qualitative research in international business examples

• DOI: 10.1057/JIBS.2011.18
• Corpus ID: 168160539

Qualitative research for international business

• Published 1 June 2011
• Journal of International Business Studies

539 Citations

Building international business theory: a grounded theory approach, qualitative research in marketing: what can academics do better, qca and business research: work in progress or a consolidated agenda, a review of literature using business systems theory: lessons for international business research, international business and national culture: a literature review and research agenda, developing international business knowledge through an appreciative inquiry learning network : proposing a methodology for collaborative research, verifying rigor: analyzing qualitative research in international marketing, the impact of qualitative methods on article citation: an international business research perspective.

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Agility and flexibility in international business research: A comprehensive review and future research directions

The changing nature of the international business field, and the progress of jibs, 57 references, is the international business research agenda running out of steam.

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What passes as a rigorous case study

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Article contents

Qualitative designs and methodologies for business, management, and organizational research.

• Robert P. Gephart Robert P. Gephart Alberta School of Business, University of Alberta
•  and  Rohny Saylors Rohny Saylors Carson College of Business, Washington State University
• https://doi.org/10.1093/acrefore/9780190224851.013.230
• Published online: 28 September 2020

Qualitative research designs provide future-oriented plans for undertaking research. Designs should describe how to effectively address and answer a specific research question using qualitative data and qualitative analysis techniques. Designs connect research objectives to observations, data, methods, interpretations, and research outcomes. Qualitative research designs focus initially on collecting data to provide a naturalistic view of social phenomena and understand the meaning the social world holds from the point of view of social actors in real settings. The outcomes of qualitative research designs are situated narratives of peoples’ activities in real settings, reasoned explanations of behavior, discoveries of new phenomena, and creating and testing of theories.

A three-level framework can be used to describe the layers of qualitative research design and conceptualize its multifaceted nature. Note, however, that qualitative research is a flexible and not fixed process, unlike conventional positivist research designs that are unchanged after data collection commences. Flexibility provides qualitative research with the capacity to alter foci during the research process and make new and emerging discoveries.

The first or methods layer of the research design process uses social science methods to rigorously describe organizational phenomena and provide evidence that is useful for explaining phenomena and developing theory. Description is done using empirical research methods for data collection including case studies, interviews, participant observation, ethnography, and collection of texts, records, and documents.

The second or methodological layer of research design offers three formal logical strategies to analyze data and address research questions: (a) induction to answer descriptive “what” questions; (b) deduction and hypothesis testing to address theory oriented “why” questions; and (c) abduction to understand questions about what, how, and why phenomena occur.

The third or social science paradigm layer of research design is formed by broad social science traditions and approaches that reflect distinct theoretical epistemologies—theories of knowledge—and diverse empirical research practices. These perspectives include positivism, interpretive induction, and interpretive abduction (interpretive science). There are also scholarly research perspectives that reflect on and challenge or seek to change management thinking and practice, rather than producing rigorous empirical research or evidence based findings. These perspectives include critical research, postmodern research, and organization development.

Three additional issues are important to future qualitative research designs. First, there is renewed interest in the value of covert research undertaken without the informed consent of participants. Second, there is an ongoing discussion of the best style to use for reporting qualitative research. Third, there are new ways to integrate qualitative and quantitative data. These are needed to better address the interplay of qualitative and quantitative phenomena that are both found in everyday discourse, a phenomenon that has been overlooked.

• qualitative methods
• research design
• methods and methodologies
• interpretive induction
• interpretive science
• critical theory
• postmodernism
• organization development

Introduction

Qualitative research uses linguistic symbols and stories to describe and understand actual behavior in real settings (Denzin & Lincoln, 1994 ). Understanding requires describing “specific instances of social phenomena” (Van Maanen, 1998 , p. xi) to determine what this behavior means to lay participants and to scientific researchers. This process produces “narratives-non-fiction division that link events to events in storied or dramatic fashion” to uncover broad social science principles at work in specific cases (p. xii).

A research design and/or proposal is often created at the outset of research to act as a guide. But qualitative research is not a rule-governed process and “no one knows” the rules to write memorable and publishable qualitative research (Van Maanen, 1998 , p. xxv). Thus qualitative research “is anything but standardized, or, more tellingly, impersonal” (p. xi). Design is emergent and is often created as it is being done.

Qualitative research is also complex. This complexity is addressed by providing a framework with three distinct layers of knowledge creation resources that are assembled during qualitative research: the methods layer, the logic layer, and the paradigmatic layer. Research methods are addressed first because “there is no necessary connection between research strategies and methods of data collection and analysis” (Blaikie, 2010 , p. 227). Research methods (e.g., interviews) must be adapted for use with the specific logical strategies and paradigmatic assumptions in mind.

The first, or methods, layer uses qualitative methods to “collect data.” That is, to observe phenomena and record written descriptions of observations, often through field notes. Established methods for description include participant and non-participant observation, ethnography, focus groups, individual interviews, and collection of documentary data. The article explains how established methods have been adapted and used to answer a range of qualitative research questions.

The second, or logic, layer involves selecting a research strategy—a “logic, or set of procedures, for answering research questions” (Blaikie, 2010 , p. 18). Research strategies link research objectives, data collection methods, and logics of analysis. The three logical strategies used in qualitative organizational research are inductive logic, deductive logic and abductive logic (Blaikie, 2010 , p. 79). 1 Each logical strategy makes distinct assumptions about the nature of knowledge (epistemology), the nature of being (ontology), and how logical strategies and assumptions are used in data collection and analysis. The task is to describe important methods suitable for each logical strategy, factors to consider when selecting methods (Blaikie, 2010 ), and illustrates how data collection and analysis methods are adapted to ensure for consistency with specific logics and paradigms.

The third, or paradigms, layer of research design addresses broad frameworks and scholarly traditions for understanding research findings. Commitment to a paradigm or research tradition entails commitments to theories, research strategies, and methods. Three paradigms that do empirical research and seek scientific knowledge are addressed first: positivism, interpretive induction, and interpretive abduction. Then, three scholarly and humanist approaches that critique conventional research and practice to encourage organizational change are discussed: critical theory and research, postmodern perspectives, and organization development (OD). Paradigms or traditions provide broad scholarly contexts that make specific studies comprehensible and meaningful. Lack of grounding in an intellectual tradition limits the ability of research to contribute: contributions always relate to advancing the state of knowledge in specific unfolding research traditions that also set norms for assessing research quality. The six research designs are explained to show how consistency in design levels can be achieved for each of the different paradigms. Further, qualitative research designs must balance the need for a clear plan to achieve goals with the need for adaptability and flexibility to incorporate insights and overcome obstacles that emerge during research.

Our general goal has been to provide a practical guide to inspire and assist readers to better understand, design, implement, and publish qualitative research. We conclude by addressing future challenges and trends in qualitative research.

The Substance of Research Design

A research design is a written text that can be prepared prior to the start of a research project (Blaikie, 2010 , p. 4) and shared or used as “a private working document.” Figure 1 depicts the elements of a qualitative research design and research process. Interest in a topic or problem leads researchers to pose questions and select relevant research methods to fulfill research purposes. Implementation of the methods requires use of logical strategies in conjunction with paradigms of research to specify concepts, theories, and models. The outcomes, depending on decisions made during research, are scientific knowledge, scholarly (non-scientific) knowledge, or applied knowledge useful for practice.

Figure 1. Elements of qualitative research design.

Research designs describe a problem or research question and explain how to use specific qualitative methods to collect and analyze qualitative data that answer a research question. The purposes of design are to describe and justify the decisions made during the research process and to explain how the research outcomes can be produced. Designs are thus future-oriented plans that specify research activities, connect activities to research goals and objectives, and explain how to interpret the research outcomes using paradigms and theories.

In contrast, a research proposal is “a public document that is used to obtain necessary approvals for a research proposal to proceed” (Blaikie, 2010 , p. 4). Research designs are often prepared prior to creating a research proposal, and research proposals often require the inclusion of research designs. Proposals also require greater formality when they are the basis for a legal contract between a researcher and a funding agency. Thus, designs and proposals are mutually relevant and have considerable overlap but are addressed to different audiences. Table 1 provides the specific features of designs and proposals. This discussion focuses on designs.

Table 1. Decisions Necessitated by Research Designs and Proposals

Source: Based on Blaikie ( 2010 ), pp. 12–34.

The “real starting point” for a research design (or proposal) is “the formulation of the research question” (Blaikie, 2010 , p. 17). There are three types of research questions: “what” questions seek descriptions; “why” questions seek answers and understanding; and “how” questions address conditions where certain events occur, underlying mechanisms, and conditions necessary for change interventions (p. 17). It is useful to start with research questions rather than goals, and to explain what the research is intended to achieve (p. 17) in a technical way.

The process of finding a topic and formulating a useful research question requires several considerations (Silverman, 2014 , pp. 31–33, 34–40). Researchers must avoid settings where data collection will be difficult (pp. 31–32); specify an appropriate scope for the topic—neither too wide or too narrow—that can be addressed (pp. 35–36); fit research questions into a relevant theory (p. 39); find the appropriate level of theory to address (p. 42); select appropriate designs and research methods (pp. 42–44); ensure the volume of data can be handled (p. 48); and do an effective literature review (p. 48).

A literature review is an important way to link the proposed research to current knowledge in the field, and to explain what was previously known or what theory suggests to be the case (Blaikie, 2010 , p. 17). Research questions can used to bound and frame the literature review while the literature review often inspires research questions. The review may also provide bases for creating new hypotheses and for answering some of the initial research questions (Blaikie, 2010 , p. 18).

Layers of Research Design

There are three layers of research design. The first layer focuses on research methods for collecting data. The second layer focuses on the logical frameworks used for analyzing data. The third layer focuses on the paradigm used to create a coherent worldview from research methods and logical frameworks.

Layer One: Design as Research Methods

Qualitative research addresses the meanings people have for phenomena. It collects narratives of organizational activity, uses analytical induction to create coherent representations of the truths and meanings in organizational contexts, and then creates explanations of this conduct and its prevalence (Van Maanan, 1998 , pp. xi–xii). Thus qualitative research involves “doing research with words” (Gephart, 2013 , title) in order to describe the linguistic symbols and stories that members use in specific settings.

There are four general methods for collecting qualitative data and creating qualitative descriptions (see Table 2 ). The in-depth case study approach provides a history of an event or phenomenon over time using multiple data sources. Observational strategies use the researcher to observe and describe behavior in actual settings. Interview strategies use a format where a researcher asks questions of an informant. And documentary research collects texts, documents, official records, photographs, and videos as data—formally written or visually recorded evidence that can be replayed and reviewed (Creswell, 2014 , p. 190). These methods are adapted to fit the needs of specific projects.

Table 2. Qualitative Data Collection Methods

The In-Depth Case Study Method

The in-depth case study is a key strategy for qualitative research (Piekkari & Welch, 2012 ). It was the most common qualitative method used during the formative years of the field, from 1956 to 1965 , when 48% of qualitative papers published in the Administrative Science Quarterly used the case study method (Van Maanen, 1998 , p. xix). The case design uses one or more data collection strategies to describe in detail how a single event or phenomenon, selected by a researcher, has changed over time. This provides an understanding of the processes that underlie changes to the phenomenon. In-depth case study methods use observations, documents, records, and interviews that describe the events in the case unfolded and their implications. Case studies contextualize phenomena by studying them in actual situations. They provide rich insights into multiple dimensions of a single phenomenon (Campbell, 1975 ); offer empirical insights into what, how, and why questions related to phenomena; and assist in the creation of robust theory by providing diverse data collected over time (Gephart & Richardson, 2008 , p. 36).

Maniha and Perrow ( 1965 ) provide an example of a case study concerned with organizational goal displacement, an important issue in early organizational theorizing that proposed organizations emerge from rational goals. Organizational rationality was becoming questioned at the time that the authors studied a Youth Commission with nine members in a city of 70,000 persons (Maniha & Perrow, 1965 ). The organization’s activities were reconstructed from interviews with principals and stakeholders of the organization, minutes from Youth Commission meetings, documents, letters, and newspaper accounts (Maniha & Perrow, 1965 ).

The account that emerged from the data analysis is a history of how a “reluctant organization” with “no goals to guide it” was used by other aggressive organizations for their own ends. It ultimately created its own mission (Maniha & Perrow, 1965 ). Thus, an organization that initially lacked rational goals developed a mission through the irrational process of goal slippage or displacement. This finding challenged prevailing thinking at the time.

Observational Strategies

Observational strategies involve a researcher present in a situation who observes and records, the activities and conversations that occur in the setting, usually in written field notes. The three observational strategies in Table 2 —participant observation, ethnography, and systematic self-observation—differ in terms of the role of the researcher and in the data collection approach.

Participant observation . This is one of the earliest qualitative methods (McCall & Simmons, 1969 ). One gains access to a setting and an informant holding an appropriate social role, for example, client, customer, volunteer, or researcher. One then observes and records what occurs in the setting using field notes. Many features or topics in a setting can become a focus for participant observers. And observations can be conducted using continuum of different roles from the complete participant, observer as participant, and participant observer, to the complete observer who observes without participation (Creswell, 2014 , Table 9.2, p. 191).

Ethnography . An ethnography is “a written representation of culture” (Van Maanen, 1988 ) produced after extended participation in a culture. Ethnography is a form of participant observation that focuses on the cultural aspects of the group or organization under study (Van Maanen, 1988 , 2010 ). It involves prolonged and close contact with group members in a role where the observer becomes an apprentice to an informant to learn about a culture (Agar, 1980 ; McCurdy, Spradley, & Shandy, 2005 ; Spradley, 1979 ).

Ethnography produces fine-grained descriptions of a micro-culture, based on in-depth cultural participation (McCurdy et al., 2005 ; Spradley, 1979 , 2016 ). Ethnographic observations seek to capture cultural members’ worldviews (see Perlow, 1997 ; Van Maanen, 1988 ; Watson, 1994 ). Ethnographic techniques for interviewing informants have been refined into an integrated developmental research strategy—“the ethno-semantic method”—for undertaking qualitative research (Spradley, 1979 , 2016 ; Van Maanen, 1981 ). The ethnosemantic method uses a structured approach to uncover and confirm key cultural features, themes, and cultural reasoning processes (McCurdy et al., 2005 , Table 3 ; Spradley, 1979 ).

Systematic Self-Observation . Systematic self-observation (SSO) involves “training informants to observe and record a selected feature of their own everyday experience” (Rodrigues & Ryave, 2002 , p. 2; Rodriguez, Ryave, & Tracewell, 1998 ). Once aware that they are experiencing the target phenomenon, informants “immediately write a field report on their observation” (Rodrigues & Ryave, 2002 , p. 2) describing what was said and done, and providing background information on the context, thoughts, emotions, and relationships of people involved. SSO generates high-quality field notes that provide accurate descriptions of informants’ experiences (pp. 4–5). SSO allows informants to directly provide descriptions of their personal experiences including difficult to capture emotions.

Interview Strategies

Interviews are conversations between researchers and research participants—termed “subjects” in positivist research and informants in “interpretive research.” Interviews can be conducted as individual face-to-face interactions (Creswell, 2014 , p. 190) or by telephone, email, or through computer-based media. Two broad types of interview strategies are (a) the individual interview and (b) the group interview or focus group (Morgan, 1997 ). Interviews elicit informants’ insights into their culture and background information, and obtain answers and opinions. Interviews typically address topics and issues that occur outside the interview setting and at previous times. Interview data are thus reconstructions or undocumented descriptions of action in past settings (Creswell, 2014 , p. 191) that provide descriptions that are less accurate and valid descriptions than direct, real-time observations of settings.

Structured and unstructured interviews. Structured interviews pose a standardized set of fixed, closed-ended questions (Easterby-Smith, Thorpe, & Jackson, 2012 ) to respondents whose responses are recorded as factual information. Responses may be forced choice or open ended. However, most qualitative research uses unstructured or partially structured interviews that pose open-ended questions in a flexible order that can be adapted. Unstructured interviews allow for detailed responses and clarification of statements (Easterby-Smith et al., 2012 ; McLeod, 2014 )and the content and format can be tailored to the needs and assumptions of specific research projects (Gephart & Richardson, 2008 , p. 40).

The informant interview (Spradley, 1979 ) poses questions to informants to elicit and clarify background information about their culture, and to validate ethnographic observations. In interviews, informants teach the researcher their culture (Spradley, 1979 , pp. 24–39). The informant interview is part of a developmental research sequence (McCurdy et al., 2005 ; Spradley, 1979 ) that begins with broad “grand tour” questions that ask an informant to describe an important domain in their culture. The questions later narrow to focus on details of cultural domains and members’ folk concepts. This process uncovers semantic relationships among concepts of members and deeper cultural themes (McCurdy et al., 2005 ; Spradley, 1979 ).

The long interview (McCracken, 1988 ) involves a lengthy, quasi-structured interview sessions with informants to acquire rapid and efficient access to cultural themes and issues in a group. Long interviews differ ethnographic interviews by using a “more efficient and less obtrusive format” (p. 7). This creates a “sharply focused, rapid and highly intense interview process” that avoids indeterminate and redundant questions and pre-empts the need for observation or involvement in a culture. There are four stages in the long interview: (a) review literature to uncover analytical categories and design the interview; (b) review cultural categories to prepare the interview guide; (c) construct the questionnaire; and (d) analyze data to discover analytical categories (p. 30, fig. 1 ).

The active interview is a dynamic process where the researcher and informant co-construct and negotiate interview responses (Holstein & Gubrium, 1995 ). The goal is to uncover the subjective meanings that informants hold for phenomenon, and to understand how meaning is produced through communication. The active approach is common in interpretive, critical, and postmodern research that assumes a negotiated order. For example, Richardson and McKenna ( 2000 ) explored how ex-patriate British faculty members themselves interpreted and explained their expatriate experience. The researchers viewed the interview setting as one where the researchers and informants negotiated meanings between themselves, rather than a setting where prepared questions and answers were shared.

Documentary, Photographic, and Video Records as Data

Documents, records, artifacts, photographs, and video recordings are physically enduring forms of data that are separable from their producers and provide mute evidence with no inherent meaning until they are read, written about, and discussed (Hodder, 1994 , p. 393). Records (e.g., marriage certificate) attest to a formal transaction, are associated with formal governmental institutions, and may have legally restricted access. In contrast, documents are texts prepared for personal reasons with fewer legal restrictions but greater need for contextual interpretation. Several approaches to documentary and textual data analysis have been developed (see Table 3 ). Documents that researchers have found useful to collect include public documents and minutes of meetings; detailed transcripts of public hearings; corporate and government press releases; annual reports and financial documents; private documents such as diaries of informants; and news media reports.

Photographs and videos are useful for capturing “accurate” visual images of physical phenomena (Ray & Smith, 2012 ) that can be repeatedly reexamined and used as evidence to substantiate research claims (LeBaron, Jarzabkowski, Pratt, & Fetzer, 2018 ). Photos taken from different positions in space may also reveal different features of phenomena. Videos show movement and reveal activities as processes unfolding over time and space. Both photos and videos integrate and display the spatiotemporal contexts of action.

Layer Two: Design as Logical Frameworks

The second research design layer links data collection and analysis methods (Tables 2 and 3 ) to three logics of enquiry that answer specific questions: inductive, deductive, and abductive logical strategies (see Table 4 ). Each logical strategy focuses on producing different types of knowledge using distinctive research principles, processes, and types of research questions they can address.

Table 3. Data Analysis and Integrated Data Collection and Analysis Strategies

Table 4. Logical Strategies for Answering Qualitative Research Questions with Evidence

Based in part on Blaikie ( 1993 ), ch. 5 & 6; Blaikie ( 2010 ), p. 84, table 4.1

The Inductive Strategy

Induction is the scientific method for many scholars (Blaikie, 1993 , p. 134), and an essential logic for qualitative management research (Pratt, 2009 , p. 856). Inductive strategies ask “what” questions to explore a domain to discover unknown features of a phenomenon (Blaikie, 2010 , p. 83). There are four stages to the inductive strategy: (a) observe and record all facts without selection or anticipating their importance; (b) analyze, compare, and classify facts without employing hypotheses; (c) develop generalizations inductively based on the analyses; and (d) subject generalizations to further testing (Blaikie, 1993 , p. 137).

Inductive research assumes a real world outside human thought that can be directly sensed and described (Blaikie, 2010 ). Principles of inductive research reflect a realist and objectivist ontology. The selection, definition, and measurement of characteristics to be studied are developed from an objective, scientific point of view. Facts about organizational features need to be obtained using unbiased measurement. Further, the elimination method is used to find “the characteristics present in all the positive cases, which are absent in all the negative cases, and which vary in appropriate degrees” (Blaikie, 1993 , p. 135). This requires data collection methods that provide unbiased evidence of the objective facts without pre-supposing their importance.

Induction can establish limited generalizations about phenomena based solely on the observations collected. Generalizations need to be based on the entire sample of data, not on selected observations from large data sets, to establish their validity. The scope of generalization is limited to the sample of data itself. Induction creates evidence to increase our confidence in a conclusion, but the conclusions do not logically follow from premises (Blaikie, 1993 , p. 164). Indeed, inferences from induction cannot be extended beyond the original set of observations and no logical or formal process exists to establish the universality of inferences.

Key data collection methods for inductive designs include observational strategies that allow the researcher to view behavior without making a priori hypotheses, to describe behavior that occurs “naturally” in settings, and to record non-impressionistic descriptions of behavior. Interviews can also elicit descriptions of settings and behavior for inductive qualitative research. Data analysis methods need to describe actual interactions in real settings including discourse among members. These methods include ethnosemantic analysis to uncover key terms and validate actual meanings used by members; analyses of conversational practices that show how meaning is negotiated through sequential turn taking in discourse; and grounded theory-based concept coding and theory development that use the constant comparative method.

Facts or descriptions of events can be compared to one another and generalizations can be made about the world using induction (Blaikie, 2010 ). Outcomes from inductive analysis include descriptions of features in a limited domain of social action that are inferred to exist in other similar settings. Propositions and broader insights can be developed inductively from these descriptions.

The Deductive Strategy

Deductive logic (Blaikie, 1993 , 2010 ) addresses “why” questions to explain associations between concepts that represent phenomena of interest. Researchers can use induction, abduction, or any means, to develop then test the hypotheses to see if they are valid. Hypotheses that are not rejected are temporarily corroborated. The outcomes from deduction are tested hypotheses. Researchers can thus be very creative in hypothesis construction but they cannot discover new phenomena with deduction that is based only on phenomena known in advance (Blaikie, 2010 ). And there is also no purely logical or mechanical process to establish “the validity of [inductively constructed] universal statements from a set of singular statements” from which deductive hypotheses were formed (Hempel, 1966 , p. 15 cited in Blaikie, 1993 , p. 140).

The deductive strategy uses a realist and objectivist ontology and imitates natural science methods. Useful data collection methods include observation, interviewing, and collection of documents that contain facts. Deduction addresses the assumedly objective features of settings and interactions. Appropriate data analysis methods include content coding to identify different types, features, and frequencies of observed phenomena; grounded theory coding and analytical induction to create categories in data, determine how categories are interrelated, and induce theory from observations; and pattern recognition to compare current data to prior models and samples. Content analysis and non-parametric statistics can be used to quantify qualitative data and make it more amenable to analysis, although quantitative analysis of qualitative data is not, strictly speaking, qualitative research (Gephart, 2004 ).

The Abductive Strategy

Abduction is “the process used to produce social scientific accounts of social life by drawing on the concepts and meanings used by social actors, and the activities in which they engage” (Blaikie, 1993 , p. 176). Abductive reasoning assumes that the socially meaningful world is the world experienced by members. The first abductive task is to discover the insider view that is basic to the actions of social actors (p. 176) by uncovering the subjective meanings held by social actors. Subjective meaning (Schutz, 1973a , 1973b ) refers to the meaning that actions hold for the actors themselves and that they can express verbally. Subjective meaning is not inexpressible ideas locked in one’s mind. Abduction starts with lay descriptions of social life, then moves to technical, scientific descriptions of social life (Blaikie, 1993 , p. 177) (see Table 4 ). Abduction answers “what” questions with induction, why questions with deduction, and “how” questions with hypothesized processes that explain how, and under what conditions, phenomena occur. Abduction involves making a logical leap that infers an explanatory process to explain an outcome in an oscillating logic. Deductive, inductive, and inferential processes move recursively from actors’ accounts to social science accounts and back again in abduction (Gephart, 2018 ). This process enables all theory and second-order scientific concepts to be grounded in actors’ first-order meanings.

The abductive strategy contains four layers: (a) everyday concepts and meanings of actors, used for (b) social interaction, from which (c) actors provide accounts, from which (d) social scientific descriptions are made, or theories are generated and applied, to interpret phenomena (Blaikie, 1993 , p. 177). The multifaceted research process, described in Table 4 , requires locating and comprehending members’ important everyday concepts and theories before observing or creating disruptions that force members to explain the unstated knowledge behind their action. The researcher then integrates members’ first-order concepts into a general, second-order scientific theory that makes first-order understandings recoverable.

Abduction emerged from Weber’s interpretive sociology ( 1978 ) and Peirce’s ( 1936 ) philosophy. But Alfred Schutz ( 1973a , 1973b ) is the contemporary scholar who did the most to extend our understanding of abduction, although he never used the term “abduction” (Blaikie, 1993 , 2010 ; Gephart, 2018 ). Schutz conceived abduction as an approach to verifiable interpretive knowledge that is scientific and rigorous (Blaikie, 1993 ; Gephart, 2018 ). Abduction is appropriate for research that seeks to go beyond description to explanation and prediction (Blaikie, 1993 , p. 163) and discovery (Gephart, 2018 ). It employs an interpretive ontology (Schutz, 1973a , 1973b ) and social constructionist epistemology (Berger & Luckmann, 1966 ), using qualitative methods to discover “why people do what they do” (Blaikie, 1993 ).

Dynamic data collection methods are needed for abductive research to capture descriptions of interactions in actual settings and their meanings to members. Observational and interview approaches that elicit members’ concepts and theories are particularly relevant to abductive understanding (see Table 2 ). Data analysis methods must analyze situated, first-order (common sense) discourse as it unfolds in real settings and then systematically develop second-order concepts or theories from data. Relevant approaches to produce and validate findings include ethnography, ethnomethodology, and grounded theorizing (see Table 3 ). The combination of what, why, and how questions used in abduction produces a broader understanding of phenomena than do what and why deductive and inductive questions.

Layer Three: Paradigms of Research

Scholarly paradigms integrate methods, logics, and intellectual worldviews into coherent theoretical perspectives and form the most abstract level of research design. Six paradigms are widely used in management research (Burrell & Morgan, 1979 ; Cunliffe, 2011 ; Gephart, 2004 , 2013 ; Gephart & Richardson, 2008 ; Hassard, 1993 ). The first three perspectives—positivism, interpretive induction, and interpretive abduction—build on logics of design and seek to produce rigorous empirical research that constitutes evidence (see Table 5 ). Three additional perspectives pursue philosophical, critical, and practical knowledge: critical theory, postmodernism, and organization development (see Table 6 ). Tables 5 and 6 describe important features of each research design to show similarities and differences in the processes through which theoretical meaning is bestowed on research results in management and organization studies.

Table 5. Paradigms, Logical Strategies, and Methodologies for Empirical Research

Sources: Based on and adapted and extended from Blaikie ( 1993 , pp. 137, 145, & 152); Blaikie ( 2010 , Table 4.1, p. 84); Gephart ( 2013 , Table 9.1, p. 291) and Gephart ( 2018 , Table 3.1, pp. 38–39).

Table 6. Alternative Paradigms, Logical Strategies, and Methodologies

Based in part on Gephart ( 2004 , 2013 , 2018 ).

The Positivist Approach

The qualitative positivist approach makes assumptions equivalent to those of quantitative research (Gephart, 2004 , 2018 ). It assumes the world is objectively describable and comprehensible using inductive and deductive logics. And rigor is important and achieved by reliability, validity, and generalizability of findings (Kirk & Miller, 1986 ; Malterud, 2001 ). Qualitative positivism mimics natural science logics and methods using data recorded as words and talk rather than numerals.

Positivist research (Bitektine, 2008 ; Su, 2018 ) starts with a hypothesis. This can, but need not, be based in data or inductive theory. The research process, aimed at publication in peer-reviewed journals, requires researchers to (a) identify variables to measure, (b) develop operational definitions of the variables, (c) measure (describe) the variables and their inter-relationships, (d) pose hypotheses to test relationships among variables, then (e) compare observations to hypotheses for testing (Blaikie, 2010 ). When data are consistent with theory, theory passes the test. Otherwise the theory fails. This theory is also assessed for its logical correctness and value for knowledge. The positivist approach can assess deductive and inductive generalizations and provide evidence concerning why something occurs—if proposed hypotheses are not rejected.

Positivists view qualitative research as highly subject to biases that must be prevented to ensure rigor, and 23 methodological steps are recommended to enhance rigor and prevent bias (Gibbert & Ruigrok, 2010 , p. 720). Replicability is another concern because methodology descriptions in qualitative publications “insufficiently describe” how methods are used (Lee, Mitchell, & Sablynski, 1999 , p. 182) and thereby prevent replication. To ensure replicability, a qualitative “article’s description of the method must be sufficiently detailed to allow a reader . . . to replicate that reported study either in a hypothetical or actual manner.”

Qualitative research allows positivists to observe naturally unfolding behavior in real settings and allow “the real world” of work to inform research and theory (Locke & Golden-Biddle, 2004 ). Encounters with the actual world provide insights into meaning construction by members that cannot be captured with outsider (etic) approaches. For example, past quantitative research provided inconsistent findings on the importance of pre- and post-recruitment screening interviews for job choices of recruits. A deeper investigation was thus designed to examine how recruitment impacts job selection (Rynes, Bretz, & Gerhart, 1991 ). To do so, students undergoing recruitment were asked to “tell us in their own words” how their recruiting and decision processes unfolded (Rynes et al., 1991 , p. 399). Using qualitative evidence, the researchers found that, in contrast to quantitative findings, “people do make choices based on how they are treated” (p. 509), and the choices impact recruitment outcomes. Rich descriptions of actual behavior can disconfirm quantitative findings and produce new findings that move the field forward.

An important limitation of positivism is its common emphasis on outsiders’ or scientific observers’ objective conceptions of the world. This limits the attention positivist research gives to members’ knowledge and allows positivist research to impose outsiders’ meanings on members’ everyday behavior, leading to a lack of understanding of what the behavior means to members. Another limitation is that no formal, logical, or proven techniques exist to assess the strength of “relationships” among qualitative variables, although such assessments can be formally done using well-formed quantitative data and techniques. Thus, qualitative positivists often provide ambiguous or inexplicit quantitative depictions of variable relations (e.g., “strong relationship”). Alternatively, the analysts quantify qualitative data by assigning numeric codes to categories (Greckhamer, Misngyi, Elms, & Lacey, 2008 ), using non-parametric statistics, or quantitative content analysis (Sonpar & Golden-Biddle, 2008 ) to create numerals that depict associations among variables.

An illustrative example of positivist research . Cole ( 1985 ) studied why and how organizations change their working structures from bureaucratic forms to small, self-supervised work teams that allow for worker participation in shop floor activities. Cole found that existing research on workplace change focused on the micropolitical level of organizations. He hypothesized that knowledge could be advanced differently, by examining the macropolitical change in industries or nations. Next, a testable conclusion was deduced: a macro analysis of the politics of change can better predict the success of work team implementation, measured as the spread of small group work structures, than an examination of the micropolitics of small groups ( 1985 ). Three settings were selected for the research: Japan, Sweden, and the United States. Japanese data were collected from company visits and interviews with employment officials and union leaders. Swedish documentary data on semiautonomous work groups were used and supplemented by interviews at Volvo and Saab, and prior field research in Sweden. U.S. data were collected through direct observations and a survey of early quality circle adopters.

Extensive change was observed in Sweden and Japan but changes to small work groups were limited in the United States (Cole, 1985 ). This conclusion was verified using records of the experiences of the three nations in work reform, compared across four dimensions: timing and scope of changes, managerial incentives to innovate, characteristics of mobilization, and political dimensions of change. Data revealed the United States had piecemeal experimentation and resistance to reform through the 1970s; diffusion emerged in Japan in the early 1960s and became extensive; and Swedish workplace reform started in the 1960s and was widely and rapidly diffused.

Cole then answered the questions of “why” and “how” the change occurred in some countries but not others. Regarding why Japanese and Swedish managers were motivated to introduce workplace change due to perceived managerial problems and the changing national labor market. Differences in the political processes also influenced change. Management, labor, and government interest in workplace change was evident in Japan and Sweden but not in the United States where widespread resistance occurred. As to how, the change occurred through macropolitical processes (Cole, 1985 , p. 120), specifically, the commitment of the national business leadership to the change and whether or not the change was contested or uncontested by labor impacted the adoption of change. Organizational change usually occurs through broad macropolitical processes, hence “the importance of macro-political variables in explaining these outcomes” (p. 122).

Interpretive Induction

Two streams of qualitative research claim the label of “interpretive research” in management and organization studies. The first stream, interpretive induction, emphasizes induction as its primary logical strategy (e.g., Locke, 2001 , 2002 ; Pratt, 2009 ). It assumes a “real world” that is inherently objective but interpreted through subjective lenses, hence different people can perceive or report different things. This research is interpretive because it addresses the meanings and interpretations people give to organizational phenomena, and how this meaning is provided and used. Interpretive induction contributes to scientific knowledge by providing empirical descriptions, generalizations, and low-level theories about specific contexts based on thick descriptions of members’ settings and interactions (first-order understandings) as data.

The interpretive induction paradigm addresses “what” questions that describe and explain the existence and features of phenomena. It seeks to uncover the subjective, personal knowledge that subjects have of the objective world and does so by creating descriptive accounts of the activities of organizational members. Interpretive induction creates inductive theories based on limited samples that provide low-scope, abstract theory. Limitations (Table 5 ) include the fact that inductive generalizations are limited to the sample used for induction and need to be subjected to additional tests and comparisons for substantiation. Second, research reports often fail to provide details to allow replication of the research. Third, formal methods for assessing the accuracy and validity of results and findings are limited. Fourth, while many features of scientific research are evident in interpretive induction research, the research moves closer to humanistic knowledge than to science when the basic assumptions of inductive analysis are relaxed—a common occurrence.

An illustrative example of interpretive induction research . Adler and Adler ( 1988 , 1998 ) undertook a five-year participant-observation study of a college basketball program (Adler, 1998 , p. 32). They sought to “examine the development of intense loyalty in one organization.” Intense loyalty evokes “devotional commitment of . . . (organizational) members through a subordination that sometime borders on subservience” (p. 32). The goal was to “describe and analyze the structural factors that emerged as most related” to intense loyalty (p. 32).

The researchers divided their roles. Peter Adler was the active observer and “expert” who undertook direct observations while providing counsel to players (p. 33). Patricia Adler took the peripheral role of “wife” and debriefed the observer. Two research questions were posed: (a) “what” kinds of organizational characteristics foster intense loyalty? (b) “how” do organizations with intense loyalty differ structurally from those that lack intense loyalty?

The first design stage (Table 5 ) recorded unbiased observations in extensive field notes. Detailed “life history” accounts were obtained from 38 team members interviewed (Adler & Adler, 1998 , p. 33). Then analytical induction and the constant comparative method (Glaser & Strauss, 1967 ) were used to classify and compare observations (p. 33). Once patterns emerged, informants were questioned about variations in patterns (p. 34) to develop “total patterns” (p. 34) reflecting the collective belief system of the group. This process required a “careful and rigorous means of data collection and analysis” that was “designed to maximize both the reliability and validity of our findings” (p. 34). The study found five conceptual elements were essential to the development of intense loyalty: domination, identification, commitment, integration, and goal alignment (p. 35).

The “what” question was answered by inducing a generalization (stage 3): paternalistic organizations with charismatic leadership seek people who “fit” the organization’s style and these people require extensive socialization to foster intense loyalty. This description contrasts with rational bureaucratic organizations that seek people who fit specific, generally known job descriptions and require limited socialization (p. 46). The “how” question is answered by inductive creation of another generalization: organizations that control the extra-organizational activities of members are more likely to evoke intense loyalty by forcing members to subordinate all other interests to those of the organization (p. 46).

The Interpretive Abduction Approach

The second stream of interpretive research—interpretive abduction—produces scientific knowledge using qualitative methods (Gephart, 2018 ). The approach assumes that commonsense knowledge is foundational to how actors know the world. Abductive theory is scientifically built from, and refers to, everyday life meanings, in contrast to positivist and interpretive induction research that omits concern with the worldview of members. Further, interpretive abduction produces second-order or scientific theory and concepts from members’ first-order commonsense concepts and meanings (Gephart, 2018 , p. 34; Schutz, 1973a , 1973b ).

The research process, detailed in Table 5 (process and stages), focuses on collecting thick descriptive data on organizations, identifying and interpreting first-order lay concepts, and creating abstract second-order technical constructs of science. The second-order concepts describe the first-order principles and terms social actors use to organize their experience. They compose scientific concepts that form a theoretical system to objectively describe, predict, and explain social organization (Gephart, 2018 , p. 35). This requires researchers to understand the subjective view of the social actors they study, and to develop second-order theory based on actors’ subjective meanings. Subjective meaning can be shared with others through language use and communication and is not private knowledge.

A central analytical task for interpretive abduction is creating second-order, ideal-type models of social roles, motives, and interactions that describe the behavioral trajectories of typical actors. Ideal-type models can be objectively compared to one another and are the special devices that social science requires to address differences between social phenomena and natural phenomena (Schutz, 1973a , 1973b ). The models, once built, are refined to preserve actors’ subjective meanings, to be logically consistent, and to present human action from the actor’s point of view. Researchers can then vary and compare the models to observe the different outcomes that emerge. Scientific descriptions can then be produced, and theories can be created. Interpretive abduction (Gephart, 2018 , p. 35) allows one to addresses what, why, and how questions in a holistic manner, to describe relationships among scientific constructs, and to produce “empirically ascertainable” and verifiable relations among concepts (Schutz, 1973b , p. 65) that are logical, hold practical meaning to lay actors, and provide abstract, objective meaning to interpretive scientists (Gephart, 2018 , p. 35). Abduction produces knowledge about socially shared realities by observing interactions, uncovering members’ first-order meanings, and then developing technical second-order or scientific accounts from lay accounts.

Interpretive abduction (Gephart, 2018 ) uses well-developed methods to create, refine, test, and verify second-order models, and it provides well-developed tools to support technical, second-level analyses. Research using the interpretive abduction approach includes a study of how technology change impacts sales automobile practices (Barley, 2015 ) and an investigation study of how abduction was used to develop new prescription drugs (Dunne & Dougherty, 2016 ).

An illustrative example of the interpretive abduction approach . Perlow ( 1997 ) studied time management among software engineers facing a product launch deadline. Past research verified the widespread belief that long working hours for staff are necessary for organizational success. This belief has adversely impacted work life and led to the concept of a “time bind” faced by professionals (Hochschild, 1997 ). One research question that subsequently emerged was, “what underlies ‘the time bind’ experienced by engineers who face constant deadlines and work interruptions?” (Perlow, 1997 , p. xvii). This is an inductive question about the causes and consequences of long working hours not answered in prior research that is hard to address using induction or deduction. Perlow then explored assumption underlying the hypothesis, supported by lay knowledge and management literature, that even if long working hours cause professionals to destroy their life style, long work hours “further the goals of our organizations” and “maximize the corporation’s bottom line” (Perlow, 1997 , p. 2).

The research commenced (Table 5 , step 1) when Perlow gained access to “Ditto,” a leader in implementing flexible work policies (Perlow, 1997 , p. 141) and spent nine months doing participant observation four days a week. Perlow collected descriptive data by walking around to observe and converse with people, attended meetings and social events, interviewed engineers at work and home and spouses at home, asked participants to record activities they undertook on selected working days (Perlow, 1997 , p. 143), and made “thousands of pages of field notes” (p. 146) to uncover trade-offs between work and home life.

Perlow ( 1997 , pp. 146–147) analyzed first-order concepts uncovered through his observations and interviews from 17 stories he wrote for each individual he had studied. The stories described workstyles, family lives, and traits of individuals; provided objective accounts of subjective meanings each held for work and home; offered background information; and highlighted first-order concepts. Similarities and differences in informant accounts were explored with an empirically grounded scheme for coding observations into categories using grounded theory processes (Gioia, Corley, & Hamilton, 2012 ). The process allowed Perlow to find key themes in stories that show work patterns and perceptions of the requirements of work success, and to create ideal-type models of workers (step 3). Five stories were selected for detailed analysis because they reveal important themes Perlow ( 1997 , p. 147). For example, second-order, ideal-type models of different “roles” were constructed in step 3 including the “organizational superstar” (pp. 15–21) and “ideal female employee” (pp. 22–32) based on first-order accounts of members. The second-order ideal-type scientific models were refined to include typical motives. The models were compared to one another (step 4) to describe and understand how the actions of these employee types differed from other employee types and how these variations produced different outcomes for each trajectory of action (steps 4 and 5).

Perlow ( 1997 ) found that constant help-seeking led engineers to interrupt other engineers to get solutions to problems. This observation led to the abductively developed hypothesis that interruptions create a time crisis atmosphere for engineers. Perlow ( 1997 ) then created a testable, second-order ideal-type (scientific) model of “the vicious working cycle” (p. 96), developed from first-order data, that explains the productivity problems that the firm (and other research and development firms)—commonly face. Specifically, time pressure → crisis mentality → individual heroics → constant interruptions of others’ work to get help → negative consequences for individual → negative consequences for the organization.

Perlow ( 1997 ) then tested the abductive hypothesis that the vicious work cycle caused productivity problems (stage 5). To do so, the vicious work cycle was transformed into a virtuous cycle using scheduling quiet times to prevent work interruptions: relaxed work atmosphere → individuals focus on own work completion → few interruptions → positive consequences for individual and organization. To test the hypothesis, an experiment was conducted (research process 2 in Table 5 ) with engineers given scheduled quiet times each morning with no interruptions. The experiment was successful: the project deadline was met. The hypothesis about work interruptions and the false belief that long hours are needed for success were supported (design stage 6). Unfortunately, the change was not sustained and engineers reverted to work interruptions when the experiment ended.

There are three additional qualitative approaches used in management research that pursue objectives other than producing empirical findings and developing or testing theories. These include critical theory and research, postmodernism, and change intervention research (see Table 6 ).

The Critical Theory and Research Approach

The term “critical” has many meanings including (a) critiques oriented to uncovering ideological manifestations in social relations (Gephart, 2013 , p. 284); (b) critiques of underlying assumptions of theories; and (c) critique as self-reflection that reflexively encapsulates the investigator (Morrow, 1994 , p. 9). Critical theory and critical management studies bring these conceptions of critical to bear on organizations and employees.

Critical theory and research extend the theories Karl Marx, and the Frankfurt School in Germany (Gephart & Kulicki, 2008 ; Gephart & Pitter, 1995 ; Habermas, 1973 , 1979 ; Morrow, 1994 ; Offe, 1984 , 1985 ). Critical theory and research assume that social science research differs from natural science research because social facts are human creations and social phenomena cannot be controlled as readily as natural phenomena (Gephart, 2013 , p. 284; Morrow, 1994 , p. 9). As a result, critical theory often uses a historical approach to explore issues that arise from the fundamental contradictions of capitalism. Critical research explores ongoing changes within capitalist societies and organizations, and analyzes the objective structures that constrain human imagination and action (Morrow, 1994 ). It seeks to uncover the contradictions of advanced capitalism that emerge from the fundamental contradiction of capitalism: owners of capital have the right to appropriate the surplus value created by workers. This basic contradiction produces further contradictions that become sources of workplace oppression and resistance that create labor issues. Thus contradictions reveal how power creates consciousness (Poutanen & Kovalainen, 2010 ). Critical reflection is used to de-reify taken-for-granted structures that create power inequities and to motivate resistance and critique and escape from dominant structures (see Table 6 ).

Critical management studies build on critical theory in sociology. It seeks to transform management and provide alternatives to mainstream theory (Adler, Forbes, & Willmott, 2007 ). The focus is “the social injustice and environmental destruction of the broader social and economic systems” served by conventional, capitalist managers (Adler et al., 2007 , p. 118). Critical management research examines “the systemic corrosion of moral responsibility when any concern for people or for the environment . . . requires justification in terms of its contribution to profitable growth” (p. 4). Critical management studies goes beyond scientific skepticism to undertake a radical critique of socially divisive and environmentally destructive patterns and structures (Adler et al., 2007 , p. 119). These studies use critical reflexivity to uncover reified capitalist structures that allow certain groups to dominate others. Critical reflection is used to de-reify and challenge the facts of social life that are seen as immutable and inevitable (Gephart & Richardson, 2008 , p. 34). The combination of dialogical inquiry, critical reflection, and a combination of qualitative and quantitative methods and data are common in this research (Gephart, 2013 , p. 285). Some researchers use deductive logics to build falsifiable theories while other researchers do grounded theory building (Blaikie, 2010 ). Validity of critical research is assessed as the capability the research has to produce critical reflexivity that comprehends dominant ideologies and transforms repressive structures into democratic processes and institutions (Gephart & Richardson, 2008 ).

An illustrative example of critical research . Barker ( 1998 , p. 130) studied “concertive control” in self-managed work teams in a small manufacturing firm. Concertive control refers to how workers collaborate to engage in self-control. Barker sought to understand how control practices in the self-managed team setting, established to allow workers greater control over their work, differed from previous bureaucratic processes. Interviews, observations, and documents were used as data sources. The resultant description of work activities and control shows that rather than allowing workers greater control, the control process enacted by workers themselves became stronger: “The iron cage becomes stronger” and almost invisible “to the workers it incarcerates” (Barker, 1998 , p. 155). This study shows how traditional participant observation methods can be used to uncover and contest reified structures and taken-for-granted truths, and to reveal the hidden managerial interests served.

Postmodern Perspectives

The postmodern perspective (Boje, Gephart, & Thatchenkery, 1996 ) is based in philosophy, the humanities, and literary criticism. Postmodernism, as an era, refers to the historical stage following modernity that evidences a new cultural worldview and style of intellectual production (Boje et al., 1996 ; Jameson, 1991 ; Rosenau, 1992 ). Postmodernism offers a humanistic approach to reconceptualize our experience of the social world in an era where it is impossible to establish any foundational underpinnings for knowledge. The postmodern perspective assumes that realities are contradictory in nature and value-laden (Gephart & Richardson, 2008 ; Rosenau, 1992 , p. 6). It addresses the values and contradictions of contemporary settings, how hidden power operates, and how people are categorized (Gephart, 2013 ). Postmodernism also challenges the idea that scientific research is value free, and asks “whose values are served by research?”

Postmodern essays depart from concerns with systematic, replicable research methods and designs (Calas, 1987 ). They seek instead to explore the values and contradictions of contemporary organizational life (Gephart, 2013 , p. 289). Research reports have the character of essays that seek to reconceptualize how people experience the world (Martin, 1990 ; Rosenau, 1992 ) and to disrupt this experience by producing “reading effects” that unsettle a community (Calas & Smircich, 1991 ).

Postmodernism examines intertextual relations—how texts become embedded in other texts—rather than causal relations. It assumes there are no singular realities or truths, only multiple realities and multiple truths, none of which are superior to other truths (Gephart, 2013 ). Truth is conceived as the outcome of language use in a context where power relations and multiple realities exist.

From a methodological view, postmodern research tends to focus on discourse: texts and talk. Data collection (in so far as it occurs) focuses on records of discourse—texts of spoken and written verbal communication (Fairclough, 1992 ). Use of formal or official records including recordings, texts and transcripts is common. Analytically, scholars tend to use critical discourse analysis (Fairclough, 1992 ), narrative analysis (Czarniawska, 1998 ; Ganzin, Gephart, & Suddaby, 2014 ), rhetorical analysis (Culler, 1982 ; Gephart, 1988 ; McCloskey, 1984 ) and deconstruction (Calais & Smircich, 1991 ; Gephart, 1988 ; Kilduff, 1993 ; Martin, 1990 ) to understand how categories are shaped through language use and come to privilege or subordinate individuals.

Postmodernism challenges models of knowledge production by showing how political discourses produce totalizing categories, showing how categorization is a tool for social control, and attempting to create opportunities for alternative representations of the world. It thus provides a means to uncover and expose discursive features of domination, subordination, and resistance in society (Locke & Golden-Biddle, 2004 ).

An illustrative example of postmodern research . Martin ( 1990 ) deconstructed a conference speech by a company president. The president was so “deeply concerned” about employee well-being and involvement at work that he encouraged a woman manager “to have her Caesarian yesterday” so she could participate in an upcoming product launch. Martin deconstructs the story to reveal the suppression of gender conflict in the dialogue and how this allows gender conflict and subjugation to continue. This research established the existence of important domains of organizational life, such as tacit gender conflict, that have not been adequately addressed and explored the power dynamics therein.

The Organization Development Approach

OD involves a planned and systematic diagnosis and intervention into an organizational system, supported by top management, with the intent of improving the organization’s effectiveness (Beckhard, 1969 ; Palmer, Dunford, & Buchanan, 2017 , p. 282). OD research (termed “clinical research” by Schein, 1987 ) is concerned with changing attitudes and behaviors to instantiate fundamental values in organizations. OD research often follows the general process of action research (Lalonde, 2019 ) that involves working with actors in an organization to help improve the organization. OD research involves a set of stages the OD practitioner (the leader of the intervention) uses: (a) problem identification; (b) consultation between OD practitioner and client; (c) data collection and problem diagnosis; (d) feedback; (e) joint problem diagnosis; (f) joint action planning; (g) change actions; and (h) further data gathering to move recursively to a refined step 1.

An illustrative example of the organization development approach . Numerous OD techniques exist to help organizations change (Palmer et al., 2017 ). The OD approach is illustrated here by the socioeconomic approach to management (SEAM) (Buono & Savall, 2007 ; Savall, 2007 ). SEAM provides a scientific approach to organizational intervention consulting that integrates qualitative information on work practices and employee and customer needs (socio) with quantitative and financial performance measures (economics). The socioeconomic intervention process commences by uncovering dysfunctions that require attention in an organization. SEAM assumes that organizations produce both (a) explicit benefits and costs and (b) hidden benefits and costs. Hidden costs refer to economic implications of organizational dysfunctions (Worley, Zardet, Bonnet, & Savall, 2015 , pp. 28–29). These include problems in working conditions; work organization; communication, co-ordination, and co-operation; time management; integrated training; and strategy implementation (Savall, Zardet, & Bonnet, 2008 , p. 33). Explicit costs are emphasized in management decision-making but hidden costs are ignored. Yet hidden costs from dysfunctions often greatly outstrip explicit costs.

For example, a fishing company sought to protect its market share by reducing the price and quality of products, leading to the purchase of poor-quality fish (Savall et al., 2008 , pp. 31–32). This reduced visible costs by €500,000. However, some customers stopped purchasing because of the lower-quality product, producing a loss of sales of €4,000,000 in revenue or an overall drop in economic performance of €3,500,000. The managers then changed their strategy to focus on health and quality. They implemented the SEAM approach, assessed the negative impact of the hidden costs on value added and revenue received, and purchased higher-quality fish. Visible costs (expenses) increased by €1,000,000 due to the higher cost for a better-quality product, but the improved quality (performance) cut the hidden costs by increasing loyalty and increased sales by €5,000,000 leaving an increased profit of €4,000,000.

SEAM allows organizations to uncover hidden costs in their operations and to convert these costs into value-added human potential through a process termed “qualimetrics.” Qualimetrics assesses the nature of hidden costs and organizational dysfunctions, develops estimates of the frequencies and amounts of hidden costs in specific organizational domains, and develops actions to reduce the hidden costs and thereby release additional value added for the organization (Savall & Zardet, 2011 ). The qualimetric process is participative and involves researchers who use observations, interviews and focus groups of employees to (a) describe, qualitatively, the dysfunctions experienced at work (qualitative data); (b) estimate the frequencies with which dysfunctions occur (quantitative data); and (c) estimate the costs of each dysfunction (financial data). Then, strategic change actions are developed to (a) identify ways to reduce or overcome the dysfunction, (b) estimate how frequently the dysfunction can be remedied, and (c) estimate the overall net costs of removing the hidden costs to enhance value added. The economic balance is then assessed for changes to transform the hidden costs into value added.

OD research creates actionable knowledge from practice (Lalonde, 2019 ). OD intervention consultants use multistep processes to change organizations that are flexible practices not fixed research designs. OD plays an important role in developing evidence-based practices to improve organizational functioning and performance. Worley et al. ( 2015 ) provide a detailed example of the large-scale implementation of the SEAM OD approach in a large, international firm.

Here we discuss implication of qualitative research designs for covert research, reporting qualitative work and novel integrations of qualitative and quantitative work.

Covert Research

University ethics boards require researchers who undertake research with human participants to obtain informed consent from the participants. Consent requires that all participants must be informed of details of the research procedure in which they will be involved and any risks of participation. Researchers must protect subjects’ identities, offer safeguards to limit risks, and insure informant anonymity. This consent must be obtained in the form of a signed agreement from the participant, obtained prior to the commencement of research observations (McCurdy et al., 2005 , pp. 29–32).

Covert research that fails to fully disclose research purposes or practices to participants, or that is otherwise deceptive by design or tacit practice, has long been considered “suspect” in the field (Graham, 1995 ; Roulet, Gill, Stenger, & Gill, 2017 ). This is changing. Research methodologists have shown that the over/covert dimension is a continuum, not a dichotomy, and that unintended covert elements occur in many situations (Roulet et al., 2017 ). Thus all qualitative observation involves some degree of deception due practical constraints on doing observations since it is difficult to do fully overt research, particularly in observational contexts with many people, and to gain advance consent from everyone in the organization one might encounter.

There are compelling benefits to covert research. It can provide insights not possible if subjects are fully informed of the nature or existence of the research. For example, the year-long, covert observational study of an asylum as a “total institution” (Goffman, 1961 ) showed how ineffective the treatment of mental illness was at the time. This opened the field of mental health to social science research (Roulet et al., 2017 , p. 493). Covert research can also provide access to institutions that researchers would otherwise be excluded from, including secretive and secret organizations (p. 492). This could allow researchers to collect data as an insider and to better see and experience the world from members’ perspective. It could also reduce “researcher demand effects” that occur when informants obscure their normal behavior to conform to research expectations. Thus, the inclusion of covert research data collection in research designs and proposals is an emerging trend and realistic possibility. Ethics applications can be developed that allow for aspects of covert research, and observations in many public settings do not require informed consent.

The Appropriate Style for Reporting Qualitative Work

The appropriate style for reporting qualitative research has become an issue of concern. For example, editors of the influential Academy of Management Journal have noted the emergence of an “AMJ style” for qualitative work (Bansal & Corley, 2011 , p. 234). They suggest that all qualitative work should use this style so that qualitative research can “benefit” from: “decades of refinement in the style of quantitative work.” The argument is that most scholars can assess the empirical and theoretical contributions of quantitative work but find it difficult to do so for qualitative research. It is easier for quantitatively trained editors and scholars “to spot the contribution of qualitative work that mimics the style of quantitative research.” Further, “the majority of papers submitted to . . . AMJ tend to subscribe to the paradigm of normal science that aims to find relationships among valid constructs that can be replicated by anyone” (Bansal, Smith, & Vaara, 2018 , p. 1193). These recommendations appear to explicitly encourage the reporting of qualitative results as if they were quantitatively produced and interpreted and highlights the advantage of conformity to the prevailing positivist perspective to gain publication in AMJ.

Yet AMJ editors have also called for researchers to “ensure that the research questions, data, and analysis are internally consistent ” (Bansal et al., 2018 , p. 1193) and to “Be authentic , detailed and clear in argumentation” (emphasis added) (Bansal et al., 2018 , p. 1193). These calls for consistency appear to be inconsistent with suggestions to present all qualitative research using a style that mimics quantitative, positivist research. Adopting the quantitative or positivist style for all qualitative reports may also confuse scholars, limit research quality, and hamper efforts to produce innovative, non-positivist research. This article provides six qualitative research designs to ensure a range of qualitative research publications are internally consistent in methods, logics, paradigmatic commitments, and writing styles. These designs provide alternatives to positivist mimicry in non-positivist scholarly texts.

Integrating Qualitative and Quantitative Research in New Ways

Qualitative research often omits consideration of the naturally occurring uses of numbers and statistics in everyday discourse. And quantitative researchers tend to ignore qualitative evidence such as stories and discourse. Yet knowledge production processes in society “rely on experts and laypeople and, in so doing, make use of both statistics and stories in their attempt to represent and understand social reality” (Ainsworth & Hardy, 2012 , p. 1649). Numbers and statistics are often used in stories to create legitimacy, and stories provide meaning to numbers (Gephart, 1988 ). Hence stories and statistics cannot be separated in processes of knowledge production (Ainsworth & Hardy, 2012 , p. 1697). The lack of attention to the role of quantification in everyday life means a huge domain of organizational discourse—all talk that uses numbers, quantities, and statistics—is largely unexplored in organizational research.

Qualitative research has, however, begun to study how words and numbers are mutually used for organizational storytelling (Ainsworth & Hardy, 2012 ; Gephart, 2016 ). This focus offers the opportunity to develop research designs to explore qualitative features and processes involved in quantitative phenomena such as financial crises (Gephart, 2016 ), to address how stories and numbers need to work together to create legitimate knowledge (Ainsworth & Hardy, 2012 ), and to show how statistics are used rhetorically to convince others of truths in organizational research (Gephart, 1988 ).

Ethnostatistics (Gephart, 1988 ; Gephart & Saylors, 2019 ) provides one example of how to integrate qualitative and quantitative research. Ethnostatistics examines how statistics are constructed and used by professionals. It explores how statistics are constructed in real settings, how violations of technical assumptions impact statistical outcomes, and how statistics are used rhetorically to convince others of the truth of research outcomes. Ethnostatistics has been used to reinterpret data from four celebrated network studies that themselves were reanalyzed (Kilduff & Oh, 2006 ). The ethnostatistical reanalyses revealed how ad hoc practices, including judgment calls and the imputation of new data into old data set for reanalysis, transformed the focus of network research from diffusion models to structural equivalence models.

Another innovative study uses a Bayesian ethnostatistical approach to understand how the pressure to produce sophisticated and increasingly complex theoretical narratives for causal models has impacted the quantitative knowledge generated in top journals (Saylors & Trafimow, 2020 ). The use of complex causal models has increased substantially over time due to a qualitative and untested belief that complex models are true. Yet statistically speaking, as the number of variables in a model increase, the likelihood the model is true rapidly decreases (Saylors & Trafimow, 2020 , p. 3).

The authors test the previously untested (qualitative) belief that complex causal models can be true. They found that “the joint probability of a six variable model is about 3.5%” (Saylors & Trafimow, 2020 , p. 1). They conclude that “much of the knowledge generated in top journals is likely false” hence “not reporting a (prior) belief in a complex model” should be relegated to the set of questionable research practices. This study shows how qualitative research that explores the lay theories and beliefs of statisticians and quantitative researchers can challenge and disrupt conventions in quantitative research, improve quantitative practices, and contribute qualitative foundations to quantitative research. Ethnostatistics thus opens the qualitative foundations of quantitative research to critical qualitative analyses.

The six qualitative research design processes discussed in this article are evident in scholarly research on organizations and management and provide distinct qualitative research designs and approaches to use. Qualitative research can provide research insights from several theoretical perspectives, using well-developed methods to produce scientific and scholarly insights into management and organizations. These approaches and designs can also inform management practice by creating actionable knowledge. The intended contribution of this article is to describe these well-developed methods, articulate key practices, and display core research designs. The hope is both to better equip researchers to do qualitative research, and to inspire them to do so.

Acknowledgments

The authors wish to acknowledge the assistance of Karen Lund at The University of Alberta for carefully preparing Figure 1 . Thanks also to Beverly Zubot for close reading of the manuscript and helpful suggestions.

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1. The fourth logic is retroduction. This refers to the process of building hypothetical models of structures and mechanisms that are assumed to produce empirical phenomena. It is the primary logic used in the critical realist approach to scientific research (Avenier & Thomas, 2015 ; Bhaskar, 1978 ). Retroduction requires the use of inductive or abductive strategies to discover the mechanisms that explain regularities (Blaikie, 2010 , p. 87). There is no evident logic for discovering mechanisms and this requires disciplined scientific thinking aided by creative imagination, intuition, and guesswork (Blaikie, 2010 ). Retroduction is likr deduction in asking “what” questions and differs from abduction because it produces explanations rather than understanding, causes rather than reasons, and hypothetical conceptual mechanisms rather than descriptions of behavioral processes as outcomes. Retroduction is becoming important in the field but has not as yet been extensively used in management and organization studies (for examples of uses, see Avenier & Thomas, 2015 ); hence, we do not address it at length in this article.

Qualitative Research in International Business

Qualitative research (e.g. case study, interviews, ethnography, visual inquiry) permits to analyze events “from the inside out”, allowing a conceptualization from the standpoints of the actors at work. Qualitative research, with its emphasis on precise and ‘thick’ descriptions, captures the complex nature of rich life experiences and yields a nuanced understanding of social realities, drawing attention to processes, meaning patterns and structural features.

In the field of International Business, qualitative research is thus appropriate for opening the “black box” of organisational processes, helping to explore “how” and “why” firms internationalise. Qualitative studies indeed play a critical role to interpret and understand in depth the complex plurality of contexts- e.g. spatial, temporal, cultural, institutional, geographic and economic – that organisations encounter when operating beyond domestic borders. It is interesting to explore on how qualitative research can move the field of International Business forward by building, enriching and testing relevant theories.

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Research methods in international business: The challenge of complexity

Lorraine eden.

1 Department of Management, Mays Business School, Texas A&M University, TAMU 4221, College Station, TX 77843-4221 USA

Bo Bernhard Nielsen

2 Discipline of International Business, The University of Sydney Business School, Abercrombie Building H70, Corner Abercrombie Street and Codrington St, Darlington, NSW 2006 Australia

3 Department of Strategy and Innovation, Copenhagen Business School, Kilevej 14, 2000 Frederiksberg, Denmark

International business (IB) research is designed to explore and explain the inherent complexity of international business, which arises from the multiplicity of entities, multiplexity of interactions, and dynamism of the global economic system. To analyze this complexity, IB scholars have developed four research lenses: difference, distance, diversity, and disparity. These four lenses on complexity have created not only unique research opportunities for IB scholarship but also unique research methodological challenges. We therefore view complexity as the underlying cause of the unique methodological challenges facing international business research. We offer several recommendations to help IB scholars embrace this complexity and conduct reliable, interesting, and practically relevant research.

Résumé

La recherche en international business (IB) est conçue pour explorer et expliquer la complexité inhérente aux affaires internationales qui découle de la multiplicité des entités, de la multiplexité des interactions et du dynamisme du système économique mondial. Pour analyser cette complexité, les chercheurs en IB ont développé quatre optiques de recherche - différence, distance, diversité et disparité - qui créent non seulement des opportunités de recherche uniques pour les chercheurs en IB, mais aussi des défis méthodologiques uniques de recherche. Par conséquent, nous considérons la complexité comme la cause sous-jacente des défis méthodologiques uniques auxquels est confrontée la recherche en international business . Nous proposons plusieurs recommandations pour aider les chercheurs en IB à appréhender cette complexité et à mener des recherches fiables, intéressantes et pertinentes sur le plan pratique.

La investigación en negocios internacionales está diseñada para explorar y explicar la complejidad inherente a los negocios internacionales, la cual surge de la multiplicidad de entidades, multiplejidad de interacciones y dinamismos del sistema económico global. Para analizar esta complejidad, los académicos de negocios internacionales han desarrollado cuatro lentes de investigación -diferencia, distancia, diversidad y disparidad- que crean no solamente oportunidades de investigación únicas para el conocimiento académico de negocios internacionales, pero también retos metodológicos de investigación únicos. Por lo tanto, vemos la complejidad como la causa subyacente de los retos metodológicos únicos enfrentados en la investigación de negocios internacionales . Ofrecemos varias recomendaciones para ayudar a los académicos de negocios internacionales a adoptar esta complejidad y llevar a cabo investigaciones confiables, interesantes y prácticamente relevantes.

A pesquisa em negócios internacionais (IB) é projetada para explorar e explicar a complexidade inerente aos negócios internacionais, que surge da multiplicidade de entidades, multiplexidade de interações e dinamismo do sistema econômico global. Para analisar essa complexidade, acadêmicos de IB desenvolveram quatro lentes de pesquisa - diferença, distância, diversidade e disparidade - que criam não apenas oportunidades de pesquisa exclusivas para a pesquisa em IB, mas também exclusivos desafios metodológicos de pesquisa. Portanto, vemos a complexidade como a causa subjacente dos desafios metodológicos únicos enfrentados pela pesquisa em negócios internacionais . Oferecemos várias recomendações para ajudar acadêmicos de IB a abraçar essa complexidade e conduzir pesquisas confiáveis, interessantes e relevantes na prática.

抽象

国际商务(IB)研究旨在探索和解释由实体多样性、互动多元复杂性和全球经济体系动态性引起的国际商业内在的复杂性。为了分析这种复杂性, IB学者开发了四个研究视角, 即差异、距离、多样化和不均衡视角, 这不仅为IB理论创造了独特的研究机会, 而且带来研究方法上独特的挑战。我们因此将复杂性视为国际商务研究所面临的独特的方法论挑战的根本原因。我们提出了一些建议, 以帮助IB学者拥抱这种复杂性并进行可靠的、有趣的和切实的研究。

INTRODUCTION

It is a well-accepted fact that high-quality research methods are a necessary building block for strong scholarship in international business (IB) research. Many scholars have written about the methodological challenges that can bedevil scholarship in IB and other disciplines and have recommended best practices for dealing with these challenges. For example, see the wide variety of methodology challenges discussed in Eden, Nielsen and Verbeke ( 2020 ) and recent papers by Aguinis and co-authors (Aguinis, Cascio & Ramani, 2017 ; Aguinis, Hill & Bailey, 2019 ; Aguinis, Ramani & Alabduljader, 2018 ; Bergh, Sharp, Aguinis & Li, 2017 ).

The new JIBS Point article by Aguinis, Ramani and Cascio ( 2020 ) follows in this tradition, providing a useful analysis of the “four most pervasive contemporary methodological choices faced by international business (IB) researchers.” Our interest lies in the unique aspects of IB research and thus our paper is designed to serve as a Counterpoint and complement to their JIBS Point article. We argue that IB research questions are designed to explore and explain the inherent complexity of the global economy, which is generated by three factors: multiplicity of entities (i.e., number and variety of actors, industries, countries, institutions, etc.), multiplexity of interactions (i.e., number and variety of ties or relationships among these entities), and dynamism over time (i.e., changing nature of the international business system). To analyze the complexity of the IB system, scholars have developed four lenses of research, which we refer to as the “four D’s” (difference, distance, diversity, and disparity). These four lenses on complexity have created unique research opportunities for IB scholars but have also presented unique research methodology problems. We therefore argue that complexity is the underlying cause of the unique methodological problems facing international business research.

Our Counterpoint article first highlights Aguinis et al.’s ( 2020 ) helpful advice for improving the quality of IB research and discusses some of the article’s limitations. We then turn to developing our thesis on the complexity of IB research, the four research lenses that can be used to analyze complexity, their resulting methodological problems, and proposed methodology solutions.

A BRIEF ASSESSMENT OF AGUINIS ET AL. ( 2020 )

Contributions.

Aguinis et al.’s ( 2020 ) article on challenges and recommended best practices in IB research methodology is a welcome addition to the literature on this topic. The authors identified the most pervasive methodological challenges faced by IB researchers by counting the self-reported research methodology problems in the 43 empirical articles published in the 2018 volume of the Journal of International Business Studies ( JIBS ). Using this method, Aguinis et al. ( 2020 ) identified four methodological challenges (percentage of JIBS articles in brackets): psychometrically deficient measures (73%), idiosyncratic samples or contexts (62%), less-than-ideal research designs (62%), and insufficient evidence about causal relations (8%). The authors explored each challenge and proposed some solutions.

The most frequently mentioned challenge (in almost three-quarters of the JIBS articles) was that the measures used were psychometrically deficient; i.g., the measures did not fully capture the construct or were not sufficiently reliable. Aguinis et al. ( 2020 ) proposed three solutions. IB scholars should: (1) determine whether the measure has been used previously to represent a different construct and, if so, demonstrate why their conceptualization is appropriate; (2) specify whether the construct is reflective or formative and, depending on the answer, apply the appropriate analytical technique; and (3) use multiple indicators to measure the construct.

The second and third challenges were reported in identical percentages of JIBS papers (62.2%), suggesting that JIBS authors coupled the two challenges together. Examples of the second challenge, idiosyncratic samples or contexts, included testing IB theories in a single country or market or during a particular time period. Solutions proposed by Aguinis et al. ( 2020 ) were to (1) treat the sample as an opportunity to go deeper, rather than as a limitation, and (2) choose unique or extreme samples or contexts. The third challenge, less-than-ideal research designs, involved questions such as multiple levels of analysis and common method variance. Recommended solutions were to (1) use Big Data to create unique insights and (2) leverage Big Data techniques to re-analyze currently available data.

The fourth challenge, insufficient evidence to infer causal relations, was reported by very few JIBS authors. Those who mentioned this issue referenced comments regarding distinguishing causality from correlation and the inability of current research methods to answer causality. To address this issue, Aguinis et al. ( 2020 ) proposed that JIBS authors use (1) quasi-experimental designs and (2) necessary-conditions analysis.

Limitations

The JIBS Point article by Aguinis et al. ( 2020 ) addresses important methodological issues. The article, however, suffers from at least three limitations, which we discuss below.

First, only one year of JIBS (2018) empirical articles was analyzed. While there is no reason to think that 2018 was an outlier year, there would have been several benefits to analyzing a longitudinal dataset. Longitudinal data allow for a more informed discussion of the limitations over time (and hence potential changes/evolutions) and provide potentially deeper insights into the importance of these limitations.

Table  1 provides some information on the general types of research methods employed by JIBS authors during the first 50 years of the journal. Of the 1265 empirical articles, nearly 30% (372 articles) were published in the most recent decade (2010–2019). Most of these 372 articles (86%) used quantitative methods (archival or survey); another 9% used qualitative methods; and the remainder (5%) used mixed methods. Clearly evident over the 50-year time period are the shifts in the relative importance of different research methods. Notable has been the growing importance of archival methods, which almost doubled from 37% as a proportion of all JIBS empirical papers in the 1970s to 62% in the 2010s, and the decline of survey methods, which fell by almost half (from 40% to 24%) of empirical papers over the same years. Papers using qualitative methods fell from 16% in the 1970s to a low of 3% in the 1990s and have now rebounded to 9% in the 2010s.

Table 1

Distribution of JIBS articles by research methodology, 1970–2019.

Source : Authors’ calculations based on data provided by Nielsen et al. ( 2020 )

Italicized numbers represent share of the total

a This table only includes JIBS publications using quantitative, qualitative, or mixed methods. The table excludes articles that are, for example, conceptual, theoretical, or editorial in nature.

It is therefore possible that examining one year rather than several years may have affected the relative shares of methods used and the resulting methodological challenges, or at least the frequencies of reports, identified in Aguinis et al. ( 2020 ). For example, the relatively low percentage given to challenge #4 (inference of causality) may have been due to the few survey papers in JIBS that year. Following the example of Brutus, Aguinis, and Wassmer ( 2013 ), which according to the authors was influential for their article methodologically, we conclude that at least five and preferably 10 years of data would have been helpful for understanding why JIBS authors identified particular research challenges and not others.

A second limitation is that the method used by Aguinis et al. ( 2020 ) was counting self-reports by JIBS authors. This is problematic for several reasons. First, the simple yardstick used (counting zero or one for whether the authors of a JIBS article mentioned a methods problem or not) is a coarse measure and not very informative. For example, it would have been useful to know whether, after having listed a methodological problem, the JIBS authors also explained whether and how they tried (or did not try) to address the problem. Second, the JIBS authors’ own assessment of the problem would have been helpful. Did they see the methodological challenge as material (i.e., could it have substantially affected the outcome of the paper) and, if so, did they assess what the likely impact would have been? Third, perhaps the JIBS authors may have gone further and identified in their paper why they had not addressed the challenge (e.g., they saw the issue as non-material, appropriate data did not exist at this point in time, or there was no method available to handle this particular problem). Fourth, a deeper analysis could have looked at whether there really was a problem or not, in other words, did the JIBS authors list too many or too few problems? Lastly, JIBS authors know they are expected to have a Discussion section where they discuss the limitations of their paper (e.g., Aguinis and his co-authors also follow this convention). Were the JIBS authors simply “checking the box” in their Limitations section? In sum, a comprehensive analysis of the research methodology problems in current JIBS articles would have benefitted from a much deeper assessment of the original JIBS articles. Given the focus on a single year and resulting limited number of articles (43), the “case study” approach (Aguinis et al., 2020 ) to analyzing JIBS methodological challenges falls somewhat short of meeting its goals.

A third and perhaps the most important limitation of Aguinis et al. ( 2020 ) from our perspective is that their four identified core research methodology issues are not unique to IB research. While the percentages may differ across disciplines (see their discussion regarding Brutus et al.’s ( 2013 ) assessment of management journal articles), the identified methodological problems and proposed solutions appear to be common across business and psychology journals rather than unique to IB research. The authors acknowledge this, noting that they used JIBS as a case study: “Secondly, our focus on recent JIBS articles is not intended to target this journal, or more broadly, the field of IB. For example, authors of articles published in Academy of Management Journal (AMJ), Strategic Management Journal (SMJ), Journal of Management (JOM), and Journal of Applied Psychology (JAP) have identified some of the challenges also referred to by JIBS authors” (Aguinis et al., 2020 ).

Self-reports by JIBS authors in 2018, as identified in Aguinis et al. ( 2020 ), suggest that IB research currently faces four major methodological challenges: measures, samples, research design, and causality. We applaud the authors’ efforts to address these important issues but have some concerns about the methods used in their paper and the lack of adequate attention to contextual influences resulting from the complexity of IB phenomena. Moreover, some of their challenges and solutions appear to be “micro” in nature, focusing on issues that may present major problems for scholars engaged in predominantly quantitative (survey) studies with particular psychometric properties (e.g., reflective versus formative measures and multiple versus single indicators to measure constructs). We conclude that their article makes a valuable contribution but should be treated with caution and recommend that IB scholars read both the JIBS Point and Counterpoint articles together.

Other Studies

For comparison purposes, we searched for other studies that have used methods similar to Aguinis et al. ( 2020 ) and Brutus et al. ( 2013 ) to identify methodology challenges relevant to IB research. We highlight two below and also acknowledge Andersen and Skaates ( 2004 ).

Peterson ( 2004 ) examined the research methods used in 124 international management (IM) articles published in three journals ( JIBS , AMJ, and Administrative Sciences Quarterly (ASQ) ) between 1990 and 1999. His analysis identified five methodological concerns in IM research: (1) non-representative or within-country samples, (2) limited data sources (only one or two countries), (3) lack of author diversity (one or two authors from the same country), (4) lack of examination of cross-cultural/national differences, and (5) excessive reliance on one research method (typically correlations and regressions), so that neither causality and nuances could be addressed. His proposed five solutions, respectively, were: (1) samples drawn from the whole country, (2) larger sample populations with more countries over at least 5–10 years, (3) cross-national research teams that meet periodically, (4) the use of standardized survey and research methods across countries, and (5) the use of multiple (mixed) research methods.

A second comparative study is Coviello and Jones ( 2004 ), which used content analysis to examine 55 articles on international entrepreneurship (IE) published in ten business journals (including JIBS ) between 1989 and 2002. The authors assessed the articles in terms of four methods issues: (1) time frame and content, (2) sample, (3) data collection and analysis, and (4) cross-national equivalence. Their key criticisms were that most articles involved static cross-country or cross-industry comparisons, had inconsistent definitions and measures of key variables, used idiosyncratic samples that led to results that were difficult to generalize, and did not capture complex processes well. Coviello and Jones ( 2004 ) argued that these methodological problems were inherent in the complexities involved in doing IE research. The authors concluded that IE scholars needed to take a multidisciplinary approach, adopt dynamic research designs that integrated positivist and interpretivist methodologies, and incorporate time as a key dimension.

Both Peterson ( 2004 ) and Coviello and Jones ( 2004 ) highlight similar complexities involved in doing IB research, despite their focus on different disciplines (management vs. entrepreneurship). Both articles stress that core methodological problems are caused by differences and diversities in cultures and contexts that are dynamic not static in nature. We concur with their assessment and go further to argue below that complexity is the underlying source of the unique methodological challenges faced by international business scholars .

THE COMPLEXITY OF IB RESEARCH

We of course agree with Aguinis et al. ( 2020 ) that IB researchers face many methodological problems and choices. Our interest lies, however, less in the commonalities of these problems with other disciplines and more with the unique methodological concerns that are specifically “IB”; i.e., caused by research questions and cross-border contexts typically studied by IB scholars and published in JIBS , some of which are highlighted in Peterson ( 2004 ) and Coviello and Jones ( 2004 ).

Complexity in IB Research

We start with a simple metaphor explaining why IB is different from mainstream disciplines like management and psychology. Eden ( 2008 ) suggested that a helpful way to understand IB research is to conceptualize a matrix where the columns are the disciplines or functional areas of business (e.g., management, entrepreneurship, finance) and the rows are the topics typically covered in these disciplines (e.g., markets, firm strategy, performance, international). IB research can therefore be viewed as the “international” row that cuts across the “discipline” columns.

Eden ( 2008 ) argued that JIBS researchers are boundary-spanners; they emphasize the adjective “international” over the noun of their particular discipline or university department. Implicit in this approach is the insight that IB researchers are not only engaged in studying business in cross-border contexts but also in cross-cultural and cross-disciplinary contexts. The domain of IB research is, in effect, a big umbrella covering the international/cross-border aspects of all business disciplines. Thus, IM and IE can be viewed as subfields of IB (see also discussions in Eden, Dai and Li ( 2010 ) on IM and IB and in Verbeke & Ciravegna ( 2018 ) on IE and IB).

The variety and breadth of research topics in the IB domain is therefore huge, ambitious, and challenging (Table  2 ). As a result, there is an inherent complexity to IB research that is different from domestically focused scholarship, and the research methodology challenges faced by IB researchers should not be simply conflated with methodological issues facing scholars in mainstream disciplines.

Table 2

The domain of international business studies.

Source : Eden ( 2008 : 3)

We believe there are three key sources to the complexity of IB research, which we illustrate in Figure ​ Figure1: 1 : multiplicity, multiplexity, and dynamism. The first source of complexity is the multiplicity (i.e., the number and variety) of entities (e.g., actors, industries, countries, contexts, cultures, institutions) in the global economic system. While often pictured as a dyad (home versus foreign), in reality most IB studies involve multiple actors in multiple countries in multiple contexts. Multiplicity creates both opportunities and problems for IB research; see, for example, the discussions in Buckley and Casson ( 2001 ), Peterson ( 2004 ), Coviello and Jones ( 2004 ), and Teagarden, Von Glinow and Mellahi ( 2018 ).

The complexity of international business research.

Multiplicity, which Buckley and Casson ( 2001 ) refer to as “combinatorial complexity”, can be addressed in many ways. Buckley and Casson recommend using parsimony and simplifying, rational-actor techniques such as real options and game theory; they provide several examples of how these techniques can be used to analyze problems such as mode of entry and location choice. Applying rational-actor economics to multiplicity has clear benefits but also some costs (Samuels, 1995 ). Other possible approaches focus more on how cross-border activities exacerbate the joint challenges of managing bounded rationality, unreliability, and investments in specific assets. Here conceptual tools from comparative institutional analysis and empirical tools such as fuzzy-set qualitative comparative analysis, as well as a variety of multi-level analysis tools, can be helpful (see Eden et al. ( 2020 ) for discussions of appropriate techniques).

The second factor contributing to complexity of the global economy and thus of IB research is the multiplexity of interactions (the number and variety of relationships and interdependencies) among these entities, which Buckley and Casson ( 2001 ) refer to as “organic complexity.” IB scholars have studied multiplexity for many years in contexts such as the MNE’s inter- and intra-organizational networks, buyer–supplier networks using lean production technologies, and international strategic alliances (Cuypers, Ertug, Cantwell, Zaheer & Kilduff, 2020 ). Multiplexity is created when there are “networks of networks” (D’Agostino & Scala, 2014 ), generating systemic problems such as cross-level effects, feedback loops, diffusion, and contagion. See, for example, Cardillo et al.’s ( 2013 ) analysis of the multiplexity of the international air transportation network and Gemmetto et al.’s ( 2016 ) study of the relationships and interdependencies of world trade flows; both papers use network theory to analyze the multiplexity of cross-border flows.

Buckley and Casson ( 2001 ) argues that rational actor approaches can be used to address multiplexity, pointing to information costs, dynamic optimization, real options, and game theory as appropriate techniques for handling the dynamism of the IB system. Other approaches to multiplexity include fuzzy-set qualitative comparative analysis, multi-level analysis techniques, and qualitative research (Eden et al., 2020 ; D’Agostino & Scala, 2014 ; Ferriani, Fonti & Corrado, 2012 ).

The third factor generating complexity for IB research is the global economy’s inherent dynamism (dynamics over time). By dynamism, we mean the various ways that time and history can affect a system such as trends, hysteresis, business cycles, crises, and other instabilities. The dynamism of the international business system generates risk, uncertainty, volatility, and ambiguity, providing both challenges and opportunities for decision-makers. Many scholars have stressed the importance of history and time to IB research (e.g., Jones & Khanna, 2006 ; Coviello & Jones, 2004 ; Eden, 2009 ). Bringing dynamism into IB research can be done using a variety of research methods, including longitudinal case studies, real options approaches, event studies, and event history analysis. Each of these approaches also raises its own methodology challenges, some of which are discussed in Eden et al. ( 2020 ).

Four Research Lenses on Complexity

To analyze the complexity of the international business system, IB scholars have developed four research lenses, which we refer to as the “four D’s” (difference, distance, diversity, and disparity) and illustrate in Figure ​ Figure1. 1 . The first – “Difference” – involves the relatively simple matter of comparing how “here” is different from “there” (e.g., cross-border comparisons of domestic with foreign). Early research in IB (e.g., the Ownership-Location-Internalization (OLI) paradigm) focused on the differences that businesses faced when they crossed national borders and one still regularly hears IB research referred to as “cross-border” or “cross-cultural” studies. The focus of “Difference” is on the border as a metaphor for separating “here” (the known or us) from “there” (the unknown or them). Research on topics varying from offshore production to liability of foreignness to insiders and outsiders all share this crossing-a-border “Difference” lens.

“Distance” became a second important research lens for IB scholars after the introduction of new datasets and metrics that could be used to measure the cultural and institutional distances between countries. Early users of Hofstede’s ( 1980 ) cultural dimensions, for example, explored the impact of cultural distance on foreign mode of entry (e.g., Kogut & Singh, 1988 ). Distance studies, using these new datasets and metrics, have been a dominant theme of IB research for nearly 30 years (see reviews in Beugelsdijk, Ambos & Nell ( 2018 ) and Maseland, Dow & Steel ( 2018 )).

“Diversity” – the third “D” – is a newer focus of IB researchers interested in exploring, for example, varieties of capitalism and variations within and across countries (see also Stahl, Tung, Kostova & Zellmer-Bruhn, 2016 ). Diversity pays attention to the multiplicity of actors and networks and the multiplexity of their interactions. Diversity is inherent in multiplexity and may involve new research metrics and methods. Dai, Eden and Beamish ( 2013 ), for example, show how Coulombe’s Law can be used to calculate the dynamic exposure faced by a foreign subsidiary surrounded by multiple war zones of different sizes at different distances and points of time. Peterson, Arregle and Martin ( 2012 ) provides a useful introduction to multilevel models that can be used to analyze diversity issues.

We believe that the fourth “D” – “Disparity” – is on the horizon and will become an important topic for IB researchers in the 2020s and 2030s. The call for IB researchers to engage more with global societal challenges (Buckley, Doh & Benischke, 2017 ), the growing importance of the new group Responsible Research in Business and Management ( http://www.rrbm.network ), and the launch of the Journal of International Business Policy , all suggest more attention is being paid by IB scholars to the massive inequalities that exist across and within countries. The current global pandemic caused by COVID-19 is likely to exacerbate these cross-country disparities. We predict that more IB research in the future will examine the role that international business plays in society, in both ameliorating and exacerbating disparity and inequality, bringing their own research methodology challenges (Schlegelmilch & Szöcs, 2020 ; Crane, Henriques & Husted, 2018 ).

We therefore conclude that complexity – generated by the multiplicity of entities, multiplexity of interactions, and dynamism of the global economy - is the underlying cause of the unique methodological challenges facing international business research. The four lenses on complexity – difference, distance, diversity, and disparity – offer unique research challenges and opportunities for IB scholars and, as a result, have also presented them with unique research methodology problems, to which we now turn.

COPING WITH THE COMPLEXITY OF IB RESEARCH

We view complexity as the keyword that best captures IB research; that is, what it means to put the adjective “international” together with the noun “business” in the matrix that defines the “IB” field. Below we discuss the implications of complexity for the methodology challenges facing IB scholars. We organize these challenges according to the timeline of a typical IB research process, building on Nielsen, Eden and Verbeke ( 2020 ): (1) problem definition and research question, (2) research design and data collection, and (3) data analysis and interpretation of results. In each phase, we focus on the complexity issues that are prevalent and/or unique to IB research, the methodology challenges they pose, and recommend possible solutions (see Figure ​ Figure1 1 ).

Phase 1: Problem Definition and Research Question

In Phase 1, the researcher or research team must identify and define the problem and question(s) that will drive the project. Here, we see at least three methodological challenges.

Defining the research problem

IB requires attention to both the similarities and differences between and across domestic and foreign operations at multiple levels of analysis (e.g., firm, industry, country). Isolating the international (cross-border) aspects of a study requires a deep understanding of domestic and foreign environments. Thus, both the multiplicity of actors and multiplexity of interactions create complexity in defining the research problem. We suggest that looking at the research problem through the lenses of the “four D’s” (difference, distance, diversity, and disparity) can provide an fruitful avenue for attending to the complex set of issues across multiple contextual dimensions, including setting, unit, location, and time.

The (non)equivalency of concepts and theories used in different contexts

Much IB research involves applying “standard” theories (e.g., internalization, transaction cost economics, resource-based view) to particular types of firms. However, the assumptions of these theories and their applicability are likely to vary across countries. IB scholars need to identify and account explicitly for contextual influences and their potential impacts on the design and interpretation of outcomes of their study. Contextual issues are critical for determining the boundaries within which particular theories may be applicable. Studies of state ownership, for example, may yield very different results when the state-owned multinationals are from China, Norway or Brazil, given the different institutional contexts of these countries. Once again, an explicit focus on the sources of complexity may help IB researchers discern how, why, where, and when concepts and theories are equivalent (or not) in different contexts.

Promising too much and delivering too little

While most scholars start with a “big” research question (e.g., how distance or diversity affects a particular MNE strategy), in practice, their empirical study is much more narrowly defined. IB scholars may end up overestimating the generalizability of their results, leading to exaggerated claims that “promise too much.” Selection of the research question should drive the data collection and choice of methodology stages, and the way the results are reported and interpreted, not the other way around.

Phase 2: Research Design and Data Collection

In the second stage where researchers are engaged in research design and data collection, there are at least three core methodological challenges.

Appropriateness of the sample

IB scholars typically prefer to use data from secondary sources such as national and international (e.g., US and UN) statistical agencies and private firms (e.g., Thomson Reuters, Standard & Poor’s). However, particularly in developing countries, such data sources are either not available or are often of questionable quality. Moreover, IB researchers often assume implicitly that all sampled entities within-country share the same characteristics, with differences existing only across countries. This assumption may be wishful thinking as differences within countries (especially between rural and city areas in developing countries) may be larger than across countries, as noted by Peterson ( 2004 ). When the samples are inadequate, of course, the results will be problematic. Aguinis et al. ( 2020 ) identified idiosyncratic samples and contexts as a methodological concern in 62% of their sampled JIBS articles. We contend that a stronger focus on understanding the types of complexity during data collection may help prevent inadequate sampling in IB studies.

Appropriateness of the sample size

Typically, studies that examine the impact of independent variable X on the dependent variable Y must hold constant other variables that can also affect Y. Less attention is paid, however, to X itself. In an international context differences in X across countries may have many facets. For example, studying the influence of institutional distance (X) on the MNE’s mode of entry choice (Y) requires unbundling institutional distance into different components, which may warrant a large sample size or more careful sample selection.

Avoiding non-sampling errors

Large multi-country datasets constructed from responses to governmental and private surveys are attractive to IB researchers because these datasets offer the opportunity to test IB research questions on much larger cross-country and cross-cultural samples. These datasets however can be problematic for IB research. First, more often the “breadth” (number of countries and number of constructs) of multi-country/culture surveys far exceeds their “depth” (number of years). Many may be single year surveys, raising reliability issues. Second, multi-country datasets – even when constructed with care – may be prone to non-sampling errors. Low measure reliability, for example, can arise from differences in assessment methods used “on the ground” across countries. Differences in how various cultures understand different constructs (e.g., what “gender equality” means) are also a problem. To this end, Chidlow, Ghauri, Yeniyurt and Cavusgil ( 2015 ) reported that establishment of translation equivalence in cross-cultural studies remains sparse with regards to whether (a) the instrument used to collect the required data is translated appropriately across different cultures and (b) the data collection procedures are comparable across different cultures. A third challenge is that IB researchers may be either unaware (or choose to ignore) changes in methods and sources used by national and international agencies to collect and publish their datasets. Lacking in-depth knowledge of a dataset raises the likelihood of its misuse and misinterpretation of the results.

In sum, non-sampling errors may bedevil IB research simply because IB research questions do not “travel well” cross-nationally and cross-culturally due to multiplicity, multiplexity, and dynamism. One solution to the problem of possible measurement non-equivalence is to test for this issue before using the datasets. Nielsen et al. ( 2020 ) provides examples of statistical methods that can test for measurement equivalence on a cross-national/cultural basis. Aguinis et al. ( 2020 ) provides more generic examples of how data collection and research design challenges may be dealt with; for example, they focus on the potential virtues of Big Data, though such approaches should be used carefully so they do not confound rather than resolve non-sampling errors in IB research.

Phase 3: Data Analysis and Interpretation of Results

IB scholars also face special issues when they are engaged in data analysis and interpretation of results. We briefly discuss three research methodology challenges which can be added to the more general issue of establishing causality (across contexts, levels, and time) raised by Aguinis et al. ( 2020 ).

Addressing anomalies and inconsistencies

Outliers and other anomalies and inconsistencies may be more prevalent in multi-country than in single country studies due to the complexity of IB research. Rare events and asymmetric, long-tailed distributions may be more prevalent in international settings, necessitating research methods designed to handle these anomalies (Andriani & McKelvey, 2007 ). For instance, ignoring the “elephant in the room” (e.g., the dominance of one country such as China or the United States in a multi-country dataset) can lead to erroneous conclusions based on Gaussian averages (e.g., about the average scale and scope of internationalization). Moreover, as datasets span multiple countries and contexts – often relying on combining data sources from different entities and countries – the likelihood of errors due to anomalies and inconsistencies in data collection methods, cleaning, and handling, including translational and equivalence issues, increases. IB researchers must take appropriate steps to correct for such biases, for example, by using investigator triangulation  ex ante  during data collection and  ex post  during analysis and reporting (Nielsen et al., 2020 ).

Choosing the level(s) of theory, data, and analysis

IB studies, as we have stressed above, involve multiplicity and multiplexity. They are typically not only multi-country and multi-context but also multi-level. Employees are nested (and may be cross-nested) within subunits of an MNE (e.g., parent, regional headquarters, plants, branches, subsidiaries); the MNE itself is cross-nested within multiple national and institutional contexts depending on its global footprint. Thus, studying an MNE – let alone a comparison across MNEs – is an exercise in studying and understanding multi-level heterogeneity (individual, plant, firm, industry, country) as well as cross-nested embeddedness at each of these levels (Nielsen & Nielsen, 2010 ). Not surprisingly, determining the “right” level or levels of theory, data, and analysis needed to address a particular research question is not easy. An extension of this research problem arises from ecological fallacies where a construct developed for use at one level of analysis (e.g., country) is used at a different level (e.g., firm), without attention paid to the possible consequences. The “four D’s” may provide useful lenses through which to examine the multiplicity and multiplexity inherent in issues of levels of theory, data, and analysis, that give rise to additional layers of interdependence and nesting.

Avoiding personal bias in interpreting and reporting results

We all “see through our own lenses.” IB researchers, given their interest in the four D’s, are likely to be more contextually aware than domestically focused but are still likely to suffer from personal and institutional biases. Working with diverse teams of scholars from other countries, cultures, and disciplines can help reduce the influence of personal biases. Multi-country/cultural research teams can also provide benefits to IB research by improving the ability of concepts and theories to “travel” across countries, as argued in Peterson ( 2004 ).

CONCLUSION: WORDS TO LIVE BY

We agree with Aguinis et al. ( 2020 ) that IB scholarship suffers from methodological challenges. IB research, by its nature, involves a high degree of complexity generated by the multiplicity, multiplexity, and dynamism of the global economy. IB scholars can use the four D’s (difference, distance, diversity, and disparity) as useful lenses for understanding and analyzing this complexity. Complexity, of course, is one of the reasons that so many scholars study IB research questions but it also brings a set of methodological challenges unique to IB research.

We end with four pieces of advice that we hope provide useful guidance for IB researchers. We note that these guiding principles are complementary to the solutions proposed in Aguinis et al. ( 2020 ) and to our methodology recommendations above.

Learn to Live with (and Embrace) Complexity in Research Design

Complexity is a word that strikes fear and dread into the heart of most researchers; the more complex the problem, the more difficult the research tasks that lie ahead. We argue that IB researchers must learn to live with (and embrace) complexity. They must be comfortable with the multiplicity, multiplexity, and dynamism that characterize the global economy. Deconstructing a research question to examine its complexity through the lens of one or more of the four D’s (difference, distance, diversity, and disparity) is, we argue, critically important for developing interesting, useful, and impactful research. Using these lenses can help the IB researcher understand how multiple parameters affect his or her variable(s) of interest, often in non-linear and interdependent ways. As a result, relying on secondary data sources and conventional research methods such as OLS regression are likely to be insufficient or inappropriate to understand the complexity of IB research. Rather, embracing complexity naturally leads to more experimental research designs, as well as mixed methods, and/or multilevel analyses. Research designs that explicitly acknowledge complexity are likely to better answer the “big” questions that IB faces now and in the future.

Use Triangulation Actively to Increase Rigor and Relevance

Looking at a phenomenon or issue from multiple angles – not the least methodological – can address the biases, errors, and limitations introduced by any single approach (Denzin, 1978 ; Jick, 1979 ). Most of the IB-specific challenges we have raised above can be directly addressed by incorporating various types of triangulation strategies into the research design. For instance, theoretical triangulation may lead to new research questions by juxtaposing different theoretical perspectives. Similarly, data source and data collection triangulation may be seen as “an opportunity to go deeper, rather than as a limitation” (Aguinis et al., 2020 ) while also increasing sample reliability and reducing non-equivalence biases. Analytical triangulation helps ensure validity and reliability of results by comparing and contrasting results using multiple analytical techniques. Investigator triangulation may reduce personal biases in both data collection, analysis, and interpretation processes (Nielsen et al., 2020 ). Indeed, we would argue that the four D’s (difference, distance, diversity, and disparity) may best be attended to by carefully building triangulation into the research design process.

Exercise Due Diligence and Good Judgment

IB researchers should spend time, up front, understanding their research question and their unit of analysis, mapping and graphing the hypothesized relationships among their variables, and taking account of previously theorized relationships. Investment in building a thorough understanding of the research problem will help point the way to the most appropriate research method(s) and technique(s) for tackling the problem. Rules of thumb as to what constitutes an “acceptable” methodological approach are a poor substitute for the due diligence necessary to enable the researcher to exercise his or her good scholarly judgment. This piece of advice also requires IB researchers to have a good command of the available different research methods, of where they work well and where they do not.

Engage in Ethical and Responsible Research Practices

There have been many articles on best practices in responsible research, including several by Herman Aguinis that are particularly appropriate for IB researchers (Aguinis et al., 2017 , 2018 , 2019 ; Bergh et al., 2017 ). In addition, Anne Tsui and colleagues have been actively encouraging business and management scholars to join RRBM (Responsible Research in Business and Management; https://www.rrbm.network ) and adopt RRBM best practices for their research. The editors of JIBS have also led the way for many years in articulating best ethical and responsible practices for IB research, e.g., through the AIB Journals Code of Ethics, JIBS editorials at https://www.palgrave.com/gp/journal/41267/volumes-issues/editorials , and the new JIBS Special Collections books, in particular, Research Methods in International Business (Eden et al., 2020 ). We conclude that “ethical” and “responsible” are good words to live by. Words that when practiced by the global community of IB scholars will build knowledge for a more prosperous, just, and sustainable world.

ACKNOWLEDGEMENTS

We thank Alain Verbeke and Stewart Miller for helpful comments on earlier drafts of this paper.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Accepted by Alain Verbeke, Editor-in-Chief, 4 September 2020. This article was single-blind reviewed.

Contributor Information

Lorraine Eden, Email: ude.umat@nedel .

Bo Bernhard Nielsen, Email: [email protected] .

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Handbook of Qualitative Research Methods for International Business

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• Volume 36 , pages 589–590, ( 2005 )

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ORIGINAL RESEARCH article

Data science's cultural construction: qualitative ideas for quantitative work.

• Department of Sociology, Sciences Po/CSO, Paris, France

Introduction: “Data scientists” quickly became ubiquitous, often infamously so, but they have struggled with the ambiguity of their novel role. This article studies data science's collective definition on Twitter.

Methods: The analysis responds to the challenges of studying an emergent case with unclear boundaries and substance through a cultural perspective and complementary datasets ranging from 1,025 to 752,815 tweets. It brings together relations between accounts that tweeted about data science, the hashtags they used, indicating purposes, and the topics they discussed.

Results: The first results reproduce familiar commercial and technical motives. Additional results reveal concerns with new practical and ethical standards as a distinctive motive for constructing data science.

Discussion: The article provides a sensibility for local meaning in usually abstract datasets and a heuristic for navigating increasingly abundant datasets toward surprising insights. For data scientists, it offers a guide for positioning themselves vis-à-vis others to navigate their professional future.

1 Introduction

Digital transformation has impacted many areas of social life, including politics ( Schradie, 2019 ; Bail, 2021 ), news ( Christin, 2020 ), and the economy ( Zuboff, 2019 ), particularly through social media. The impacts differ, ranging from efficiency gains to polarization and misinformation, but they have in common the entanglement of the novel “data scientists” profession in these changes. This new role has remained obscure despite its salience and older foundations ( González-Bailón, 2017 ). While the ambiguity has likely had benefits for data science ( Dorschel and Brandt, 2021 ), data scientists have struggled with the lack of clarity ( Avnoon, 2021 ). This article asks how the emerging data scientist community has defined their novel role on social media and addresses methodological issues that come with studying an emergent case.

The problem is complicated as strategies of established professions are not immediately available to an emerging profession. Evidence shows how existing professions respond to the ongoing changes in organizational settings (see, e.g., Greenwood et al., 2002 ; Armour and Sako, 2020 ; Goto, 2021 ), but traces of data science's self-definition first appeared on the Internet in blog posts, or on Twitter. A now-classic tweet serves as an example and a working definition: “Data scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” 1 The definition presents data science as an expert role and, read verbatim, gives a sense of the quantitative and coding skills this work entails, but it does not try to be comprehensive or entirely clear and demands that any systematic analysis reconciles local specificity and the phenomenon's global salience.

The immediate questions of how much software engineering a statistician has to know or which parts have been answered by various training programs and textbooks ( Schutt and O'Neil, 2013 ; Salganik, 2018 ; Saner, 2019 ; Dorschel and Brandt, 2021 ). A more puzzling question remains in the definition's imitation of a dictionary definition on social media, where that formalism was unnecessary and long before one existed in print. The style instead leveraged the lay view of expert work as jurisdictions of formal professions ( Freidson, 2001 ). It connects the problem of data science's construction to discussions in the literature on expert knowledge and work. This literature has long developed a nuanced understanding of professions as a system of competitors ( Abbott, 1988 ), emergent relational arrangements ( Eyal, 2013 ), and their organizational dimensions ( Muzio and Kirkpatrick, 2011 ). In contrast, the definition's playfully premature formalism highlights cultural processes underpinning emergent professions.

Culture has an everyday meaning and a technical meaning. Data scientists have recognized the role of culture in the everyday sense, at least sporadically and casually, in terms of “two cultures” in quantitative thinking ( Breiman, 2001 ) or the “culture of big data” ( Barlow, 2013 ). They mean characteristics of their work that do not follow purely technical or formal steps. Sociological theories of expert work acknowledge cultural processes in a more technical sense but often assign them less weight compared to other mechanisms, competition, informal relations, and organizational dynamics. Culture featured in Abbott's (1988) classic account in the background of the main argument as the “diagnosis, treatment, and inference” that jointly form the “cultural machinery of jurisdiction” ( Abbott, 1988 , p. 60). Culture also played an external role such as when public opinion creates problem areas that professions can claim as their jurisdictions ( Abbott, 1988 , ch.7). Fourcade's (2009) comprehensive analysis of economists and their history worked out this side in the interplay of economic culture and institutions, indicating that contexts shape economic theories, which, in turn, shape their environments.

Capturing meaning-making presents a unique challenge in an emergent setting where technological and economic forces converge with the ideas of professional pioneers. Cultural processes have shaped quantitative expertise for a long time ( Porter, 1986 , 1995 ; Desrosières, 1998 ), and data scientists have made a new iteration visible through their appearances in public discourse and popular culture. 2 Several studies have demonstrated the complexity of this outside relationship between experts, and their publics (e.g., Wynne, 1992 ; Epstein, 1996 ), which may in part stem from mismatching views as outsiders have low regard for the technically advanced knowledge that experts value ( Abbott, 1981 ). This article addresses its motivating question of how the data scientist community has defined their role from a cultural perspective that builds on Burke's (1945) notion of A Grammar of Motives . This modern interpretation, which John Mohr introduced as “computational hermeneutics” ( Mohr et al., 2013 ), extends research on expert work into the digital age and gives the intuition data scientists have had since their beginning a rigorous foundation.

The analysis integrates recent arguments for understanding culture in professions into novel computational procedures for formal measures of culture. Spillman and Brophy (2018 , p. 156) stressed the “implicit and explicit claims about the practical or craft knowledge” in addition to the common focus on abstract or technical expertise. Whereas, they illustrated their argument with reference to documentary and ethnographic analyses, this study moves to the digital context, where data scientists often discussed their role. It uses a large dataset of tweets to capture public discussions and draws on advances among scholars of culture around using computational social science techniques (see Edelmann et al., 2020 ). The focus in qualitative research on “vocabularies of motive about work” ( Spillman and Brophy, 2018 , p. 159) links to methodological ideas for recovering cultural features from large numbers of textual documents to reconstruct the meaning that actors assign to situations ( Mohr et al., 2013 , 2015 ).

This conceptual approach guides a computational analysis of data science's cultural construction. The combination informs an analytical strategy for studying expert work, meaning construction, and disputes on social media where they unfold in public. It is able to track meaning-making on different levels to capture data science's local definition and global salience. The results reveal data science within the larger changes of the digital era as a rhetorical strategy for circumventing established groups, their leaders, and legacies to adapt old skills to contemporary issues (see Frickel and Gross, 2005 ; Suddaby and Greenwood, 2005 ). They show an arrangement of actors and themes that suggests new ethical and technical ideas and practical challenges around implementing them as a previously unreported motive of data science's construction. To develop this argument, the article first introduces the data science case, the reflexive analytical approach, and the empirical strategy before summarizing and discussing the observations.

2 Data science as an emergent profession

Data scientists have told origin stories that centered on Facebook and LinkedIn in their early startup days, struggling to get users to connect and navigate the then-new world of social media ( Hammerbacher, 2009 ; Davenport and Patil, 2012 ), but the data science label first appeared in academic circles during the 1990s and early 2000s (e.g., Hayashi, 1998 ; Cleveland, 2001 ), and many underlying ideas are much older ( Donoho, 2015 ; González-Bailón, 2017 ). Data scientists recognize their ties to established quantitative expertise and present their integration of it with computer sciences as a distinguishing feature (e.g., Schutt and O'Neil, 2013 ).

Such origin stories and programmatic definitions do not necessarily spread along direct and linear paths. Historical research of quantitative work and thinking has shown how quantitative experts shared technical ideas about their work in ways that indicate cultural processes ( Porter, 1995 ), such as through “evidential cultures” of data analysis ( Collins, 1998 ). Following the practical work in social media startups during the mid to late 2000s, data science has spread into various industries and public services, all the way to the Obama administration ( Hammerbacher, 2009 ; Davenport and Patil, 2012 ; Lohr, 2015 ; Smith, 2015 ). Its appearance and diffusion indicate another iteration in the long and storied history of quantitative expertise as it extends into the digital age.

Sociological accounts of data scientists have studied data science from different perspectives, beginning with their emergence ( Brandt, 2016 ). Some research shows that data scientists struggle with integrating the multiple competencies and areas of expertise of their roles in their workplaces ( Avnoon, 2021 ). Other research suggests that precisely the ambiguities that undergird the data science role, at least on the level of the larger educational and economic fields, have advanced data science's professional recognition ( Börner et al., 2018 ; Dorschel and Brandt, 2021 ). Journalistic accounts of data science described socio-technical arrangements (e.g., Lohr, 2015 ), where the sociology of expertise would partly locate data science's roots ( Eyal, 2013 ). Social scientists have even reflected on their own relationship with data science, both conceptually, in STS ( Ribes, 2019 ), and practically, in quantitative research ( González-Bailón, 2017 ; Salganik, 2018 ), and stressed the threats to society ( O'Neil, 2016 ; Eubanks, 2018 ). These critical perspectives have initiated concerns with ethics among data scientists ( Loukides et al., 2018 ), another familiar step in the development of professions ( Abbott, 1983 ). The question of how data scientists resolve the ambiguity of their new role as a group a cultural process has remained unexplored.

3 Empirical strategy

3.1 a reflexive perspective.

The early discussions of data science on social media offer a promising opportunity for shedding further light on this new case, but an analysis of data science's cultural construction on social media faces challenges as some who contribute to it may not self-identify as data scientists, and new ideas may not immediately appear relevant. For example, some social scientists helped define data science without affiliating with the new group (e.g., González-Bailón, 2017 ; Salganik, 2018 ). This problem raises questions about the analyst's perspective, which anthropologists and sociologists discuss as reflexivity ( Gouldner, 1970 ; Geertz, 1988 ). Reflexivity has gained new attention and motivated the idea of “asymmetric comparisons,” wherein an analysis captures “the larger diversity in the world” ( Krause, 2021 , p. 9). These comparisons address the problems with an analysis of data science on social media by suggesting comparisons between narrower views of data science to broader observations that are missing initially.

Quantitative research often aims for representative samples and conceives of foregone observations as a problem of missing data that introduces biases. It has addressed that issue systematically for a long time (e.g., Kim and Curry, 1977 ; Little and Rubin, 2019 ). Assuming that all relevant variables are available, which quantitative methodologists acknowledge is not always the case, the main distinction is between missing information on single items for respondents and entire units that did not respond ( Loosveldt and Billiet, 2002 ; Peytchev, 2013 ). The debate further discusses missing data in specific areas of research, such as social networks, which raise questions about the completeness of the units used for studying them (e.g., Kossinets, 2006 ).

Both perspectives can help shed light on data science's formation. For an asymmetric data science comparison that the qualitative perspective counsels, the quantitative perspective would mean adding information on a set of data scientists for which some information may be missing. Such a case should consist of a larger network boundary to reveal the implication of the initial boundary decision. Finally, it seems unlikely that research subjects routinely discuss relevant social dynamics directly ( Jerolmack and Khan, 2014 ), especially as they still define their identity, such as data scientists. The boundary ( Laumann et al., 1983 ) needs to capture more and less overtly related types of content. This complication captures a specific challenge in the larger program of bringing qualitative ideas to quantitative research (e.g., Mützel, 2015 ; Evans and Foster, 2019 ; Brandt, 2023 ).

3.2 Observations and operationalization

This cultural analysis of data science's emergence on social media is part of a larger project that began with field observations of the early data science community in New York City between 2012 and 2015. Those observations covered public events where data scientists presented their work and views of the field. They captured data scientists from close proximity in an important setting but missed many other settings, as well as data science's ongoing construction after the fieldwork ended. This article analyzes the subsequent discussions of data science issues on Twitter, avoiding some constraints from in-person observations even as new limitations come up, which I discuss below. Twitter was ubiquitous in the community during the field observations, where data scientists often mentioned their Twitter accounts when they introduced themselves to audiences. I started following data scientists whom I encountered and added others that appeared in my timeline and seemed relevant. I avoided a general search to ensure consistency with the field observations that had identified central perspectives in the larger data science discussion.

The analysis follows Mohr et al. (2013) to reveal data science's cultural construction on Twitter as a “grammar of motives” that considers “what was done (act), when or where it was done (scene), who did it (agent), how [they] did it (agency [that is, by what means]), and why (purpose)” ( Burke, 1945 , p. xv). Mohr et al. (2013) proposed formal methods for extracting motives from quantitative data. On Twitter, the data scientists (and other users) are “actors,” and Twitter is the “agency” that allows individuals, organizations, and other groups to register, publish tweets of 280 characters or less, follow other accounts to see their tweets, and react to those tweets via liking them or responding. These activities were the “acts.” Both the acts and Twitter, as infrastructure, remained largely stable throughout this analysis and did, therefore, not contribute to an analysis of data science's ongoing construction. 3

Purposes and scenes are the relevant analytic dimensions in addition to the actors. The analysis identifies purposes through Twitter's hashtag functionality. Twitter allows users to include hashtags (#) followed by 1 grams, such as #ArabSpring, #MeToo, or #datascience. These hashtags highlight causes that a tweet seeks to promote and link to other tweets with the same hashtag. I use weighted log odds ratios to identify dominant purposes. For revealing “scenes,” Mohr et al. (2013) used text analytic methods, which I apply to tweet texts. Table 1 summarizes these connections between concepts, operationalization, and analytic techniques (columns 1, 2, and 7). The respective sections provide details on each technique. Together, they reveal key dimensions of data science's cultural construction on social media.

Table 1 . Sample design and raw data structure for asymmetrical comparison.

3.3 Data structure

Twitter's digital infrastructure offers access to vast observations. Concepts from the sociology of professions and expertise, outlined in the introduction, guided the original collection of relevant tweets, but the digital transformation has made vast observations of social activities easily accessible. To design an asymmetric comparison for a reflexive analysis ( Krause, 2021 ), I used Twitter's API to obtain the publicly available timelines of the accounts that posted the tweets in the initial dataset, the connections between accounts, and accounts missing from the initial dataset. The design responds to methodological concerns with capturing actors and what they have to say.

I introduce an intermediary comparison for better understanding the effect of changing boundary conditions and specifying data science's emergent contours. When developing his hermeneutic perspective, Burke (1945 , p. xix–xx) noted that “an agent might have [their] act modified (hence partly motivated) by friends (co-agents) or enemies (counter-agents).” In this reflexive analysis, my Twitter “friends”—Twitter-speak and Burke's conceptual language overlap for what network analysts call first-degree neighbors—may have captured a more focused discussion. 4 The idea of a counter-agent makes sense for the accounts that my ongoing observations missed in as far as they possibly covered a broader discussion. Social network analysis language refers to these accounts as second-degree neighbors. The subsequent analysis captures the “larger diversity in the world” ( Krause, 2021 ) by comparing (1) the patterns that emerge from the dataset of actively collected tweets to those of digitally obtained full timelines and, within those timelines, (2) patterns in friends tweets to those in strangers tweets, or first and second-degree neighbors.

The initial dataset consisted of the tweets that I collected from my timeline as insightful moments from the project's theoretical perspective, beginning in March 2017. This analysis includes tweets until March 2020, when the coronavirus pandemic took over much of the data science conversation. During this time, I manually collected 1,025 tweets from 395 accounts ( Table 1 , column 3). The next section summarizes their content. These observations missed the vast majority of tweets these users posted and shared. I obtained additional tweets by these users and their relations through the Twitter API ( Table 1 , columns 4–6). The resulting dataset includes 455,344 second-degree Twitter ties and a corpus of 752,815 tweets that explicitly indicated English as their language. 5

3.4 Data science on Twitter

This section summarizes data science-related tweets as a first illustration of how Twitter featured in data science's definition, capturing talk of positions, expertise, promises, and threats. Several tweets in the small dataset discussed jobs, which are critical for claiming an area of work ( Abbott, 1988 ). One tweet from November 2018 mentioned an opening in Facebook's Core Data Science team. Others advertised an opening at Detroit's Innovation Team to data scientists who look in that region, or a vacancy at MindGeek, which that tweet identified as the owner of an adult content website. 6 Many others commented on hiring issues, warning, for example, of a lack of demand or that those hiring data scientists mainly look for versions of themselves. Some were quite reflective, noting, for example, that “In my experience, people who [do] data science well tend to get PhDs, but the PhD itself is negative preparation for the job.” In a topic as straightforward as work, tweets can capture more nuance than the popular celebrations or critiques of their large demand capture.

Data science also involves technical expertise, which seems much harder to fit into tweets. Some tweets have taken a light take on methods, joking, for example, how someone may falsely underestimate their significance for data science or, conversely, that some use the common perception of methods as leading to rigor without understanding them. Others share more profound thoughts. Yann LeCun, a pioneer in artificial intelligence and the first director of NYU's data science institute, used the idea of methods across data work, painting, or musical composition to explain the meaning of deep learning. 7 As for the job tweets, these tweets develop technical data expertise instead of broadcasting simple lists of skills.

Many tweets that mentioned data science did not shed additional light on data science's professional construction. I recorded some of them, such as one in which Kirk Borne, a data science popularizer, announced a webinar and used many hashtags, presumably to increase its visibility. This tweet, and a few like it, entered the observations as a record of promotions that mentioned data science without developing its meaning.

The tweets so far illustrate how the data science community discussed the meaning of jobs or methods and their promise online. Other tweets problematized the question of the community itself. The idea of ethics in data science flared up occasionally, and prominently so in the fall of 2018 when well-known data scientists Hilary Mason and DJ Patil published a book titled Ethics and Data Science together with Mike Loukides ( Loukides et al., 2018 ). Another instance of community formation unfolded as a collective reaction to bullying when several data scientists spoke up against one account formally affiliated with data science for having bullied a member of their community. While these examples capture clear moments of community building, others remain more subtle.

This summary shows that Twitter served, at least in some instances, as a discursive space for defining data science. The subsequent analysis models the community's collective construction of data science on Twitter in terms of its underlying motives and across varying boundary specifications.

4 Analysis and results

The first analytical step considers actors, the Twitter accounts that posted tweets about data science. Burke (1945 , p. xix–xx) suggested that agents “subdivide” into groups. This step first analyzes the group structure of the 395 accounts that constitute the small dataset of qualitative observations with respect to the connections between them as well as connections in the large dataset of 455,344 accounts they followed. The “walktrap” community finding algorithm, a standard function in R's igraph package ( Csardi and Nepusz, 2006 ) that builds on the widely used modularity measure ( Pons and Latapy, 2005 ) with a focus on communication settings ( Smith et al., 2020 ), revealed the relational subdivisions of these actors. It uses random walks to partition a network into groups of nodes with dense connections between each other and sparse connections to other nodes.

I begin with the most comprehensive dataset. The large dataset includes 455,344 accounts, all contacts followed by the 395 accounts from the qualitative observations. I created a bipartite network of these following relations, with the 395 focal accounts on one level and the ones they follow as the second level. I projected this bipartite network on the level of the focal nodes, retaining ties between nodes that follow the same other account, weighted by the number of common accounts, and applied the community finding algorithm. This strategy ensures the interpretability of the structural characteristics in terms of the focal nodes while considering a wider structural context. Substantively, it captures that although two accounts may not follow each other, say, two junior data scientists where one is in a university and another in a startup, they may still follow the same prominent accounts. The weighting accounts for the number of accounts in which the two data scientists may share an interest.

The algorithm identified two main communities and a third, smaller community. This result amid an average out-degree of over one thousand nodes for the focal accounts before the projection indicates a strong interest in other Twitter accounts. The two larger groups consist of 265 and 101 accounts and the smaller one of 26 accounts. The modularity score is 0.08, indicating substantial integration. Only 14% of the node pairs have no accounts in common among those they follow, while 49% share ten or more. Qualitative inspection revealed that the largest one consists of more hands-on accounts, including software coders in applied roles but also academics from different disciplines and a few commentators from media and industry, but these two groups of accounts more distinctively cluster in the second larger group, which includes less of the hands-on accounts, capturing the role of often self-described “thought leaders” in these early data science discussions. This structure offers a plausible image of data science's emergent community structure that includes core contributors and some hangers-on. While it reflects abundant records, it is simple and does not indicate any underlying motives.

The next analytical step changes perspective. It considers the immediate relational structure within the tighter boundary of the small dataset of 395 accounts and the 11,580 ties between them. 8 The community detection produced five groups with a modularity score of 0.15. 9 Figure 1 shows this network on an aggregate level where the node sizes indicate the number of accounts in each group (reported in separate discussions below); the arrows between them bundle individual ties from one group to another. The line thickness of the arrows indicates the followership ties from the sender-group perspective. Each group has at least one connection to each other group, except for the media group, where no account follows any account in the social scientists group. On the aggregate level, the strong connections stand out between what I will be introducing as the hacker group and the visionaries, with 123 and 104 ties in the respective directions. Both groups are large and have intuitive links to data science's emergence, but while their interconnection is strong, they are much weaker than the internal connections, consisting of 1,919 and 4,560 ties, which led to the clusters that I discuss next. This network of only direct following relations recovers existing groups that contributed to early data science conversations on Twitter.

Figure 1 . Network of groups and their aggregate relations.

The first group contains prominent accounts ( Figure 2 ; squares represent second-degree accounts from the data collection perspective, and circles represent first-degree accounts). The 29 accounts in this group have a dense core but otherwise moderate interconnections with a density of 0.14. 10 Several belong to newspapers and magazines, such as Forbes, The Economist, CNN, WIRED, and TechCrunch, an online publisher covering the tech industry. These accounts capture data science's cultural context ( Abbott, 1988 ; Fourcade, 2009 ), signaling the broader interest in data issues during data science's emergence. There are also HarvardBiz and Columbia_Tech, two university-affiliated accounts, and IBM Services from the technology industry, which all represent official and corporate actors. Circular node shapes indicate first-degree accounts, which capture one of Burke's ideas on actors. This group includes only a few direct neighbors, such as CNN, The Economist, and chicagolucius, a personal account of a user who indicates roles as a chief data scientist and data officer with the City of Chicago. 11 The outsized salience of second-degree accounts here increases exposure to their tweets through retweets. This group reflects the institutional attention that data science has attracted and the power of some accounts in broadcasting data science ideas even in the confines of the small dataset.

Figure 2 . Followership relations within the media group (1).

The second group consists of 24 accounts ( Figure 3 ), which capture a different side of the community, and one with more interconnections than the previous group at a density of 0.31. 12 There are few, if any, broadly familiar accounts, which mostly belong to epidemiologists and biostatisticians. We see accounts with Harvard affiliations, but this time, they belong to a data initiative and the public health school. Most of these accounts are, again, second-degree neighbors who have entered the observations via direct connections, which are central in this group. The public prominence of media accounts ensured the diffusion of their tweets in the first group. In contrast, this group's academic culture of communicating knowledge and ideas contributed to their diffusion beyond a tight boundary. As these accounts entered the analysis via data science-related tweets, they reflect the idea that expert work unfolds in problem areas rather than formal groups ( Abbott, 1988 ).

Figure 3 . Followership relations within the biostatistics group (2).

Table 2 presents the structurally most central actors of cluster three, which is too large to show visually (it consists of 115 accounts). This group is quite tightly interconnected, considering its size, with a density of 0.15. The most central first-degree neighbor account belongs to hadleywickham, a former professor of statistics, developer of popular R packages, and now a research scientist at RStudio, a software company with free software options. There is also seanjtaylor, who introduced himself on Twitter as a research scientist at Lyft at the time of this analysis but has used the data scientist label for his roles in the past and has continued commenting on data science issues. Another central account is robinson_es, who introduced herself as a data scientist at Warby Parker and advertised a book on building a data science career in her Twitter bio. The most central second-degree accounts are similar, with JennyBryan as a former professor who is now with RStudio, like Wickham, or skyetetra, who introduced herself as a data scientist and author of a book on data science careers, like robinson_es. While not all are equally technical, they all work with data, both first- and second-degree accounts. We can think of this group as data hackers and potentially the group that fits the opening definition of data science most closely. The dominance of second-degree neighbors in this institutionally undefined group of technical profiles indicates the relational backbone of data science's construction.

Table 2 . Overview over 15 most central accounts in the hacker group (3).

Consider, in contrast, the fourth group, which consists of only 15 accounts and contains some of the social scientists that have shaped data science (see Figure 4 ). The interconnections are strong, like in the other cluster of predominantly academic accounts, and have a density value of 0.39. The most central account among them belongs to Duncan Watts (duncanjwatts), 13 now a professor at The University of Pennsylvania, following several years as a research scientist at Microsoft and as a sociology professor at Columbia University. During my field observations, I heard a story that quantitative analysts at Facebook, where the mythology locates data science's origin in the mid-2000s ( Hammerbacher, 2009 ; Davenport and Patil, 2012 ), consulted Watts for advice on the label. Matt Salganik (msalgnaik), another central node, is a quantitative sociologist at Princeton University who wrote a book about quantitative research in the digital age that addressed both social scientists and data scientists ( Salganik, 2018 ). Laura Nelson (LauraK_Nelson) is a sociologist at the University of British Columbia and promotes principles from qualitative methods for computational research (e.g., Nelson, 2020 ). Not necessarily well-known outside academic circles, all these scholars have apparent connections to data science. Shamus Khan (shamuskhan), on the other hand, does mostly qualitative research, but he has published quantitative studies as well (e.g., Accominotti et al., 2018 ). He appears in this dataset because he still tweeted about a data science opportunity at Columbia University, where he taught at the time. Following a media group, epidemiologists, and the hacker group, this is a social science group. The large share of first-degree neighbors in this group of social scientists amid its small size captures my own position in this analysis and suggests that social scientists are keeping quieter than they could about data science [see Ribes (2019) and Brandt (2022) on this issue].

Figure 4 . Followership relations with the social scientists group (5).

The last group, cluster five, is also the largest (177 accounts) and has some of the nominally most explicit connections to data science. Table 3 once again focuses on the most central accounts out of another quite interconnected cluster, considering its size, with a density value of 0.15. The names may not be immediately familiar, but many of them participate actively in the advancement of digital tools. In contrast to the hacker group, this group often comments on broader issues and developments. hmason is the most central node among the first-degree accounts, consistent with her status as a data scientist, founder of a data startup, and co-author of an early data science definition, 14 as well as a book on data science ethics ( Loukides et al., 2018 ). AndrewYNg is a Stanford professor, co-founder of Coursera, and head of artificial intelligence at Alibaba. Then, there are also wesmckinn and amuellerml, who do quite technical work. There is KirkDBorne, formally the chief data scientist at Booz Allen Hamilton at the time and a data science popularizer, but also mathbabedotorg, who was a math professor before she became a data scientist and eventually an activist and author who points at issues with algorithms ( O'Neil, 2016 ). The second-degree accounts mirror the direct neighbors, as for the hacker group, just trailing them slightly in centrality. Many have similar technical skills as those in group three, and several have PhD-level training, but they also bring weightier institutional affiliations, which makes them possible data science visionaries. The balance between two groups in this more talk- and thought-focused cluster shows the beginnings of data science as a distinct object.

Table 3 . Overview over 15 most central accounts in the visionary group (5).

The network's fragmentation into five groups in the small dataset captures the distributed organization of the data science conversation. It reveals the technical and popular perspectives in data science as well as potential sources for non-technical ideas and my social scientific perspective. The first analysis of the large dataset suggested a simple picture that reproduced the familiar divisions. It captured the larger divide between technical expertise and general issues in which data science flourished but not its micro-level foundation. The second analysis of the small dataset revealed fragmentation of the accounts followership network into groups that are internally plausible and reveal a more complex relational underpinning of data science's construction on social media, which involved some densely connected communities that still tied into neighboring groups. The two analytic lenses complement each other to indicate a fractal structure ( Abbott, 2001 ). This additional complexity shows the counterintuitive implications of accounting for “the larger social world” and its promise for studying an emergent group. The different group compositions have started suggesting different motives for data science's definition. The next two steps study them directly.

4.2 Purposes

This step turns to purposes to move further toward a Burke-informed cultural understanding of data science's construction on social media from Mohr's computational hermeneutics perspective. Twitter users can indicate a tweet's purpose through hashtags, and popular hashtags in a group indicate the group's purposes. This step analyzes the prominence of different hashtags using weighted log odds ratios. Odds ratios in text analyses measure the odds for a word occurring in one corpus compared to another ( Silge and Robinson, 2017 ), such as in speeches by Republicans and Democrats or in tweets in the small and large datasets. The frequency of words in two corpora may vary vastly, and they do so by design in the large dataset of missed tweets and the small dataset of qualitative observations. Log odds ratios correct for these asymmetries, but words that do not occur at all in one corpus remain problematic. The following analysis uses weighted log odds ratios, which account for words that may have occurred by chance ( Monroe et al., 2008 ; Schnoebelen et al., 2020 ). 15

This step starts once again with the most comprehensive dataset. The tweets in the large dataset include 335,337 hashtags (46,971 unique hashtags). Figure 5 shows the 25 hashtags with the highest weighted log odds ratios from the large corpus compared to hashtags from the small tweet dataset. The large one includes tweets that promote technical and commercial concerns through hashtags such as artificialintelligence, neuralnetworks , which operationalize artificial intelligence, and internetofthings , on one side, and startups and innovation , on the other. nyc was promoted as well, reflecting the location of the qualitative observations but also its significance in broader discourse, as were women in tech. The blockchain hashtag captures broader technology purposes among these tweets. These are big issues and a range of different ones. Consistent with some of the existing writing ( O'Neil, 2016 ; Eubanks, 2018 ; Zuboff, 2019 ), data science and related concerns thus emerge as part of a comprehensive effort, or a larger cultural discourse, to promote technology and business, the large corpus shows.

Figure 5 . Weighted log odds ratios of hashtags in small dataset and large datasets.

Similar to the initial community structure, these are reflections of familiar purposes of technology and data science advocates. Their occurrence in the tweets dataset underlines Twitter's utility for studying data science's construction, but the bird's-eye view offers few new insights. Next, I turn to the small dataset.

The small dataset includes 475 hashtags (213 unique hashtags). The list of hashtags with the largest weighted log odds ratios on the side of the small dataset includes several that directly or indirectly promoted data science, such as datascience, data, bigdata, AI, ML , and technology themes, such as python, pydata, rstatsnyc , and rladies . The hashtags rladies and data4good promoted political and moral purposes, similar to some prominent purposes in the large dataset but with different political connotations and more concrete initiatives. Some of the hashtags stand for groups or conferences, such as strataconf and datadive . datadive described events where a group meets to work closely on a dataset, while strataconf referred to a major data conference with expensive tickets. rstatsnyc captured the promotion of a local community and reflected the new hope that New York gained as a tech location vis-à-vis Silicon Valley in the latest technological transformation. The hashtags that capture local or topically specific purposes show the payoff of taking different perspectives and moving to a smaller dataset. Twitter facilitates global discussions, but it also accommodates local ones, and they are potentially crucial for mobilizing support and involvement.

The distinctive hashtags reflect purposes that start revealing data science's roots in a collective project around technical skills and ideas for a professional community. The technical hashtags are not distinctive for data science, however, as critics have often noted. The hashtags that stand for community activities, which are not part of the popular data science discussion, suggest a process wherein diverse technologies gain a joint meaning as data science.

The contrast between the large and small datasets serves as a necessary first step to establish the utility of this approach but may overlook variation from more gradual shifts of perspective. One complementary step compares purposes associated with second-degree accounts to those of the first-degree accounts within the large dataset of missed tweets (see Figure 6 ). Tweets by second-degree accounts included 186,607 hashtags (36,131 unique hashtags), and tweets by direct neighbor accounts included 148,718 hashtags (17,291 unique hashtags). Some outlier hashtags appear on these lists. 16 Purposes are once again more diffuse across second-degree tweets in the large dataset. They include oracle , which is a database firm and synonymous with that firm's technology, and storage, referring to data storage that data scientists have relied on from early on ( Hammerbacher, 2009 ), and voicesinai or learntocode —other technical concerns. Then, there is more on sales and several hashtags that promote different technical conferences in the late 2010s. New York City features again as well.

Figure 6 . Weighted log odds ratios of hashtags in tweets of first- and second-degree accounts in the large dataset.

The first-degree accounts tweeted about a combination of the issues that appeared in the small dataset and the large dataset. Data science again tops the list, with machine learning and artificial intelligence nearby and R not far behind. w ids2018 and 2019 appear on this list, promoting women in data science in general and a conference that Stanford University hosts for this purpose, an initiative that has spread to a large number of institutions. This list still includes more of the commercial concerns that the small dataset missed, such as techstartups and businesscoaching .

The differences between first-degree and second-degree purposes remain smaller than between the small and large datasets to capture a more continuous view of the different levels and contexts of data science's construction on social media. The small dataset systematically reveals locally and topically specific purposes that connect the purposes data science supporters share more generally to the situations of specific supporters or beneficiaries. Overall, the small tweet dataset captured most clearly the promotion of data science issues, even in technical terms, and collective activities that would be part of data science's “cultural machinery” ( Abbott, 1988 , p. 60). Together, the different perspectives captured how new socio-technical arrangements come together in expert work ( Eyal, 2013 ). The purposes across the large tweet dataset spoke to broader tech and business concerns, reflecting the larger cultural shifts of the digital era. These purposes, missing from the small dataset, were more prominent among second-degree accounts than among direct neighbors. Instead of constraining the analysis to a representative picture, the comparisons capture the “larger diversity in the world” ( Krause, 2021 ) at varying depths of data science definitions.

The final analytical step turns to “scenes” to see the contexts wherein the actor groups articulate purposes ( Burke, 1945 , p. 3) as part of their construction of data science on social media. Mohr et al. (2013) used latent Dirichlet allocation (LDA) topic models for recovering scenes from texts, which identify words that co-occur in documents within a larger corpus of documents. Each word may be part of one or more topics, and each document may consist of one or more topics ( Blei et al., 2003 ). Several specialized topic modeling approaches are available for specific research problems. This analysis follows Mohr's approach and uses LDA topic models “to identify the lens through which one can see the data most clearly” more than “to estimate population parameters correctly” ( DiMaggio et al., 2013 , p. 582). In this sense, the following models provide an initial image of data science's cultural construction while tracing its contours from varying perspectives. 17 They treat tweets as documents after removing hashtags, addressed accounts, URLs, stop words and numbers, and use word stemming. 18 Consistent with the earlier steps, I generated separate topic models for the large dataset of missed tweets and the small tweet dataset and, within the large dataset, for the tweets of first- and for those of second-degree accounts. This division into distinct corpora captures the scenes as fresh looks from each of the perspectives, revealing their misses, and gains. Computational limitations demanded taking samples of 35,000 tweets from the large dataset of missed tweets for each of the three analytical steps. 19

The first step starts again with the large tweet dataset of full timelines missing from the small dataset. The analysis revealed 45 topics, of which many have no connection to data science, reflecting that it was not a strategic endeavor and instead part of the much broader conversation on Twitter, but data science-related topics still emerged even in this bird's-eye view. Overall, ten topics were about data science issues, another ten about tech or science issues, and then nine, six, and ten about current issues, mostly politics, miscellaneous topics, and different types of chatter (see also Table 4 ).

Table 4 . Summaries for topic models of large tweet dataset.

The tech and science topics comment on the digital transformation, for example, startup opportunities and the big technology companies, as well as articles and journals that are relevant to these accounts. The topics that capture discussions of generally important issues include topics around Trump and politics, education, the economy, and healthcare, as well as urban and civil rights issues. Then, there is a group of leisure topics, including sports, movies, and music, cultural concerns in the lay sense. Finally, several topics have no specific substantive meaning and instead reflect observations, opinions, pleasantries, and general Twitter chatter.

As Supplementary Table S1 shows, the data topics captured quite a few dimensions of data science, a striking result considering the simple modeling procedure, diverse accounts, and openness of Twitter as a discursive space. More specifically, data topics cover practical issues, such as careers and hiring, but also training and studying. The more technical among them revolve around different data analytic approaches or procedures, ranging from statistics and causal inference to machine learning and artificial intelligence, as well as coding-related issues or data visualizations. Perhaps most interestingly, this analysis revealed a topic that picked up on issues of bias and ethics. These topics cover the dimensions of data science that are familiar from more formal, deliberate, and curated discussions directly from concrete conversations. They still present a mirror image of the familiar themes of data science discussions. This broad view responds more to data science rise than its meaning construction, which the small dataset was designed to capture.

The tweets in the small dataset cover 13 topics or, in Burke's terms, scenes. Table 5 lists these topics as 20 words most closely associated with each of them. The table also lists names that I assigned to topics as summaries. Topics two (2) and 13 may be labeled statistics and machine learning. Topic 2 includes words such as model, logistic, regression , and algorithm , and topic 13 includes machine, learning, code , and python , a popular programming language. Topic 11 is about software issues and their importance for data science, several words suggest. Topic seven (7) seems to discuss data science relative to other roles, and topics nine (9) and ten (10) include career advice and open positions. Topic four (4) describes data science training, which seems essential if topic three (3) is right about the challenges it indicates. The tweets associate successful data science with team efforts, as topic six (6) suggests. Topics five (5) and twelve (12) capture discussions and exchanges at conferences and in digital formats as other scenes.

Table 5 . Thirteen level topic model of small tweet dataset.

These topics reveal a more refined set of scenes that still show analytically important depth and diversity. The scenes are familiar from the popular data science discourse, and they reflect themes from sociological ideas about expert work. Several books describe the technical challenges associated with data science work (e.g., Schutt and O'Neil, 2013 ; Wickham and Grolemund, 2016 ), universities have started to offer data science training ( Börner et al., 2018 ; Saner, 2019 ), data scientists have discussed their roles and careers ( Shan et al., 2015 ), and how to build teams ( Patil, 2011 ). The concern with neighboring roles echoes Abbott's classic idea about conflicts between expert professions ( Abbott, 1988 ). The overlap between existing contributions, topics from the large dataset, and this collection of tweets gives confidence in the utility of a small dataset for analyzing data science's cultural definition on social media. In contrast to the existing contributions, these topics portray scenes of ongoing development requiring concrete engagement rather than definite frames of reference and larger processes.

However, the first topic (1) seems neither intuitive nor familiar. Some words are clear enough: Data scientists often work in companies, for instance, while challenge, win , and happy may also go together, as data analysis competitions are a popular sport and recruitment tool in data science. say, word, hour , and room , in contrast, make less intuitive sense. A topic modeling approach provides the opportunity to deal with such surprising results by returning the documents that included these words (e.g., Karell and Freedman, 2019 ). Some tweets were about an analysis of gender diversity that won a data challenge; others discussed the diversity of data scientists in the room should reflect the outside world. Authors of further tweets wondered what they should say to their audience in a room during the half-hour that they had to speak to them. Topic eight (8) echoes the reflective ideas behind these issues. It consists of words that suggest these users reflect on broader problems, including ethics, discussion, thought, read , and better , but the first topic insists on recognizing the collective challenges around advancing these issues as part of data science, adding substance to the conference-related purposes in the previous analysis.

Like the other topics, the reflective perspective has appeared in the broader discourse ( O'Neil, 2016 ), and some of these tweets may concern proposals for a code of ethics for data science (e.g., Loukides et al., 2018 ). These observations capture the collective discussion of these topics and the original implications for active data scientists. Again, however, the general ethic topic manifests itself in discussions of practical questions about implementing it in the community. The initial ambiguity about the words in topic one captures the close connection between these generally familiar ideas and the real experience of constructing a novel professional role.

The final comparison reiterates the analytical strategy of comparing a wider perspective to a narrower one without the radical difference between the full large dataset and the small dataset. It compares topic models of corpora from subsets within the large dataset of missed tweets of tweets of first- and second-degree accounts, which remain closer to the project's theoretical focus. 20 Similar to the full large dataset, these models revealed 45 and 40 topics, which I once again report in thematic groups. Table 4 presents the summaries (together with the full dataset as an additional reference); Supplementary Tables S2 , S3 show all topics in terms of their top 15 words.

Like the initial model for the large tweets dataset, these models reveal familiar scenes and additional ones that the small dataset missed and a more refined set of these topics from tweets by first-degree accounts than in the second-degree tweets. The different groups map onto those from the initial description, with some details that I discuss below. More interestingly, the shifting perspective shows, again, benefits for locating data science's construction in its larger social context. The slightly broader perspective focusing on second-degree tweets has much fewer topics focused on data issues and, to a lesser degree, on tech and science, and more on current issues and especially general social media chatter. While they do not have an evident connection to data science's construction, they serve as an important indicator of where that construction happened, namely, among general concerns and not only the specialized scientific concerns that were more salient in the network analysis.

The dataset of missed tweets by first-degree accounts already reveals a more refined set of data-related topics as well as reflexive discussions. It includes an ethics topic, reflecting this issue's prominence in data science discussions and the well-documented strategy for gaining legitimacy ( Abbott, 1983 ). Here, ethics appear in the context of algorithmic bias, which is part of the larger conversation. In the small dataset, in contrast, the diversity concerns appeared as well around the problem of discussing it in the data science community and its audience and self-reflection on recognizing the purpose of the data science role. Both ethics scenes, in the large and small datasets, are about non-technical questions about what is right, but they differ on how this concern presents itself to those who confront the scene.

The asymmetric comparison shows the limits of the each dataset for capturing meaning construction. Shifting perspectives to narrower dataset designs reveals locally meaningful scenes of concrete engagement with the collective construction of data science as a social object. This pragmatic reflexivity from the small dataset remained largely absent from the larger datasets. The analytic strategy then indicates the utility of considering different levels of data science's cultural construction instead of settling on one definite level for studying an emergent process, especially one that seeks the largest possible view. It also points to technical directions for implementing a more refined text analysis that considers immediate word contexts on the large dataset that tests ideas following from the small dataset.

5 Discussion

This analysis departed from a limited perspective to gain analytical traction on data science discussions on social media from a cultural perspective, an emergent process that poses unique research design challenges that today's digital affordances can help address. Initial examples of tweets illustrated reflections of an emerging profession around technical knowledge, training, and jobs, as well as the wider digital change. The results of network and text analyses found patterns consistent with existing research on data science, as well as ideas in the literature on expert work and quantification. They extend recent arguments that data science's emergence follows from an ambiguous image in its outside construction in firms and sciences ( Dorschel and Brandt, 2021 ) and the struggle of individual data scientists with that ambiguity ( Zuboff, 2019 ; Avnoon, 2021 ). This analysis captured how the data science community sorted out that ambiguity on social media. The qualitative research on which this study built identified meaning-making around concrete analytical and relational issues. This computational ethnography showed that data science pioneers reflected on these challenges between each other and how they arrived at the specific issues in more general discussions.

The analysis addressed the research design challenge of studying emergent processes by adopting an “active approach to data” ( Leifer, 1992 ). It integrated ideas from qualitative and quantitative research about missing observations to guide an analysis of two complementary datasets in an asymmetric comparison ( Krause, 2021 ). This comparison captured the interplay of how actors integrate broader cultural shifts and their more technical ideas into a novel professional identity. Instead of resorting to a single scope or boundary, this article makes an argument for using computational tools to gain analytic leverage from the variation across different boundary specifications. For quantitative analysts, this approach means that rather than departing from the idea of a general analysis, which has merit in many situations but works less well for capturing localized meaning-making processes (e.g., Nelson, 2021 ), they can approach a research problem in relation to their point of departure and comparing different angles on a specific case or process. This approach offers one solution to the increasingly important question of the relevant scope of quantitative analyses ( Lazer et al., 2021 ).

These conclusions are subject to limitations. Subsequent research has to establish connections between the scenes and purposes and the actors for better understanding data science's development. This article's focus on the emergent moment and the methodological challenges that come with it benefited from relying on basic network and text analytic procedures. They can serve as points of departure for analyses that discover more nuanced social and meaning structures. More advanced social network analysis techniques can untangle the precise attachment processes between accounts, such as between the groups this initial analysis reveals. Similarly, more advanced text analytic techniques can identify more nuanced topics and meaning changes of words, such as around the technical and non-technical issues this analysis revealed. More broadly, additional studies of data science have to step outside the Twitter setting to consider agency and acts, but these findings also invite research on further professional or otherwise collective activities on Twitter and how they use social media to discuss with each other in public.

Keeping those limitations in mind, these insights into the collective definition of a professional role complement existing views on professions of expert workers defending their boundaries against competitors ( Abbott, 1988 ), establishing themselves in modern corporations ( Muzio et al., 2011 ), or navigating more extensive socio-technical arrangements ( Eyal, 2013 ). The analysis revealed actors outside of broad commercial and narrow technical concerns, a potential source of new views, and a distinct motivation behind starting data science: building a platform to adopt new practical and ethical standards. While familiar from other scientific and intellectual movements (see Frickel and Gross, 2005 ), this motive appears here for the first time for data science. Compared to other professions that acknowledge non-technical aspects of their work (e.g., MacKenzie and Millo, 2003 ), data scientists discuss these concerns as a community, integrating them into their stock of knowledge.

Practicing data scientists can use this glimpse into their early days as a reference point for assessing their current situation and future direction as a profession. The digital era renders the institutional scaffolding of classic professions less necessary for collective organizing ( Avnoon, 2023 ). This advantage does not relieve professionals from mutual engagement over the content and contours of their work if they seek autonomy from their employers. More immediately, data scientists can also find utility in the culturally informed computational analysis and design around qualitative approaches.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the study involving human data in accordance with the local legislation and institutional requirements. The social media data was accessed and analyzed using the Twitter API in accordance with the platform's terms of use and all relevant institutional/national regulations. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article because only information participants chose to share publicly on Twitter was used for the analysis.

Author contributions

PB: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funded by the European Union (ERC, ReWORCS, #101117844).

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

Views and opinions expressed are those of the author only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fdata.2024.1287442/full#supplementary-material

1. ^ https://twitter.com/josh_wills/status/198093512149958656

2. ^ Newspapers regularly cite data scientists as sources in or protagonists of their stories, and data scientists have featured in popular culture such as in Netflix's House of cards (seasons four and five).

3. ^ Twitter and the interface have gone through substantial change, even before the Elon Musk takeover and its rebranding into X. This analysis focuses on a relatively short window, however, and within that window on a specific corner of the Twitter discussion. The stability assumption is robust within that scope.

4. ^ See Alexander et al. (2012) for this reflexive view on computational hermeneutics.

5. ^ The large dataset missed tweets because Twitter only grants access to a given account's 3,200 most recent tweets. Potentially problematic for some purposes, the over seven hundred thousand available observations offer important context to the small dataset.

6. ^ This tweet was from January 2018, before revelations of MindGeek benefited from videos posted without consent. While such a tweet would indicate ignorance today, at the time it more likely tried to present a progressive twist on possible areas of professional work.

7. ^ I identify individuals by name if they maintained a public profile in the community.

8. ^ While this number looks large, it only represents 8% of all possible ties. In addition, 13 accounts did not follow any accounts and remained outside of the network.

9. ^ There was a sixth group with only three accounts as well as 15 isolates and two isolated pairs that I leave out of this description.

10. ^ Density is a social network analysis measure that indicates the share of all ties in a network out of all possible ties with 1 as the highest score.

11. ^ I refer to the Twitter account names since they serve as the main method for using Twitter and what users have chosen to share as their public profiles.

12. ^ The density measure is sensitive to networks of different sizes in terms of numbers of nodes. In this analysis, the similar density scores between this group and the media group despite their vastly different sizes highlight the great importance of direct following relationships in this group.

13. ^ I report the names together with Twitter usernames for this group because the accounts belong to social scientists and may already be familiar to readers.

14. ^ https://web.archive.org/web/20160220042455/dataists.com/2010/09/a-taxonomy-of-data-science/

15. ^ A related measure with similar qualities is the tf-idf measure. The weighted log-odds-ratios capture better words that are common in different corpora but still more salient in one than another, which is important for this analysis that compares different perspectives.

16. ^ li and rottweiler were outliers in tweets of second-degree accounts. One promotes the account itself and the other the account owner's dog amid other tweets about data science issues, indicating personal promotion efforts. On the friends side, the sexual citizens hashtag does not fit with data science. It refers to a book that had been recently published by Shamus Khan, one of the academic friends accounts, together with Hirsch and Khan (2020) . This hashtag also promotes a personal project, a book, that has a collective orientation at the same time. This difference indicates that the project's interest in data science's collective construction may have led to overlooking actors who pursue more self-serving purposes, supporting the benefits of the asymmetric comparison. As both agendas appear systematically, data science may not have a uniform definition at this early stage.

17. ^ The more specialized implementations can account for meta information on the documents for estimating topic models. At this initial research step focusing on the effect of different perspectives on the emergent image of data science, no specific meta information informed the topic estimation. The discussion will outline how this study's results inform such more refined implementations in future research.

18. ^ I used the topicmodels package ( Grün and Hornik, 2011 ) in R with the Gibbs sampler method and an alpha of .1. I obtained the number of topics after testing a series of possible numbers of topics using the ldatuning package ( Nikita, 2020 ) and considering the four evaluation metrics the packages provides, particularly Griffiths and Steyvers's (2004) . My qualitative reading of the results and familiarity with the case confirmed that this implementation provided satisfactory results for the purposes of observing data science's construction across the different perspectives.

19. ^ This limitation only has small effects on the results. While topic models of more tweets obviously capture more topics (in contrast to other many other corpora, Twitter specializes in no particular set of issues), analyses of different sample sizes and randomly composed corpora have revealed the same set of main topics.

20. ^ Specialized techniques exist [e.g., correlated topic models ( Blei and Lafferty, 2009 )] for modeling these two corpora jointly while considering the two types of accounts. Rather than finding the one precise topic model, however, this analysis aims to compare the “lenses” different points of departure offer.

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Keywords: data science, emergence, expertise, professions, reflexivity, computational social science, social network analysis, computational ethnography

Citation: Brandt P (2024) Data science's cultural construction: qualitative ideas for quantitative work. Front. Big Data 7:1287442. doi: 10.3389/fdata.2024.1287442

Received: 01 September 2023; Accepted: 22 July 2024; Published: 14 August 2024.

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*Correspondence: Philipp Brandt, philipp.brandt@sciencespo.fr

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5 ways to better communicate with international business partners.

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If many of your business partners are located in other countries, communication will prove key in ... [+] keeping your relationships strong, dynamic and long-standing.

Many trade press articles claim that globalization is reversing, citing reasons ranging from increased tensions between the U.S. and China to ongoing logistics disruptions. However, a report from the Harvard Business Review notes that global flows of trade, capital and information have already recovered beyond pre-pandemic levels by 2021, with the recovery of people flow also gaining acceleration through 2022 and 2023.

With this background, more businesses than ever are relying on international partners to achieve their organizational goals. While international partners can bring a broader range of skills and perspectives to your initiatives, communication can sometimes be a challenge. By focusing on simple ways to improve communication with international partners, you can ensure more successful outcomes.

1. Establish Cultural Fit

I’ve written recently about how cultural fit can help improve international relationships , but it bears repeating here. If you want to have quality communication with your international business partners, you must establish cultural fit before you even enter into the partnership.

This can be done by completing a compatibility and trust assessment, where you look at a prospective partner’s approach to areas such as communication preferences, team orientation, innovation mindset and performance trust. You don’t have to be from the same geographic culture to have a compatible business culture.

Ensuring cultural fit at the start of the relationship helps establish clear expectations for both parties, which will go a long way in ensuring effective communication in the future.

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‘twisters’ debuts on digital streaming this week, student loan payment and interest freeze extended for 8 million after latest loan forgiveness setback, 2. use clear, concise language.

Even when you and your international partners are aligned in terms of vision and performance, language barriers can persist. Even someone who speaks your language well might not understand all the subtleties and nuances that a native speaker would.

When meeting with your international colleagues, it can be helpful to keep this in mind by using clear and concise language. You should try to avoid slang and idioms that can be confusing to non-native speakers. There are many idioms across languages that don’t translate easily or directly to other languages. Similarly, avoiding business or industry-specific jargon may be necessary to ensure effective communication.

This doesn’t mean you have to dumb down what you’re saying. But be mindful of how you communicate so that your messages don’t get lost in translation.

3. Speak The Language — Literally

Of course, your ability to communicate effectively with international partners will improve drastically if you can speak their language. Investing in language training for yourself and your staff can increase engagement and grow your skills. Such efforts can also demonstrate to your partner just how much you value your relationship.

New technology is making it easier than ever to speak the language of your partners, even if you don’t have the time to learn it. I recently had the opportunity to speak with Artem Morgunov, co-founder of GalaxyVoice AI , an AI-based tool that helps translate your voice into other languages in real time, while maintaining your true voice.

"AI is now so advanced that we can not only provide instant translations for a variety of languages and accents, but even in your real voice," he explains.

"Thanks to voice-cloning technology and AI, it is now possible to speak in another language more easily than ever before. For example, you can give presentations in perfect Mandarin and still sound like yourself — with all your emotions and intonations. And, of course, without knowing a single word of it. The days of relying on slow and expensive interpreters or monotonous computer translations are over.”

4. Streamline Communications With Appropriate Tech

As exciting as tools like AI voice translation can be, they are far from the only tech tools available to streamline tech. As the COVID pandemic accelerated remote work, it also increased the adoption of a wide range of tools designed to improve communications among geographically diverse teams.

For example, video conferencing enables international partners to hold “face to face” meetings without the need for time-consuming travel. Workplace collaboration tools can also streamline the sharing of files, ideas, questions and more, allowing each team member to contribute from their own workspace (and time zone).

While the right tools can vary between partnerships, you and your partners should always look for solutions that will make communication more convenient for everyone.

5. Improve Your Writing

So much of business communication depends on writing. Yet many businesspeople who can communicate quite effectively when speaking struggle with the written word. A report from Verbal Identity found that while two-thirds of employees at large companies write as part of their job, bad and inefficient writing is estimated to cost businesses $400 billion per year due to time wasted, miscommunications and other issues.

These challenges are further compounded when using writing to communicate with an international business partner. In addition to obtaining language training for you and your team, you should also be mindful of your writing abilities. The ability to write clearly and concisely can make all the difference in achieving your partnership goals, especially when in-person meetings will be less frequent.

Investing in writing training (and using tools to help employees write better) will make your partnerships more effective by preventing misunderstandings and the need for frequent clarification. As you and your team become more efficient writers, other partnerships goals will fall into place.

Make Communication Your Top Priority

Your communication skills can make or break any partnership — but they are especially vital when working with an international partner. As you collaborate to find solutions that will prevent misunderstandings and keep everyone on the same page, you’ll be able to enjoy a more streamlined and efficient work process that delivers your desired outcomes.

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