people are our most important asset” argument is actually fairly hollow in light of the evidence. Far too many articles on HR start with this premise, but the reality is that organizations have historically not rested their fortunes on human resources. The HR function remains among the least influential in most organizations, and competitive strategies have not typically been based on the skills, capabilities, and behaviors of employees. In fact, as Snell, Youndt and Wright (1996:62) noted, in the past executives have typically tried to “take human resources out of the strategy equation--i.e., by substituting capital for labor where possible, and by designing hierarchical organizations that separate those who think from those who actually do the work.

Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory

Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.Comment: 10 pages, 6 figures, 2 table