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Although a significant body of work has amassed that explores the antecedents, correlates, and consequences of employee turnover in organizations, little is known about how employees go about quitting once they have made the decision to leave. That is, after the decision to voluntarily quit their job is made, employees must then navigate through the process of planning for their exit, announcing their resignation, and potentially working at their company for weeks after their plans to resign have been made public. Our lack of understanding of the resignation process is important as how employees quit their jobs has the potential to impact the performance and turnover intentions of other organizational members, as well as to harm or benefit the reputation of the organization, overall. Moreover, voluntary turnover is likely to increase in the coming decades. In this chapter, we unpack the resignation process. Specifically, drawing from the communication literature and prior work on employee socialization, we develop a three-stage model of the resignation process that captures the activities and decisions employees face as they quit their jobs, and how individual differences may influence how they behave in each of these three stages. In doing so, we develop a foundation upon which researchers can begin to build a better understanding of what employees go through after they have decided to quit but before they have exited their organization for the final time.

Did the expansion of democratic institutions play a role in determining central government spending behavior in the 19th and 20th centuries? The link between democracy and increased central government spending is well established for the post-Second World War period, but has never been explored during the first “wave of democracy” and its subsequent reversal, that is 1870–1938. The main contribution of this paper is the compilation of a dataset covering 24 countries over this period to begin to address this question. Utilizing various descriptive techniques, including panel data regressions, we explore correlations between central government spending and the institutional characteristics of regimes. We find that the data are consistent with the hypothesis that democracies have a broader need for legitimization than autocracies as various measures of democracy are associated with higher central government spending. Our results indicate that the extension of franchise had a slight positive impact on central government spending levels, as did a few of the other democracy variables. We also find that early liberal democracies spent less and monarchies more than other regimes; debt increases spending; and participation in the Gold Standard reduced government spending substantially.

INSTRUCT is a multi‐user, text retrieval system which was developed as an interactive teaching package for demonstrating modern information retrieval techniques, these including natural language query processing, best match searching and automatic relevance feedback based on probabilistic term weighting. INSTRUCT has recently been extended and now additionally has facilities for query expansion using both relevance and term co‐occurrence data, for cluster‐based searching and for two browsing search strategies. These retrieval mechanisms are used to search a file of 26,280 titles and abstracts from the Library and Information Science Abstracts database; both menu‐based and command‐based searching are allowed.

This chapter identifies the attributes that learners need in order to learn effectively in new technology rich educational environments. There are a number of different ways of synthesising the findings from this emerging literature which relies heavily on qualitative research. This chapter reports on a literature review which adopted a deliberately interpretative qualitative meta-analysis, synthesising the findings from 15 key studies. As such, the chapter demonstrates a way of reviewing and compiling current research. The synthesis resulted in the identification of six attributes that learners need to do well in next generation learning spaces. These are engaged, connected, confident, adaptable, intentional and self-aware. Although some of these attributes are applicable to all learning contexts, those of being connected, confident, adaptable, and intentional seem to be particularly important in learning in next generation learning spaces. The challenge is to design learning activities that encourage and reward the development of these attributes. The hope is that through both its findings and its method, this chapter provokes debate on what it now means to be a successful learner in today’s technology rich world.

Computer-aided/assisted qualitative data analysis software (CAQDAS) supports qualitative and mixed methods researchers to organize, analyze, and explore data in a meaningful, and efficient, way. Successfully utilizing CAQDAS software can be challenging, particularly for the novice researcher. To assist all researchers 21 CAQDAS dilemmas are articulated. These relate to choosing, using, and getting started with the software, as well as writing about CAQDAS use. These dilemmas suggest there is no right way to use CAQDAS programs, rather the specific research project, along with researcher experience and philosophy, should drive the extent to which any project utilizes the extensive CAQDAS capabilities, while also encouraging the researcher(s) to drive their ideas and exploration beyond what they initially thought possible.

The increasing flood of documentary information through the Internet and other information sources challenges the developers of information retrieval systems. It is not enough that an IR system is able to make a distinction between relevant and non‐relevant documents. The reduction of information overload requires that IR systems provide the capability of screening the most valuable documents out of the mass of potentially or marginally relevant documents. This paper introduces a new concept‐based method to analyse the text characteristics of documents at varying relevance levels. The results of the document analysis were applied in an experiment on query expansion (QE) in a probabilistic IR system. Statistical differences in textual characteristics of highly relevant and less relevant documents were investigated by applying a facet analysis technique. In highly relevant documents a larger number of aspects of the request were discussed, searchable expressions for the aspects were distributed over a larger set of text paragraphs, and a larger set of unique expressions were used per aspect than in marginally relevant documents. A query expansion experiment verified that the findings of the text analysis can be exploited in formulating more effective queries for best match retrieval in the search for highly relevant documents. The results revealed that expanded queries with concept‐based structures performed better than unexpanded queries or Ñnatural languageÒ queries. Further, it was shown that highly relevant documents benefit essentially more from the concept‐based QE in ranking than marginally relevant documents.

Although not new in the strictest sense of the word, mention must be made of airless spraying because of the rapidly increasing interest now being shown in this process. This is due to the fact that it is proving to be one of the quickest and most economical methods of applying paint over large areas yet devised. Indeed, the limiting factor in the speed of application is most often the speed at which the operator can work: at its maximum, even the smallest airless unit will deliver well over a quart of paint a minute!

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.