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We survey and organize over fifty years of theoretical research on status and expectation state processes. After defining some key terms in this theoretical approach, we briefly describe theories and branches in the program.

We also focus on a few theories that illustrate distinct patterns of theory growth, using them to show the variety of ways in which the research program has grown.

The program structure developed from a single set of theories on development and maintenance of group inequality in the 1960s to six interrelated branches by 1988. Between 1988 and today, the overall structure has grown to total 19 different branches. We briefly describe each branch, identifying over 200 resources for the further study of these branches.

Although the various branches share key concepts and processes, they have been developed by different researchers, in a variety of settings from laboratories to schools to business organizations. Second, we outline some important issues for further research in some of the branches. Third, we emphasize the value of developing new research methods for testing and applying the theories.

These theories have been used to explain phenomena of gender, racial, and ethnic inequality among others, and for understanding some cases of personality attributions, deviance and control processes, and application of double standards in hiring.

Status and expectation state processes often operate to produce invidious social inequalities. Understanding these processes can enable social scientists to devise more effective interventions to reduce these inequalities.

Status and expectation state processes occupy a significant segment of research into group processes. This chapter provides an authoritative overview of ideas in the program, what is known, and what remains to be discovered.

Purpose – For decades, archivists have been appraising, preserving, and providing access to digital records by using archival theories and methods developed for paper records. However, production and consumption of digital records are informed by social and industrial trends and by computer and data methods that show little or no connection to archival methods. The purpose of this chapter is to reexamine the theories and methods that dominate records practices. The authors believe that this situation calls for a formal articulation of a new transdiscipline, which they call computational archival science (CAS).

Design/Methodology/Approach – After making a case for CAS, the authors present motivating case studies: (1) evolutionary prototyping and computational linguistics; (2) graph analytics, digital humanities, and archival representation; (3) computational finding aids; (4) digital curation; (5) public engagement with (archival) content; (6) authenticity; (7) confluences between archival theory and computational methods: cyberinfrastructure and the records continuum; and (8) spatial and temporal analytics.

Findings – Each case study includes suggestions for incorporating CAS into Master of Library Science (MLS) education in order to better address the needs of today’s MLS graduates looking to employ “traditional” archival principles in conjunction with computational methods. A CAS agenda will require transdisciplinary iSchools and extensive hands-on experience working with cyberinfrastructure to implement archival functions.

Originality/Value – We expect that archival practice will benefit from the development of new tools and techniques that support records and archives professionals in managing and preserving records at scale and that, conversely, computational science will benefit from the consideration and application of archival principles.

In this study, we analyze the power of the individual return-to-volatility security performance heuristic (ri/stdi) to simplify the identification of securities to buy and, consequently, to form the optimal no short sales mean–variance portfolios. The heuristic ri/stdi is powerful enough to identify the long and shorts sets. This is due to the positive definiteness of the variance–covariance matrix – the key is to use the heuristic sequentially. At the investor level, the heuristic helps investors to decide what securities to consider first. At the portfolio level, the heuristic may help us find out whether it is a good idea to invest in equity to begin with. Our research may also help to integrate individual security analysis into portfolio optimization through improved security rankings.

We examine patterns and changes in the use of various theoretical perspectives, and in the approach to testing individual or combinations of theories, within the field of international strategy that constitutes one of the major areas of international business (IB) research. We conduct a systematic bibliometric analysis of 22 years’ worth of empirical papers. We generate tabular evidence and introduce the use of network graphing methodology to report and analyse the co-occurrence of theories. We find a changing distribution of theoretical perspectives, indicative of a re-centring of the field around strategic and organizational perspectives. This is accompanied by use of more complex approaches to testing contingencies of the sort likely to result from these theory combinations, especially across firm, interfirm and institutional levels of analysis. We thus generate and discuss critically a quantitative and graphical overview of the progress of international strategy research. This creates unique and comprehensive insights into the development of theory and empirics in IB. We draw lessons for academics and report practical recommendations for the conduct of research. Overall, our study sheds new light on the disciplinary nature of IB research and its interplay with related fields and disciplines. It explicates patterns of theory accretion alongside patterns of theory testing and refinement. It provides a comprehensive map of the field of IB strategy as it evolved since 1990 and illuminates its future.

This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.

The paper describes the conceptual and mathematical background of a tool for modelling and optimising transportation networks. The tool has been created to model resources and cargo flows in port terminal networks. The concept behind the tool is characterised by three features. Firstly the nodes are non-stationary and can be adapted to accommodate changed requirements in the transportation system. Secondly a high degree of complexity is built into the operational modes of the nodes. Thirdly, the concept incorporates an information network interacting closely with nodes and links. All networks can be optimised employing powerful algorithms and tools from system theory, neural networks and combinatorial graph theory.

University libraries have traditionally been the primary caretaker of scholarly resources. However, as electronic modes of information delivery replace print materials, expectations of academic libraries have evolved rapidly. In this environment, academic libraries need to be adaptable organizations. Librarianship, though, is deeply rooted in strong values and beliefs which inherently limit receptivity to change and innovation, but these constraints are not absolute. Social network research indicates that professional advice networks play a significant role in how one thinks about and performs work and that individual perspectives are broadened when diverse input is received. Based on social network analysis methods, this study explored the relationship between individual receptivity to innovation and the composition of a person's professional advice network through a purposive sample of academic librarians in Illinois. The group completed a survey that explored two dimensions: (1) the nature of relationships within their professional advice network and (2) the individual's personal receptivity to innovation. Analysis of the nature of relationships within the professional advice networks was based on a combination of quantitative and qualitative techniques, in contrast to the analysis of the respondents’ receptivity to innovation which was based on quantitative measures. Based on the information from the 440 respondents, the results of this research indicate that there is a relationship between the size of the professional advice networks and individual's receptivity to innovation, but additional aspects of the professional advice network may play a role in an individual's overall receptivity to innovation.

Standard financial risk management practices proved unable to provide an adequate understanding and a timely warning of the financial crisis. In particular, the theoretical foundations of risk management and the statistical calibration of risk models are called into question. Policy makers and practitioners respond by looking for new analytical approaches and tools to identify and address new sources of financial risk. Financial markets satisfy reasonable criteria of being considered complex adaptive systems, characterized by complex financial instruments and complex interactions among market actors. Policy makers and practitioners need to take both a micro and macro view of financial risk, identify proper transparency requirements on complex instruments, develop dynamic models of information generation that best approximate observed financial outcomes, and identify and address the causes and consequences of systemic risk. Complexity analysis can make a useful contribution. However, the methodological suitability of complexity theory for financial systems and by extension for risk management is still debatable. Alternative models drawn from the natural sciences and evolutionary theory are proposed.