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The advent of information technology has generated not only interest in how to acquire, store and “mine” data, but also how to manage knowledge. Yet, there is still considerable confusion and a lack of understanding of what today’s knowledge managers really do. Continuing a stream of previous research on the behavior activities of traditional managers, this study investigated the relative amount of time today’s knowledge managers (N=307) spend on traditional management functions, communications, human resources and networking. Besides identifying what knowledge managers really do, this study examined what successful knowledge managers do. Comparisons are then made with managers in the 1980s. Finally, the role that information technology plays in knowledge managers carrying out their managerial activities was assessed. The implications of some surprising findings and conclusions end the paper.

Entrepreneurship is an activity with far-reaching economic and cultural implications. Research seeking to understand the cognition and behavior of entrepreneurs is devoting increasing attention to how entrepreneurs construct and utilize discourse. However, word-level analysis of the specific language used by entrepreneurs has not received significant attention. The purpose of this paper is to identify the words that comprise entrepreneurship discourse and describe how word-usage has changed over time.

To examine the language of entrepreneurship, the authors use modified MapReduce algorithms in conjunction with text mining techniques to construct a longitudinal corpus of approximately 2.5m words. The authors identify the most frequently used words in the entrepreneurship lexicon and then use content analysis to chart the evolution of word-use.

The findings reveal that entrepreneurs’ lexicon is complex and fluid. The most commonly used words suggest new trends in entrepreneurship.

The findings and methodological procedures contribute to research on entrepreneurs and the entrepreneurial process and, specifically, to work on entrepreneurial discourse, language-use and new venture communication. The findings also have implications for entrepreneurs and policymakers.

An emerging research stream focuses on the place-based ecosystems where artificial intelligence (AI) innovations emerge and develop. This literature builds on the contextual turn in management research and, specifically, work on entrepreneurial ecosystems. However, as a nascent research area, the literature on AI and entrepreneurial ecosystems is fragmented across academic and practitioner boundaries and unconnected disciplines because of disparate and ill-defined concepts. As a result, the literature is disorganized and its main insights are latent. The purpose of this paper is to synthesize research on AI ecosystems and identify the main insights.

We first consolidate research on the “where” of AI innovation through a scoping review. To address the fragmentation in the literature and understand how entrepreneurial ecosystems are associated with AI innovation, we then use content analysis to explore the literature.

We identify the main characteristics of the AI and ecosystems literature and the key dimensions of “AI entrepreneurial ecosystems”: the local actors and factors in geographic territories that are coordinated to support the creation and development of AI technologies. We clarify the relationships among AI technologies and ecosystem dimensions and uncover the latent themes and underlying structure of research on AI entrepreneurial ecosystems.

We increase conceptual precision by introducing and defining an umbrella concept—AI entrepreneurial ecosystem—and propose a research agenda to spur further insights. Our analysis contributes to research at the intersection of management, information systems, and entrepreneurship and creates actionable insights for practitioners influenced by the geographic agglomeration of AI innovation.

Total quality management (TQM) and business process re‐engineering (BPR) are two management approaches designed to improve organizational performance and quality. Because of the pace, time required, and initiatives for change, TQM and BPR are regarded as two completely different improvement programmes. They also share common features, which create the basis for a possible integration of TQM and BPR under the endless quality improvement concept. Organizations that can effectively combine TQM and BPR would be very successful in gaining a sustainable competitive advantage. In the context of the system approach, introduces a BPR life cycle model. Answers two main questions: when should a re‐engineering programme be undertaken?; and how long should the period of gradual improvement last before a new re‐engineering programme is implemented? Provides an implementation example of this model through a real organization.