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The purpose of this paper is to overcome the confusion generated by a loose definition of the term knowledge artifact (KA) and its impacts on the design of technologies supporting their use.

The paper looks at the conceptual foundations underpinning the concept of KA that are related to the way in which knowledge is conceived, and revisits the outcomes of empirical investigations to shed light on different aspects of the use of KA in various settings.

The paper identifies a class of KAs and its role in relation to other classes of KAs, as it emerges from the empirical investigations.

The focus is on documental artifacts that are, however, widely used in different domains and organizations. New empirical work is needed to consider other kinds of artifacts and their role in knowledge-intense activities.

The paper aims to drive the attention of the designer on phenomena that hinder the acceptance, appropriation and effectiveness of the technologies they design to support a crucial aspect of collaboration.

The paper is original in the following ways: first, documenting the interplay between a kind of KA that is poorly considered in the literature with other classes of KAs; second, highlighting a set of principles that should guide the construction of computational KAs of a different nature.

The aim of this paper is to examine empirically the relevance of the theory of deferred action for knowledge management systems (KMS) design in practice.

The study adopts a case study approach to examine knowledge work and knowledge management in virtual teamwork in a large UK telecommunications company to understand the occurrence of emergent knowledge and how it is managed by virtual team leaders. The section in the company studied is described as a “knowledge intensive organization” dealing with the company's e‐commerce activities.

The analysis confirms the complex adaptive system hypothesis – a complex system adapts to its environment through self‐organization. The data reveal the behaviour of the virtual team to be self‐organizing and adaptive to its environment. It confirms the knowledge tacitness hypothesis and social embeddedness of knowledge hypothesis as important determinants of knowledge sharing. Specifically, the data reveal the main issues concerning knowledge sharing practices of virtual team workers and the crucial team leader's role in the effectiveness of the teams' capability to develop social links to externalise and share tacit knowledge to accomplish tasks.

In this paper, the authors contribute “emergent knowledge” as a third category of organizational knowledge in addition to the existing tacit and explicit knowledge that needs to be considered when designing KMS. It also derives socio‐technical systems design principles based on the theory of deferred action, and a tentative development process with metrics is then proposed for KMS design that caters for emergent, tacit, and explicit knowledge.

Existing models such as the SECI model do not acknowledge emergent knowledge or its conversion into explicit knowledge. The theory of deferred action is invoked to derive design principles, termed deferred systems design principles, to depict how explicit knowledge, tacit knowledge, and emergent knowledge can be represented to design knowledge management systems for “emergent organizations”.

The study contributes to the limited research and lack of consideration of emergent knowledge as an integral part of organizational knowledge, especially in an era of emergent organizations.

Many organizations are developing e‐mail mediated help services, although the implications of using e‐mail for client service are not yet fully understood. A qualitative study of a successful service was developed incorporating content analysis of service logs and interviews with staff and users. Two models of ideal service exchanges emerge: concise question/response dyads and extended dialog. Staff tend to consider dialog typical, while users almost exclusively consider the minimum exchange normal. Service logs show most exchanges are simple question/answer pairs where users explicitly request instructions, explanations, brief informational answers, or direct intervention by staff. However, users sometimes underspecify their request or omit needed information while staff often respond incompletely to queries. This frequent omission of information places significant stresses on a dyadic exchange model. As users become more experienced in the using e‐mail for requesting service, broader acceptance and use of a dialog model of help provision might occur.

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.

The burnout, stress, and work-life balance challenges faced by teachers have received renewed interest due to the myriad disruptions and changes to K-12 schooling brought about by the COVID-19 pandemic. Even prior to the pandemic, relatively little was known about teachers' time use outside of the classroom, the blurring of work and home boundaries, and how teachers compare to similar professionals in these regards. We use daily time-diary data from the American Time Use Survey (ATUS) for 3,168 teachers and 1,886 professionals in similarly prosocial occupations from 2003 to 2019 to examine occupational differences in time use. Compared to observationally similar non-teachers, teachers spend significantly more time volunteering at their workplace and completing work outside the workplace during the school year. On average, teachers spend 19 more minutes working outside of the workplace on weekdays than observably similar non-teachers and 38 more minutes on weekends. The weekend disparity is particularly large among secondary school teachers. This suggests that before the widespread switch to online and hybrid learning necessitated by the COVID-19 pandemic, teachers were already navigating blurrier work-life boundaries than their counterparts in similar professions. This has important implications for teacher turnover and for the effectiveness and wellness of teachers who remain in the profession.

The purpose of the current study is to identify the nature, scope and locus of and to systematize, the conceptual contradictions existing in dynamic capabilities research.

This paper employs a content analysis literature review of 86 papers on dynamic capabilities published between 1997 and 2019, using two databases – EBSCO and Web of Science/Knowledge databases. To structure the review, Smith and Lewis's (2011) categorization of organizational tensions is adopted.

First, the findings of this study suggest that DCs not only are shaped by a tension between efficiency and flexibility but also are built upon a large number of contradictory aspects, represented by organizing, performing, belonging and learning paradoxes. Second, the analysis identifies defensive and active responses to these tensions, with the former prevailing in the dynamic capabilities view literature. Both kinds of responses may facilitate or hinder organizational change. Third, it was found that while the literature has focused predominantly on organizing and learning paradoxes, the linkage between these categories remains under-researched.

This study makes three contributions. First, it identifies the scope (i.e. number), locus (analytical level) and nature (paradox categories and sub-categories) of DC-related paradoxes and responses to paradoxical tensions. Second, it shows that the nature and locus of conceptual contradictions are more complex than conceptualized in prior studies, going beyond the contingency and ambidexterity argument of how to deal with DC-related paradoxes. Third, it seeks to extend Di Stefano et al.'s (2014) proposition of integrating paradoxical views on different DC-related aspects. The idea of “audio console” introduced in this study highlights the interrelation of paradoxes between the categories and across analytical levels.

This study aims to investigate the impact of the macroeconomic environment on South African industrial sector returns.

Using standardized coefficients derived from time-series factor models, the authors quantify the impact of macroeconomic influences on industrial sector returns. The authors analyze the structure of the resultant residual correlation matrices to establish the level of factor omission and apply a factor analytic augmentation to arrive at a specification that is free of omitted common factors.

The authors find that global influences are the most important drivers of returns and that industrial sectors are highly integrated with the global economy. The authors show that specifications that comprise only macroeconomic factors and proxies for omitted factors in the form of residual market factors are likely to be underspecified. This study demonstrates that a factor analytic augmentation is an effective approach to ensuring an adequately specified model.

The findings have a number of implications that are of interest to investors, econometricians and researchers. While the study focusses on a single market, the South African stock market, as represented by the Johannesburg Stock Exchange (JSE), it is a highly developed and globally integrated market. In terms of market capitalization, it exceeds the Madrid Stock Exchange, the Taiwan Stock Exchange and the BM&F Bovespa. Yet, a limited number of studies investigate the macroeconomic drivers of the South African stock market.

Investors should be aware that while the South African domestic environment, especially political risk, has an impact on returns, global influences are the greatest determinants of returns. No industrial sectors are insulated from global influences and this limits the potential for diversification. This study suggests an alternative set of macroeconomic factors that may be used in further analysis and asset pricing studies. From an econometric perspective, this study demonstrates the usefulness of a factor analytic augmentation as a solution to factor omission in models that use macroeconomic factors to proxy for systematic influences that describe asset prices.

The contribution lies in providing insight into a large and well-developed yet understudied financial market, the South African stock market. This study considers a much broader set of macroeconomic factors than prior studies. A methodological contribution is made by estimating and interpreting standardized coefficients to discriminate between the impact of domestically and internationally driven factors. This study shows that should coefficients not be standardized, inferences relating to the relative importance of factors will differ. Finally, the authors unify an approach of using pre-specified factors with a factor analytic approach to address factor omission and to ensure a valid and readily interpretable specification.