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Purpose – The author introduces the Eastern philosophy of wisdom, especially its epistemology of Yin-Yang Balancing as the Eastern cognitive frame, to shed light on the debates over the distinction and integration between research and practice as well as between qualitative and quantitative methods so as to solve the problems of relevance-rigor gap as well as complexity-simplicity gap. The author also applies the frame of Yin-Yang Balancing to the development of a novel method of case study.

Methodology/Approach – This is a conceptual article.

Central theme – The Eastern philosophy of wisdom is better at an open-minded exploration of open-ended issues by emphasizing relevance and complexity, while the Western philosophy of science is better at a closed-minded exploitation of close-ended issues by emphasizing rigor and simplicity. A geocentric integration of both Eastern and Western philosophies is needed.

Research and practical implications – Management research is far behind the need for theoretical insights into practical solutions largely due to the increasing gaps between relevance and rigor as well as between complex problems and simple solutions. The root cause of the two gaps lies in the overreliance on the Western philosophy of science, so a new light can be found in the Eastern philosophy of wisdom, and the ultimate solution is a geocentric integration of Eastern and Western philosophies. A novel method of case study can be built by applying the Eastern philosophy.

Originality/Value – The author highlights the urgent needs for the Eastern philosophy of wisdom and its integration with the Western philosophy of science toward a geocentric meta-paradigm. As a specific application of the geocentric meta-paradigm, the author proposes a novel method of case study called Yin-Yang Method.

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.

Conventional models for the business valuation of technology are usually financially oriented and only measure economic value. Several of these financially oriented approaches have been reviewed by Leloux and Groen (2007). Current monetary (financial) valuation methods for technology include cost-based methods, income-based methods and market-based methods (Martin, 1999; Goldheim, Slowinski, Joseph, Edward, & John, 2005).

Fuzzy numbers are often used to represent non-probabilistic uncertainty in engineering, decision-making and control system applications. In these applications, fuzzy arithmetic operations are frequently used for solving mathematical equations that contain fuzzy numbers. There are two approaches proposed in the literature for implementing fuzzy arithmetic operations: the α-cut approach and the extension principle approach using different t-norms. Computational methods for the implementation of fuzzy arithmetic operations in different applications are also proposed in the literature; these methods are usually developed for specific types of fuzzy numbers. This chapter discusses existing methods for implementing fuzzy arithmetic on triangular fuzzy numbers using both the α-cut approach and the extension principle approach using the min and drastic product t-norms. This chapter also presents novel computational methods for the implementation of fuzzy arithmetic on triangular fuzzy numbers using algebraic product and bounded difference t-norms. The applicability of the α-cut approach is limited because it tends to overestimate uncertainty, and the extension principle approach using the drastic product t-norm produces fuzzy numbers that are highly sensitive to changes in the input fuzzy numbers. The novel computational methods proposed in this chapter for implementing fuzzy arithmetic using algebraic product and bounded difference t-norms contribute to a more effective use of fuzzy arithmetic in construction applications. This chapter also presents an example of the application of fuzzy arithmetic operations to a construction problem. In addition, it discusses the effects of using different approaches for implementing fuzzy arithmetic operations in solving practical construction problems.

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.

The aim of this chapter is to: (1) model culture as a configuration of multiple values, (2) identify different culture archetypes across the globe, and (3) empirically demonstrate heterogeneity in culture archetypes within and across 52 countries. We use Schwartz values from the World Values Survey (WVS) and the archetypal analysis (AA) method to identify diverse culture archetypes within and across countries. We find significant heterogeneity in culture values archetypes within countries and homogeneity across countries, calling into question the assumption of uniform national culture values in economics and other fields. We show how the heterogeneity in culture values across the globe can be represented with a small number of distinctive archetypes. The study could be extended to include a larger set of countries, and/or cover a broader range of theoretically grounded values than those available in the Schwartz values model in the WVS. Research and practice often assume cultural homogeneity within nations and cultural diversity across nations. Our finding of different culture archetypes within countries and similar archetypes across countries demonstrates the important role of culture sharing and exchange as a source of reducing cultural conflicts between nations and enhancing creativity and innovation through interaction and integration in novel ways. We examine culture as a configuration of multiple values, and use a novel AA method to capture heterogeneity in culture values within and across countries.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

In light of the growing interest in neuroscience within the managerial and organizational cognition (MOC) scholarly domain at large, this chapter advances current knowledge on core neuroscience methods. It does so by building on the theoretical analysis put forward by Healey and Hodgkinson (2014, 2015), and by offering a thorough – yet accessible – methodological framework for a better understanding of key cognitive and social neuroscience methods. Classifying neuroscience methods based on their degree of resolution, functionality, and anatomical focus, the chapter outlines their features, practicalities, advantages and disadvantages. Specifically, it focuses on functional magnetic resonance imaging, electroencephalography, magnetoencephalography, heart rate variability, and skin conductance response. Equipped with knowledge of these methods, researchers will be able to further their understanding of the potential synergies between management and neuroscience, to better appreciate and evaluate the value of neuroscience methods, and to look at new ways to frame old and new research questions in MOC. The chapter also builds bridges between researchers and practitioners by rebalancing the hype and hopes surrounding the use of neuroscience in management theory and practice.

While quantitative survey design represents a default research method in the field of hospitality and tourism, qualitative approaches remain largely sidelined. This is particularly true for netnography, a novel method of scientific enquiry that targets the online interactions of various actors. The present chapter seeks to introduce the netnographic approach, outline its implementation in hospitality and tourism, as well as demarcate it from other methods, such as survey, text mining and content analysis. By giving an overview of recent studies employing netnography, the chapter demonstrates applied examples of ethnographic research online, presents a cross-cultural study on disappointing travel experiences and suggests further research avenues, such as cross-cultural investigation. It concludes by discussing strengths and weaknesses of the netnographic approach. The value of this chapter lies in its reflection of state-of-the-art research in hospitality and tourism based on netnography and the proposition of further directions of research.

This chapter highlights the value offered by photographic research methods to the study of organizational compassion. We demonstrate this potential by first briefly reviewing the history and usage of photographic research methods in the social sciences and the state of compassion research. We then describe how compassion emerged as a key theme in a field study that utilized photographic methods. From this, we identify four approaches that photographic research methods can be used to extend our understanding of compassion in organizations. Specifically, we clarify how this stream of research can be enhanced by the inclusion of photographic methods. We highlight critical research decisions and possible concerns in implementing photographic methods. The chapter concludes with additional organizational phenomena that would benefit from using a photographic methods approach.

The various methods gathered under the umbrella label of qualitative (Guba & Lincoln, 1994), defined as the study of “things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them” (Denzin & Lincoln, 2005, p. 3), offer many benefits through their ability to access, explore, and experience real organizational people and problems in rich detail (Van Maanen, 1979). As an example, photographic research methods—primarily qualitative methods through which researchers use photographs to elicit information during interviews and focus groups—often result in deep and nuanced data (Collier & Collier, 1986; Harper, 2005; Vince & Warren, 2012). Photographic methodologies are well-suited to the exploration of new phenomena because they allow researchers to get close to the lived experience and organizational processes (Dion, 2007), attend simultaneously to the social and material world in organizations (Shortt & Warren, 2012), and offer the potential to “mine deeper shafts into a different part of human consciousness than do words-alone interviews” (Harper, 2002, p. 23). Organizational research has traditionally been dominated by a positivistic paradigm that focuses on theory evaluation through the use of quantitative methodologies (Lin, 1998; Sutton, 1997), whereas qualitative research offers the potential to build theory by illuminating underlying processes and causal mechanisms in specific contexts (Lee, 1999). Researchers developing theory may be particularly interested in the richness of the data gathered with qualitative methods (Edmondson & McManus, 2007) such as photographic methods. Qualitative research is thus well-matched to nascent literatures that require inductive study about a phenomenon to generate foundational knowledge (Edmondson & McManus, 2007).

One such nascent research stream that could benefit from photographic methodologies is organizational compassion (Rynes, Bartunek, Dutton, & Margolis, 2012). In its current state, compassion research within the organizational literature has generated many narratives of experiences of compassion in response to a specific tragedy (Dutton, Worline, Frost, & Lilius, 2006), as an organizational capability (Lilius et al., 2011b), or as an organizational capacity that an organization can develop (Madden, Duchon, Madden, & Plowman, 2012). These stories demonstrate that the common elements of the compassion process are the noticing of someone else's pain, empathizing with that person, and then responding in a way designed to lessen that pain (Kanov et al., 2004); however, because this process is so individualized, photographic methodologies offer researchers a chance to capture valuable new information about this process and the experience of compassion within organizations. In this chapter, we describe many potential benefits of designing organizational compassion research based on photographic methodologies.

In doing so, we offer several contributions. First, we show how photographic methodologies can create deeper responses during interviews and observations that may lead to surprising insights for theory. Second, by suggesting some of the insights that have been generated about compassion through photographic methodologies, we offer novel research ideas for this growing body of literature. The following sections provide background on the development and history of photographic methodologies and review the studies and methodologies that have contributed to our understanding of compassion within organizations. Subsequently, we describe some of the ways in which compassion has surfaced during our own field study using photograph elicitation. Finally, we describe possible studies that could benefit from the use of four forms of photographic methodologies to explore more targeted research questions related to organizational compassion and also offer a range of other organizational phenomena that could benefit from a photographic methods approach.