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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.

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Methodological practices in international business research: An after-action review of challenges and solutions

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• Published: 09 August 2020
• Volume 51 , pages 1593–1608, ( 2020 )

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• Herman Aguinis 1 ,
• Ravi S Ramani 2 &
• Wayne F Cascio 3  

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We combine after-action review and needs-assessment frameworks to describe the four most pervasive contemporary methodological challenges faced by international business (IB) researchers, as identified by authors of Journal of International Business Studies articles: Psychometrically deficient measures (mentioned in 73% of articles), idiosyncratic samples or contexts (mentioned in 62.2% of articles), less-than-ideal research designs (mentioned in 62.2% of articles), and insufficient evidence about causal relations (mentioned in 8.1% of articles). Then, we offer solutions to address these challenges: demonstrating why and how the conceptualization of a construct is accurate given a particular context, specifying whether constructs are reflective or formative, taking advantage of the existence of multiple indicators to measure multi-dimensional constructs, using particular samples and contexts as vehicles for theorizing and further theory development, seeking out particular samples or contexts where hypotheses are more or less likely to be supported empirically, using Big Data techniques to take advantage of untapped sources of information and to re-analyze currently available data, implementing quasi-experiments, and conducting necessary-condition analysis. Our article aims to advance IB theory by tackling the most typical methodological challenges and is intended for researchers, reviewers and editors, research consumers, and instructors who are training the next generation of scholars.

Nous combinons les cadres de l’examen après action et de l’évaluation des besoins pour décrire les quatre défis méthodologiques contemporains les plus répandus auxquels sont confrontés les chercheurs en international business (IB), tels qu’identifiés par les auteurs des articles du Journal of International Business Studies : Mesures psychométriquement déficientes (mentionnées dans 73% des articles), échantillons ou contextes idiosyncrasiques (mentionnés dans 62.2% des articles), plans de recherche non idéaux (mentionnés dans 62.2% des articles) et preuves insuffisantes sur les relations causales (mentionnées dans 8.1% des articles). Nous proposons ensuite des solutions pour relever ces défis : démontrer pourquoi et comment la conceptualisation d’une construction est exacte dans un contexte particulier, préciser si les constructions sont réfléchies ou formatrices, tirer parti de l’existence de multiples indicateurs pour mesurer les constructions multidimensionnelles, utiliser des échantillons et des contextes particuliers comme véhicules pour la formalisation et le développement ultérieur de la théorie, rechercher des échantillons ou des contextes particuliers où les hypothèses sont plus ou moins susceptibles d’être soutenues empiriquement, utiliser les techniques du Big Data pour tirer parti de sources d’information inexploitées et réanalyser les données actuellement disponibles, mettre en œuvre des quasi-expériences et effectuer l’analyse des conditions nécessaires. Notre article vise à faire progresser les théories en IB en abordant les défis méthodologiques les plus typiques et s’adresse aux chercheurs, aux réviseurs et aux éditeurs, aux utilisateurs de recherche et aux instructeurs qui forment la prochaine génération d’universitaires.

Combinamos los marcos de la revisión después la acción y la valoración de necesidades para describir los cuatro retos metodológicos contemporáneos más dominantes enfrentados por los investigadores de negocios internacionales (IB por sus iniciales en inglés), tal como lo identifican los autores de los artículos del Journal of International Business Studies: Medidas psicométricamente deficientes (mencionado en 73% de los artículos), muestras idiosincráticas o contextos (mencionado en 62,2% de los artículos), y evidencia insuficiente de las relaciones causales (mencionado en 8,1% de los artículos). Luego, ofrecemos soluciones para abordar estos retos: demostrando por qué y cómo la conceptualización de un constructo es exacto dado un contexto particular, especificando si los constructos son reflexivos o formativos, aprovechando la existencia de múltiples indicadores para medir los constructos multidimensionales, usando muestras particulares y contextos como vehículos para teorizar y avanzar el desarrollo de teoría, buscando muestras particulares o contextos en donde las hipótesis son más o menos propensas a ser apoyadas empíricamente, usando técnicas de Big Data para aprovechar fuentes de información no explotadas y re-analizar los datos disponibles actualmente, implementando cuasi-experimentos, y llevando a cabo análisis de condición necesaria. Nuestro artículo tiene como objetivo avanzar la teoría de negocios internacionales tratando de resolver los retos metodológicos más típicos y está dirigido a investigadores, evaluadores y editores, consumidores de investigación, e instructores quienes están entrenando la próxima generación de académicos.

Combinamos modelos de revisão pós-ação e de avaliação de necessidades para descrever os quatro desafios metodológicos contemporâneos mais difundidos enfrentados por pesquisadores de negócios internacionais (IB), conforme identificados pelos autores dos artigos do Journal of International Business Studies: Medidas psicometricamente deficientes (mencionadas em 73% dos artigos), amostras ou contextos idiossincráticos (mencionadas em 62,2% dos artigos), designs de pesquisa abaixo do ideal (mencionados em 62,2% dos artigos) e evidências insuficientes sobre relações causais (mencionadas em 8,1% dos artigos). Em seguida, oferecemos soluções para enfrentar esses desafios: demonstrar por que e como a conceitualização de um construto é precisa dado um contexto específico, especificando se os construtos são reflexivos ou formativos, aproveitando a existência de vários indicadores para medir construtos multidimensionais, usando amostras e contextos específicos como veículos para teorizar e desenvolver teorias adicionais, buscando amostras ou contextos específicos em que hipóteses são mais ou menos propensas a serem empiricamente suportadas, usando técnicas de Big Data para tirar proveito de fontes de informação inexploradas e analisar novamente dados atualmente disponíveis, implementando quase experimentos e conduzindo análises de condições necessárias. Nosso artigo tem como objetivo avançar a teoria em IB por enfrentar os desafios metodológicos mais típicos e destina-se a pesquisadores, revisores e editores, consumidores de pesquisa e instrutores que estão treinando a próxima geração de acadêmicos.

我们结合事后回顾和需求评估框架来描述国际商务 (IB) 研究人员面临的四种最普遍的正如《国际商务研究期刊》诸多文章的作者所指出的当代方法论挑战: 心理测量有缺陷的量表(在73%的文章中提及)、特质样本或情境(在62.2%的文章中提及)、欠佳的研究设计(在62.2%的文章中提及)、以及因果关系证据不足(在8.1%的文章中提及)。然后, 我们提供了解决这些挑战的方案: 展示在特定情境下构建的概念化为什么以及如何精确, 指定构建是反射性的或是形成性的, 利用现存的多个指标来衡量多维构建, 使用特定样本和情境作为理论化和进一步理论开发的工具, 寻找假设或多或少被实证支持的特定的样本或情境, 使用大数据技术来利用未开发的信息源并重新分析当前可获数据, 实施准实验并进行必要的条件分析。我们的文章旨在通过应对最典型的方法论挑战来推进IB理论, 针对的是正在培训下一代学者的研究人员、审稿者和编辑、研究的消费者和教员。

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INTRODUCTION

Several recently published reviews and reflections have highlighted the increasing diversity of international business (IB) research in terms of its disciplinary bases, theoretical and conceptual underpinnings, topics, and methodologies (e.g., Aguinis, Cascio & Ramani, 2017 ; Cantwell & Brannen, 2016 ; Griffith, Cavusgil & Xu, 2008 ; Liesch, Hakanson, McGaughey, Middleton & Cretchley, 2011 ; Shenkar, 2004 ; Verbeke & Calma, 2017 ). Also, a recently published edited volume describes best practices in IB research methods (Eden, Nielsen & Verbeke, 2020 ). Our article complements and goes beyond these efforts by identifying contemporary methodological challenges faced by IB researchers, as described by Journal of International Business Studies (JIBS) authors themselves, and offering solutions to each of these challenges. Therefore, our article makes the following contributions. First, our proposed solutions are mostly based on innovations outside of IB (i.e., organizational behavior, human resource management, strategy, psychology, and entrepreneurship). Also, they are readily available and can be implemented without substantial effort or cost. Second, our proposed solutions extend current knowledge by identifying new insights and opportunities. As one example, in their chapters in Eden et al.’s ( 2020 ) edited volume, Doty & Astakhova ( 2020 ) and van Witteloostuijn, Eden & Chang ( 2020 ) referred to challenges posed by the use of psychometrically deficient constructs, and suggested traditional solutions such as the use of multisource data and observable constructs. We extend these suggestions by identifying additional solutions (i.e., using reflective versus formative indicators; using Big Data) that offer new avenues for IB researchers facing this challenge. Third, we provide examples of research that has implemented our proposed solutions to illustrate that our recommendations are realistic and not just wishful thinking. Overall, our goal is to help advance IB theory by addressing the most typical methodological challenges. Our article is intended for researchers with the typical methodological training offered by IB doctoral programs, journal reviewers and editors, research consumers, and instructors who are training the next generation of scholars.

Our approach is based on combining two inter-related frameworks: an after - action review (AAR) and a needs assessment . Popularized by the military, an AAR is a “continuous learning process reflecting the desire to sustain performance or the need to change behavior in order to effect more favorable outcomes” (Salter & Klein, 2007 : 5). Importantly, AARs do not seek to assign blame. Instead, they adopt an objective, non-punitive stance that allows for a reflective examination of past performance, with the goal of improving future outcomes (Morrison & Meliza, 1999 ). We complement our AAR approach with a needs-assessment framework drawn from the training-and-development literature (Aguinis & Kraiger, 2009 ; Noe, 2017 ). In this approach, information about challenges forms the basis of future training and development efforts aimed at addressing gaps between current and desired levels of knowledge, skills, and performance (Cascio & Aguinis, 2019 ; Noe, 2017 ). Based on these two approaches, we conducted a review and content analysis to uncover the most pervasive contemporary methodological challenges, as identified by the authors of all empirical articles published in JIBS from January through December 2018. In addition, to make our article most useful and relevant for IB readers, we illustrate the methodological challenges by referring to variables used in IB research specifically. We then present solutions that can help IB researchers address these challenges.

We pause here to clarify two important issues. First, because we examined published JIBS articles, the methodological challenges we identify represent only those that survived the review process. In other words, each of the articles probably had other methodological challenges, but the authors addressed them as their manuscripts improved during the review process based on feedback provided by the reviewers and the editor. We discuss this issue in more detail in the section titled “Limitations and Suggestions for Future Research.” Second, 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. 1 So, we focus on JIBS as a case study. Also, in implementing our combined AAR-needs-assessment approach, our intention is not to cast blame. Rather, our hope is that, over time, the potential solutions we outline will be used not only in JIBS but also in other IB journals, such as the Journal of World Business , Global Strategy Journal , Journal of International Management , and Management and Organization Review , among others.

Established theories of human performance provide an explanation for and help us understand the existence of the methodological challenges we identified. Specifically, these likely exist due to a combination of three factors: (a) researcher motivation; (b) researcher and reviewer knowledge, skills, and abilities (KSAs); and (c) context (Aguinis, 2019 ; Van Iddekinge, Aguinis, Mackey & DeOrtentiis, 2018 ). Regarding motivation, researchers receive highly valued incentives for publishing in top journals, regardless of an article’s methodological limitations (Aguinis, Cummings, Ramani & Cummings, 2020b ; Rasheed & Priem, 2020 ). Second, regarding KSAs, given the fast pace of methodological developments, many reviewers are unable to update their methodological repertoire and may not be as familiar with the latest methodological innovations (Aguinis, Hill & Bailey,  2020 ). Similarly, researcher KSAs are affected by financial constraints and the decreased number of opportunities for doctoral students and more seasoned researchers alike to receive university-sponsored state-of-the-science methodological training (Aguinis, Ramani & Alabduljader, 2018 ). Third, regarding context, there are established and consensually accepted methodological practices that are passed on from generation to generation of researchers – and these practices are difficult to change, even if novel and better approaches become available (Aguinis, Gottfredson & Joo, 2013 ; Nielsen, Eden & Verbeke, 2020 ). A human-performance theoretical framework that includes motivation, KSAs, and context allows us to understand why these methodological challenges exist and persist and also to offer suggestions for how to move forward in the future.

CONTEMPORARY METHODOLOGICAL CHALLENGES

We reviewed all 43 articles published in JIBS from January through December 2018, excluding editorials, reviews, and conceptual articles (the total number of all types of articles was 66). Following the procedure implemented by Brutus, Aguinis & Wassmer ( 2013 ) in their review of the management literature, we identified self-reported methodological challenges by examining the Limitations, Future Directions, Robustness Checks, and similar sections in each article’s Discussion section. We used dummy coding (1 = yes, 0 = no) to count how many articles mentioned each particular challenge. 2

A legitimate question is whether JIBS authors are sufficiently transparent in acknowledging methodological challenges (Aguinis et al., 2018 ). It was reassuring to find that most JIBS articles (approximately 72%) explicitly acknowledged methodological challenges that potentially influenced substantive results and conclusions. We suspect that many of these challenges are included in published articles as a result of the review process and at the request of reviewers or editors.

Table  1 presents the four most frequently mentioned methodological challenges: (i) psychometrically deficient measures, (ii) idiosyncratic samples or contexts, (iii) less-than-ideal research designs, and (iv) insufficient evidence about causal relations. Because these challenges are so pervasive, this list is likely to be familiar to many IB researchers, journal editors, and reviewers, as well as consumers of research.

Because we do not wish to identify authors, Table  1 shows aggregate results. For the same reason, the description of each of the four most frequently mentioned challenges does not refer to any specific articles. Rather, we provide generalized statements to show how authors typically refer to each of the challenges. 3

The methodological challenge that was most frequent and was mentioned in 73% of the articles was that the measures used were psychometrically deficient . Authors typically reported this challenge using one of two formats:

“Our study used [X] to measure construct [Z]. A challenge of this approach is that [X] does not fully capture the construct [Z].”

“Our study used [X] as a measure of construct [Z]. However, others have noted that data regarding [X] is subject to manipulation or error and therefore may not be sufficiently reliable as a way to measure [Z].”

The methodological challenge that was mentioned second most frequently referred to the use of idiosyncratic samples or contexts (62.2%). Authors referred to this challenge using the following general phrasing:

“Our study examined the relationship between [X] and [Y] in the context of or using a sample drawn from [Z]. A challenge of this approach is that results obtained and conclusions drawn may be altered significantly when examined in a different context (e.g., country, time-period) or when using a different sample (e.g., a more diverse group of firms, publicly listed versus non-public firms).”

The methodological challenge that was mentioned third most frequently was the use of a less - than - ideal research design (62.2%). This challenge referred to variables included, excluded, or not measured, and the manner in which the data were collected. Authors typically reported this challenge using one of these three formats:

“We did not include/exclude/measure/control for [X] due to limitations in the dataset used.”

“We examined the relationship between [X] and [Y] at the level of [Z] (e.g., firm, industry, country), but these relationships might differ based on additional levels of analysis such as [W] (e.g., industry, country, geographic cluster).”

“We captured data regarding the relationship between [X] and [Y] during a particular period, but these relationships might change if we had more longitudinal data.”

The fourth methodological challenge, which was not nearly as frequent as the previous three (mentioned in 8.1% of the articles), involved insufficient evidence about causal relations . Authors generally described this challenge as follows:

“Because we used method [Z] (e.g., archival data, cross-sectional survey) to study the relationship between [X] and [Y], our results and conclusions should not be interpreted as implying a causal link between the constructs.”

Interestingly, there are differences in the results of our analysis based on articles published in JIBS compared to those by Brutus et al. ( 2013 ) based on articles published in AMJ, Administrative Science Quarterly (ASQ), JAP, and JOM. For example, compared to Brutus et al. ( 2013 ; Table 5), issues of psychometrically deficient measures, idiosyncratic samples or contexts, and less-than-ideal research designs are much more prevalent in IB research, while issues of insufficient evidence about causal relations are much less frequent. Specifically, we found that 73% of JIBS articles reported issues related to psychometrically deficient measures. In contrast, the average across the four journals examined by Brutus et al. ( 2013 ) was 25%. Similarly, idiosyncratic samples or contexts are reported in 62.2% of JIBS articles versus 32%, as reported by Brutus et al. ( 2013 ). The values for less-than-ideal research designs are 62.2% for JIBS versus an average of 6% across AMJ, ASQ, JAP, and JOM. However, JIBS articles are much less likely (8%) to note issues of insufficient evidence about causal relations compared to the four journals examined by Brutus et al. (34%).

Based on the aforementioned results and comparisons, while some broad dimensions of methodological limitations seem to apply across fields, IB researchers face a unique combination of methodological challenges, and those are in many ways distinct from the concerns expressed by authors of articles in management journals. These differences are likely due to the fact that, as noted by Eden et al. ( 2020 : 11), “IB refers to a complex set of phenomena, which require attention to both similarities and differences between domestic and foreign operations at multiple levels of analysis.” Thus, methodological challenges in IB result, at least in part, from substantive questions about processes that are locally embedded, relationally enacted, and iteratively unfolding (Poulis & Poulis, 2018 ) – what Norder, Sullivan, Emich & Sawhney ( 2020 ) recently called “local-seeking global.”

PROPOSED SOLUTIONS

As a preview of the material that follows, Table  2 provides a summary of our proposed solutions for each of the methodological challenges we described in the previous section, together with sources that describe each of the solutions in more detail. Furthermore, we include both ex-ante solutions (e.g., regarding measures and research design), as well as ex-post solutions (e.g., using Big Data and necessary-conditions analysis). We do so because while ex-ante solutions are ideal (Aguinis & Vandenberg, 2014 ), they may not always be feasible. In such cases, ex-post solutions can be a satisfactory alternative.

Solutions for Challenge #1: Psychometrically Deficient Measures

A measure is considered psychometrically deficient if it fails to represent the desired construct in a comprehensive manner (Aguinis, Henle & Ostroff, 2001 ). Using such measures provides an incomplete (i.e., deficient) understanding of the construct. Consequently, estimates of relations between the focal and other constructs are usually underestimated (i.e., observed effect-size estimates are smaller than their true values). For example, measures of patent counts and patent citations are common in IB research. In our review, we found that these measures have been used in JIBS articles as proxies for constructs as diverse as knowledge sourcing and innovation performance. Furthermore, the articles using these measures mentioned that an important methodological challenge was the inability of patent-based measures to represent the intended construct comprehensively. A similar challenge involving psychometrically deficient measures applies to data used to assess other key IB constructs such as research and development (R&D) investment and R&D intensity.

Recent research in IB has identified some potential solutions. These include specifying decision rules used to select variables (Aguinis et al., 2017 ); calls for IB researchers to work together to identify clear standards (Delios, 2020 ; Peterson & Muratova, 2020 ); and an illustration and recommendation regarding the specific construct of cultural distance (Beugelsdijk, Ambos & Nell, 2018 ). We offer three additional solutions to address the challenge of psychometrically deficient measures.

First, consider that an ounce of prevention is worth a pound of cure (cf. Aguinis & Vandenberg, 2014 ). In other words, the first solution is for researchers to evaluate whether the measure they are considering might be psychometrically deficient before they collect and analyze data. Future IB research can begin by examining the literature to determine if the measure has been employed previously to represent more than one construct. If so, it is necessary to provide one or more theoretical arguments to explain why it is appropriate to use the measure to assess the focal construct. It is also necessary to demonstrate why and how the conceptualization of the construct is accurate, given the context of the study (Ketchen, Ireland & Baker, 2013 ). An example of a study that adopted such a solution is Banerjee, Venaik & Brewer’s ( 2019 ) article on understanding corporate political activity (CPA) using the integration-responsiveness framework. The authors briefly reviewed past approaches to studying CPA, and then outlined how their conceptualization aligned with the focus of their study.

Second, most constructs in IB research are multidimensional in nature (e.g., organizational innovation). So, another solution for the psychometric-deficiency challenge is to follow the three-step process outlined by Edwards ( 2001 ) as follows. The first step is to specify whether the construct is reflective (i.e., measures are indicators of a superordinate construct) or formative (i.e., measures are aggregated to form the construct). For example, consider the construct of organizational innovation, which has been examined in several JIBS articles using measures such as product innovation, total number of patent applications, number of patents per employee, process innovation, and administrative innovation, among others. A reflective conceptualization implies that there exists an unobserved latent variable (i.e., organizational innovation), and that these measures are observable embodiments of this latent variable (Edwards, 2011 ). In contrast, a formative conceptualization implies that these measures are building blocks of an underlying latent variable (i.e., organizational innovation), which is defined by some combination of these measures (Edwards, 2011 ).

The second step is to identify different dimensions of the construct and the implications of conceptualizing it in this manner. Because each measure in a reflective conceptualization captures all relevant information about the construct, different measures may be omitted, as they provide interchangeable information (Edwards, 2001 , 2011 ). Therefore, researchers may use just one measure to fully describe organizational innovation. However, in a formative conceptualization, each measure captures a different aspect of the construct, and omitting a measure detracts from the overall understanding of the construct (Edwards, 2001 , 2011 ). Therefore, researchers should use multiple measures and combine the scores to arrive at a composite value of organizational innovation.

The third and final step involves using analytical techniques based on the conceptualization of the construct as reflective or formative (Edwards, 2001 ). For example, while reflective measures are expected to have high inter-correlations, formative measures do not need to demonstrate high internal consistency (Edwards, 2011 ). Researchers can take similar steps regarding issues such as measurement equivalence, measurement error, model identification, and model fit (Diamantopoulos & Papadopoulos, 2010 ; Edwards, 2001 , 2011 ; Vandenberg & Lance, 2000 ). Besides organizational innovation, some examples of IB constructs that may be conceptualized as reflective or formative include international business pressures (Coltman, Devinney, Midgley & Venaik, 2008 ), export coordination and export performance (Diamantopoulos, 1999 ; Diamantopoulos & Siguaw, 2006 ), and exploration and exploitation (Nielsen & Gudergan, 2012 ). Overall, following these three steps will provide IB researchers with evidence on whether psychometric deficiency is a concern, regardless of the manner in which the construct is conceptualized.

Third, yet another solution when facing challenges regarding the choice and operationalization of measures is to use multiple rather than single indicators. This is possible even when a particular database includes a single-item measure. Specifically, it is possible to gain a more comprehensive understanding of the construct in question by using two or more measures that provide alternative information. In the case of organizational innovation, researchers may utilize multiple measures such as, for example, the number of new products introduced, degree of “newness” of the products, degree of technological advancement of products versus competitors' offerings, and process improvements to capture information about different facets of the construct. Another approach is to cross-reference measures of the construct across different databases (Boyd, Gove & Hitt, 2005 ; Cascio, 2012 ). For instance, returning to the example of patent-based measures, rather than relying only on patent counts or patent citations, future research can use both when examining constructs related to innovation, knowledge, or technology (Ketchen et al., 2013 ).

Solutions for Challenge #2: Idiosyncratic Samples or Contexts

The highly diverse nature of IB research and its local-seeking global theories (Norder et al., 2020 ) makes the challenge of using idiosyncratic samples or contexts particularly salient. For example, Teagarden, Von Glinow, and Mellahi ( 2018 ) noted that much of IB research is about contextualizing business. Our results showed that challenges mentioned in JIBS articles include testing theories (i) in a single country with a particular form of governance, (ii) during a particular time-period, (iii) using relations between two specific countries, and (iv) examining a particular product category in a particular market in a particular group of countries.

For researchers wrestling with methodological challenges stemming from the specific sample or context in which they test their theories, we offer two solutions. The first is to re-conceptualize this methodological challenge. Specifically, rather than treating it as a methodological limitation, a particular sample or context can be used as a vehicle for theorizing and further theoretical development. For example, using a specifically targeted sample or context can help expand the understanding of the focal theory’s boundary conditions, provide support for a theory or part of a theory that was previously lacking, or improve our understanding of practical implications of interventions based on the theory’s propositions (Aguinis, Villamor, Lazzarini, Vassolo, Amorós, & Allen, 2020c ; Bamberger & Pratt, 2010 ; Makadok, Burton & Barney, 2018 ). Adopting this solution will also help future IB researchers answer numerous calls (e.g., Antonakis, 2017 ; Leavitt, Mitchell & Peterson, 2010 ) for greater clarity and precision in theories and theorizing.

The second solution is to embrace the uniqueness of the challenge posed by sample or context specificity. That is, future IB research can specifically seek out samples or contexts where hypotheses are more likely or less likely to be supported empirically, thereby putting focal theories at risk of falsification (Bamberger & Pratt, 2010 ; Leavitt et al., 2010 ). Adopting this solution is akin to the “case-study” approach (e.g., Eisenhardt, Graebner & Sonenshein, 2016 ; Gibbert & Ruigrok, 2010 ; Tsang, 2014 ). Future IB research can also use unique samples and contexts as individual settings for a multiple-case-design approach, thereby examining the explanatory power of the theory under similar, yet subtly different, conditions. For example, an examination of the role of historic relations between governments on multinational operations may include the same construct or outcome (e.g., foreign investment, knowledge transfer between subsidiaries) using two sets of countries that share similar historic ties of cooperation or competition, such as China and Japan, and Japan and South Korea. A good example of the potential application of this solution is the study by Ambos, Fuchs, and Zimmermann ( 2020 ) examining how headquarters and subsidiaries manage tensions that arise from demands for local integration and global responsiveness.

Solutions for Challenge #3: Less-than-Ideal Research Design

Concerns regarding research-design issues are not new to IB research. For example, Peterson, Arregle & Martin ( 2012 ) and Martin ( 2020 ) discussed levels of analysis. Similarly, Chang, van Witteloostuijn & Eden ( 2010 ), Doty & Astakhova ( 2020 ), and van Witteloostuijn et al., ( 2020 ) addressed common method variance. We contribute to this discussion by identifying how challenges associated with the effects of less-than-ideal research design for theory building and testing – including those associated with levels of analysis and common method variance – can be addressed, at least in part, by relying on developments in the use of Big Data.

Big Data is often characterized by three V’s: volume, velocity, and variety (Laney, 2001 ). Volume refers to the sheer scale of the data, velocity is about the speed with which the data are generated as well as the speed of the analytics process required to meet those demands, and variety is about the many forms that Big Data can take – including structured numeric data, text documents, audio, video, and social media (Chen & Wojcik, 2016 ). It is not unusual to refer to Big Data using other terms, such as data mining, knowledge discovery in databases, data or predictive analytics, or data science (Harlow & Oswald, 2016 ). As such, Big Data differs from commonly used large datasets (e.g., Bloomberg Business, Compustat, ILOSTAT, Orbis, Osiris) that collect large quantities of data on a predefined list of measures over a specific period, as well as from researchers’ own data-collection efforts that seek information regarding a limited set of measures over a pre-determined period. It is important to note that Big Data is not the same as “more data,” particularly when they are of dubious quality. Indeed, ensuring the veracity and value of the data is an important consideration when using Big-Data approaches (Braun, Kuljanin & DeShon, 2018 ; Iafrate, 2015 ; IBM, 2020 ; Zhang, Yang, Chen & Li, 2018 ). Nor is Big Data the solution to fundamental challenges, such as how constructs are defined and operationalized in the first place (Podsakoff, MacKenzie & Podsakoff, 2016 ). Nevertheless, accurate Big Data – sometimes referred to as “Smart Data” – represents a unique opportunity to address some of the challenges posed by less-than-ideal research designs (Tonidandel, King & Cortina, 2018 ).

Big Data can provide unique insights by allowing researchers to examine previously untapped complementary sources of information (George, Haas & Pentland, 2014 ). For example, consider a study about the ownership or governance structures of firms and the situation that traditional datasets do not provide sufficient information regarding these variables. A Big-Data approach can rely on examining articles in newspapers and other business and current-event outlets, publicly available financial and other company filings (e.g., Securities and Exchange Commission filings such as the 10-K, 10-Q, or 8-K reports), and reports and white papers issued by watchdog groups (e.g., Citizens for Responsibility and Ethics in Washington, D.C.). Adopting a Big-Data approach will allow future IB researchers to gain additional insights based on variables not included in public datasets or measured during their data collection, and also when the data included are limited or censored in terms of levels of analysis (e.g., only firm-level data measured), or period-of-data collection (e.g., data only collected for certain years).

Another way to leverage Big-Data techniques to address the methodological challenge of less-than-ideal research design is by re-analyzing currently available data. Returning to the example of patent-based measures, higher-quality research designs will result from using the Big Data technique of, for example, text mining (George, Osinga, Lavie & Scott, 2016 ). Text mining is a data-analytic technique that infers knowledge about desired constructs from unstructured data in the form of text, and is particularly useful when analyzing Big Data (O’Mara-Eves, Thomas, McNaught, Miwa & Ananiadou, 2015 ). In our example, future IB research can use text mining to analyze the co-occurrence of words within patents, or infer meaning from common terms used across patents, to gain a deeper and more comprehensive perspective about knowledge creation and transfer (George et al., 2016 ). Together, using Big Data approaches to examine additional sources of information and re-analyze existing data can help IB researchers address previously identified challenges stemming from issues related to levels of analysis and common method variance.

We readily acknowledge that the prospect of gathering supplemental information using Big-Data methods such as web scraping and text mining may seem daunting. However, there is a growing number of resources, including packages using the free software, R (e.g., Munzert, Rubba, Meißner & Nyhuis, 2014 ; Silge & Robinson, 2016 ), and tutorials and resource articles (e.g., Cheung & Jak, 2016 ; Kosinski, Wang, Lakkaraju & Leskovec, 2016 ; Landers, Brusso, Cavanaugh & Collmus, 2016 ; McKenny, Aguinis, Short & Anglin, 2018 ; Tonidandel et al., 2018 ). These are particularly useful for those with little or no background or experience with Big Data. Moreover, we see great potential in implementing Big-Data solutions in the specific IB domains of international environment and cross-country comparative studies. For example, IB researchers may use Big Data to expand their examinations of questions related to how business processes relate to organizational performance across different countries. These include, for example, marketing strategies and the use of consumer-behavior data (e.g., Hofacker, Malthouse & Sultan, 2016 ; Matz & Netzer, 2017 ), managerial metrics (e.g., Mintz, Currim, Steenkamp & de Jong, 2020 ), knowledge sharing (e.g., Haas, Criscuolo & George, 2015 ), and capital-allocation decisions (e.g., Sun, Zhao & Sun, 2020 ).

Solutions for Challenge #4: Insufficient Evidence About Causal Relations

Like other fields, IB research seeks to study and understand change; it is, therefore, inherently interested in the issue of causality. Indeed, many have noted the need for IB to address the issue of causality to enable greater theoretical progress. For example, Cuervo-Cazurra, Andersson, Brannen, Nielsen & Reuber ( 2016 ), and Reeb, Sakakibara & Mahmood ( 2012 ) noted the potential of using experimental designs to address causality and alternative explanations. Similarly, Reeb et al. ( 2012 ), and Meyer, van Witteloostuijn & Beugelsdijk ( 2017 ) encouraged researchers to be more careful in how they refer to the results of their own studies to avoid implying unwarranted causality. Finally, Shaver ( 2020 ) alluded to the potential of Big Data to help improve causal inferences.

Demonstrating causality is not possible in the absence of critical research-design features such as the existence of different levels of the independent variables (Eden, Stone-Romero & Rothstein, 2015 ; Lonati, Quiroga, Zehnder & Antonakis, 2018 ). In other words, “it is not possible to put right with statistics what has been done wrong by design” (Cook & Steiner, 2010 : 57). This issue is especially challenging for IB research because it is typically conducted in settings that are not conducive to randomized experimental designs (Eden, 2017 ). We propose two solutions – one related to design and one related to analysis – that will help address this methodological challenge.

First, future IB research can make greater use of quasi-experimental designs (Eden, 2017 ; Lonati et al., 2018 ; Stone-Romero & Rosopa, 2008 ). Such designs allow for non-random assignment of units (e.g., employees, firms, industries, countries) to different conditions or for naturally occurring (as opposed to controlled) variation in the independent variable. Doing so combines elements from both non-experimental field studies and randomized experiments to improve confidence in causal inferences (Abadie, Diamond & Hainmueller, 2015 ; Antonakis, Bendahan, Jacquart & Lalive, 2010 ; Banerjee & Duflo, 2009 ; Cuervo-Cazurra, Mudambi, Pedersen & Piscitello, 2017 ). Quasi-experimental designs are particularly well suited for IB research that examines constructs such as changes in government policy, organizational expansion, innovation, and foreign direct investment decisions (e.g., Castellani, Mariotti & Piscitello, 2008 ; Grant & Wall, 2009 ; Kogut & Zander, 2000 ). They are also useful when, as happens often in IB research, a change in an outcome due to a change in an independent variable manifests itself only after a period of time. Examples of quasi-experiments in recent IB-related research include Buckley, Chen, Clegg & Voss’s ( 2018 ) study of risk propensity in foreign direct investment location; Huvaj & Johnson’s ( 2019 ) investigation of the effect of organizational complexity on innovation; and Vandor & Franke’s ( 2016 ) examination of the impact of cross-cultural experience on opportunity-recognition capabilities. Additional examples include how monthly and quarterly earnings estimates might influence a multinational corporation’s expansion plans (Irani & Oesch, 2016 ), how knowledge-sharing and innovation spread across local and foreign subsidiaries (Wang, Noe & Wang, 2014 ), and the impact of home-country corporate social responsibility efforts on foreign operations (Flammer & Luo, 2017 ).

The second solution relates to data analysis. Specifically, future IB research can use necessary-conditions analysis (NCA; Dul, 2016 ). NCA is a data-analytic technique that helps identify the variables that are essential for achieving a particular outcome (Dul, 2016 ). Most data-analytic techniques that are commonly used (e.g., OLS and other types of multiple and multivariate regression, multilevel modeling, and structural-equation modeling) generally operate on the basis of a compensatory or additive logic. In that approach, variables can compensate for each other in predicting an outcome, and certain variables may be completely absent (i.e., have a value of zero). NCA relies on a multiplicative logic in which the lack of a single variable eliminates the possibility of achieving an outcome (Dul, 2016 ; Dul, Hak & Goertz, 2010 ). By understanding which variables are critical to the presence of a desired outcome, NCA can help future IB researchers gain a better understanding of causal effects when examining a particular outcome.

For example, consider the goal of examining how firms differ in their approaches to entering foreign markets. A study addressing this issue may examine potential determinants such as overseas market similarity and potential, company internal competitiveness, company familiarity with entering overseas markets, and company internal cash reserves (Koch, 2001 ). Using the resulting estimates, such as regression coefficients from additive data-analytic techniques, provides information on how each of these variables differentially covaries with the outcome. In addition, these results may indicate that the absence of a particular variable – say, lack of familiarity with entering overseas markets – can be compensated for by higher levels of another – say, company internal cash reserves. While useful, these results do not provide information about whether the lack of familiarity causes firms to differ in how they approach foreign-market entry. In contrast, NCA relies on a multiplicative logic. Using NCA provides information not only on whether the lack of familiarity causes firms to choose different strategies but also the minimum level of familiarity needed for choosing one approach over another. Note that in both instances the data analyzed are the same (e.g., archival, cross-sectional survey), and it is only the data-analytical technique that differs. Therefore, combining NCA with additive data-analytical techniques can help future IB researchers improve knowledge about underlying causal structures and causal chains even when using data collected with non-experimental designs.

Finally, we wish to clarify that NCA is not simply a new form of data mining in which theory does not play an important role. Instead, NCA is an analytical technique that can help IB researchers advance theory by applying the principles of strong inference (Leavitt et al., 2010 ) to examine competing theory-driven hypotheses. Because NCA operates on a “necessary but not sufficient” logic, it can only be used to identify determinants whose absence would prevent an outcome from being achieved, but not the determinants that will produce the outcome (Dul, 2016 ). For example, in the illustration in the previous paragraph, NCA can identify whether or not lack of familiarity causes differences in foreign-market entry strategies. However, it cannot provide insight into how other determinants influence the adoption of different foreign-market entry strategies. Accordingly, NCA represents a complement for, not a replacement of, current analytical approaches such as, for example, OLS regression. Examples of IB-related studies that have used NCA include the role of contracts and trust in driving innovation (Van der Valk, Sumo, Dul & Schroeder, 2016 ), and the link between specific firm capabilities and performance (Tho, 2018 ).

LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH

Our specific goal was to identify contemporary methodological challenges faced by IB researchers, as described by JIBS authors themselves, and to offer solutions to each of these challenges. In this section, we offer suggestions for future research that would go beyond and expand upon our article’s specific goals.

First, we focused on articles published in a single volume of JIBS. Although we have no reason to believe that the 2018 volume represents an outlier, future research would benefit from a longitudinal examination of trends to gain insights on possible changes in methodological challenges over time. For example, Cascio and Aguinis ( 2008 ) content-analyzed all articles published in the Journal of Applied Psychology and Personnel Psychology from January 1963 to May 2007 to identify the relative attention devoted to each of 15 broad topical areas and 50 more specific subareas in the field of industrial and organizational psychology. Regarding the evolution of research methodology, Aguinis, Pierce, Bosco & Muslin ( 2009 ) content analyzed the 193 articles published in the first ten volumes (1998 to 2007) of Organizational Research Methods to understand which research design, measurement, and data-analysis topics had become more or less popular over time. A similar longitudinal analysis based on JIBS and other IB journals focusing on methodological challenges would help us understand not only limitations over time, and what changes and improvements have taken place, but also provide insights into the importance of these limitations in relation to the cumulative nature of IB knowledge. For example, there is a possibility that certain limitations (e.g., insufficient evidence about causal relations) are directly linked to methodological improvements over time (e.g., the ability to conduct field quasi-experiments and true experiments). The introduction and popularization of methodological improvements (e.g., less frequent use of cross-sectional research designs) would then be linked to a concomitant decrease in particular methodological limitations (e.g., decreased concern about insufficient evidence about causal relations). Additional potential insights from future longitudinal research may show that definitions and measures of common IB constructs such as cultural distance have indeed improved over time (Beugelsdijk et al. 2018 ; Peterson & Muratova, 2020 ), thereby leading to less-frequent concerns about psychometric properties in certain substantive domains. Finally, improvements in measures over time could also have concomitant implications for the use of control variables, given that their psychometric properties have important implications for the validity and fit of models. Unfortunately, this information is seldom investigated or reported (Bernerth & Aguinis, 2016 ; Nielsen & Raswant, 2018 ). Overall, we see great value in future research adopting a longitudinal approach to examining methodological challenges and limitations.

Second, as noted earlier, we did not examine which challenges were added to or removed from an article through the peer-review process – and which ones authors chose to address. Another extension of our work, therefore, would be to access reviewer comments and possibly also contact authors to understand the reasons for the various methodological choices they made and which challenges and limitations they chose to disclose, or not disclose, and the reasons why. Relatedly, Green, Tonidandel & Cortina ( 2016 ) were able to access 304 editors’ and reviewers’ letters for 69 manuscripts submitted to Journal of Business and Psychology and coded the statistical and methodological issues raised in the reviewing process. As a result, they were able to draw conclusions about the comments and suggestions that reviewers and editors made about methodological issues when making recommendations and decisions to accept or reject manuscripts. This same approach could be used with IB manuscripts to learn about the process that resulted in specific methodological challenges and limitations being included in a published article – and why.

A third issue that we believe warrants future research is an investigation of the disclosure of methodological challenges and limitations in relation to researchers’ motivations and the prevailing reward systems in universities (Aguinis et al. 2020b ; Rasheed & Priem, 2020 ). Disclosing a study’s limitations is clearly an important factor to understand the rigor, value, and usefulness of the knowledge that has been produced. Yet, as Nielsen et al. ( 2020 : 7) noted, “scholars often view the benefits from research integrity as accruing only in the long term and primarily to society as a whole. In the short term, pressure to publish and the desire for tenure and promotion may be much more salient.” The pressure to publish in what are considered top journals has never been higher. The motto “a win is a win” used in sports is now often used to describe the publication of a paper in a prestigious journal (Aguinis et al. 2020b ). Many of us have witnessed faculty-recruiting as well as promotion-and-tenure committees discuss how many A-level publications a candidate has produced and how many A-level publications are needed for a favorable decision, while the unique intellectual value and methodological rigor of a publication do not receive nearly the same amount of attention (Aguinis et al. 2020b ). Given this situation, there are now calls to improve the transparency and disclosure of all methodological procedures (Aguinis et al., 2017 ; Aguinis & Solarino, 2019 ; Delios, 2020 ), including challenges and limitations. For these changes to happen, however, reward systems, including journal-submission policies, may need to change. To this point, Aguinis, Banks, Rogelberg, and Cascio ( 2020a ) offered ten actionable recommendations for reducing questionable research practices that do not require a substantial amount of time or resources on the part of journals, professional associations, and funding agencies. These include (1) updating the knowledge-production process (e.g., preregistration of quantitative and qualitative primary studies); (2) updating knowledge-transfer and knowledge-sharing processes (e.g., an online archive for each journal article where authors can voluntarily place any study materials they wish to share); (3) changing the incentive structure (e.g., best-paper award and acknowledgement based on open-science criteria); (4) improving access to training resources (i.e., open-access training for authors and reviewers); and (5) promoting shared values (i.e., editorial statements that null results, outliers, “messy” findings, and exploratory analyses can advance scientific knowledge if a study’s methodology is rigorous).

Finally, there are several additional methodological challenges, some of which are discussed in the chapters included in Eden et al. ( 2020 ), which we did not address in our article. Examples include the extent to which robustness checks and sensitivity tests are frequent and useful in IB research and the extent to which power analysis is conducted and reported in IB research. These are important methodological topics that certainly warrant future investigation, even though they were not mentioned frequently in the articles included in our review.

As IB research becomes more diverse and complex, there is an increased awareness that methodological approaches require attention to both similarities and differences between domestic and foreign operations at multiple levels of analysis (Eden et al., 2020 ). Such approaches must be able to provide answers to local-seeking global questions about processes that are locally embedded, relationally enacted, and iteratively unfolding. Careful attention to methodological challenges is critical for ensuring continued IB theoretical advancements and the relevance of practical contributions. Adopting a perspective that combined an AAR with a needs-assessment approach, and using JIBS as a case study, we described the four most frequently mentioned contemporary methodological challenges identified by JIBS authors themselves. These are: psychometrically deficient measures, idiosyncratic samples or contexts, less-than-ideal research designs, and insufficient evidence about causal relations. Building upon existing work, and particularly chapters included in the edited volume by Eden et al. ( 2020 ), we proposed solutions that rely mostly on methodological innovations from outside of IB that can be used to address each of these challenges. Our article can be used as a resource by journal editors and reviewers to anticipate which methodological challenges they are most likely to encounter in the manuscripts they consider for possible publication. IB researchers can use our article as a resource to anticipate and address these challenges before they collect and analyze data. Instructors can also use our article for training the next generation of IB scholars. Overall, we hope that it will serve as a catalyst for theory advancements in future empirical research not only in JIBS but also in other journals in IB and related fields.

For example, an article in JAP (Wolfson, Tannenbaum, Mathieu & Maynard, 2018 ) noted that the measures they used to assess performance might have been psychometrically deficient. Similarly, an article in SMJ (Furr & Kapoor, 2018 ) urged caution when interpreting their findings due to the use of an idiosyncratic sample or context. As another example, an article in AMJ (Miron-Spektor, Ingram, Keller, Smith & Lewis, 2018 ) noted that the study had a less-than-ideal research design. Finally, a JOM article (Kuypers, Guenter & van Emmerik, 2018 ), noted that a limitation of the study was insufficient evidence about causal relations. As we describe in the next section of our article, these are the four most pervasive methodological challenges mentioned by JIBS authors.

Similar to Brutus et al. ( 2013 ), we utilized a methodological lens to analyze limitations noted by authors in published research. However, a major difference is that Brutus et al.’s ( 2013 ) main goal was to improve the manner in which authors report the limitations of their research. That is, they attempted to influence ex-post decisions regarding the transparency and usefulness of author-disclosed limitations that might affect a study’s results and conclusions. In contrast, our goal is to help advance IB theory by addressing the most typical methodological challenges. That is, our goal is to offer ex-ante and ex-post solutions to help researchers make theoretical advancements by overcoming these methodological limitations.

In the interest of transparency, we make the complete list of articles (including which article referred to which challenge), available upon request.

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Aguinis, H., Ramani, R.S. & Cascio, W.F. Methodological practices in international business research: An after-action review of challenges and solutions. J Int Bus Stud 51 , 1593–1608 (2020). https://doi.org/10.1057/s41267-020-00353-7

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Business Research Methods

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Emma Bell, Bill Harley, Alan Bryman

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The complete introduction to doing business research and is the ideal guide for students embarking on a research project.

Business Research Methods  is the clearest, most relevant guide, written specifically to engage business students taking research methods courses or completing a research project. The sixth edition offers extensively-revised global examples throughout, as well as unique interviews with students and educators providing invaluable real-world insights and advice.

The authors have extensively revised this edition to make it the most engaging and relevant text available. New chapters on quantitative methods and visual research offer extensive coverage of these areas and even greater practical support in applying these techniques, while cutting-edge material on inclusivity and bias in research, feminist perspectives, and decolonial and indigenous research is also introduced.  'Student experience' features provide practical tips, presenting personal insights and advice from fellow students to help you avoid common mistakes and follow others' successful strategies when undertaking your own research project. 'Research in Focus' features also highlight a greater global range of examples, including new case studies from China, Denmark, Germany, Spain, and India, all of which demonstrate how fascinating and essential research can be.

Above all else, the book places strong emphasis on those challenges faced most frequently by students, such as choosing a research question, planning a project, and writing it up. Presenting essential topics in a concise way, Business Research Methods will provide you with key information without becoming overwhelming: it is now even clearer, more focused, and more relevant than ever before.

KEY FEATURES

• Highly comprehensive and exceptionally well-written: a complete and clear guide to the process of conducting business research.
• The only business research book to use interviews with students and educators to provide invaluable real-world insights and advice on potential pitfalls to avoid, and successful strategies to emulate, when undertaking a research project.
• Examples from various business functions - including marketing, strategy, accounting, and human resource management - span cultures and geographies, and clearly show the relevance of business research to the real world.
• 'Tips and skills' and 'Checklists' boxes help students progress with their own project, and become equipped with the skills needed to become successful business researchers in life beyond university.
• Extensive online resources include interviews with research students and educators, video tutorials, a research project guide, multiple-choice questions, and data sets - all designed to help students excel on their business research course.
• Also available as an eBook enhanced with self-assessment activities and multi-media content to offer a fully immersive experience and extra learning support.

Available as an enhanced eBook via VitalSource .

Part 1 - The research process 1: The nature and process of business research 2: Business research strategies 3: Research designs 4: Planning a research project and developing research questions 5: Getting started: reviewing the literature 6: Ethics in business research 7: Writing up business research

Part 2 - Quantitative research 8: The nature of quantitative research 9: Sampling in quantitative research 10: Structured interviewing 11: Self-completion questionnaires 12: Asking questions 13: Quantitative research using naturally occurring data 14: Secondary analysis and official statistics 15: Quantitative data analysis: descriptive statistics 16: Quantitative analysis: inferential statistics

Part 3 - Qualitative research 17: The nature of qualitative research 18: Sampling in qualitative research 19: Ethnography and participant observation 20: Interviewing in qualitative research 21: Focus groups 22: Language in qualitative research 23: Documentary data 24: Visual qualitative research 25: Qualitative data analysis

Part 4 - Mixed methods research 26: Breaking down the quantitative/qualitative divide 27: Mixed methods research: combining quantitative and qualitative research

Emma Bell , Professor of Organisation Studies, The Open University

Bill Harley , Professor of Management and Marketing, University of Melbourne

Alan Bryman , Professor of Organizational and Social Research, University of Leicester (formerly)

Bill Harley is Professor of Management in the Department of Management and Marketing at The University of Melbourne. His work has been published in journals including the British Journal of Industrial Relations, Journal of Management Studies, Academy of Management Learning & Education Industrial Relations, and Work Employment and Society. Bill was previously General Editor of Journal of Management Studies and is currently on the editorial boards of the same journal, as well as Academy of Management Learning & Education, Journal of Applied Behavioral Sciences and Human Relations. He is Chair of the Society for the Advancement of Management Studies.

Emma Bell is Professor of Organisation Studies at the Open University, UK. Her research explores culture, belief, and materiality in organizations using qualitative methods of inquiry and has been published in journals including Organization Studies, Human Relations, Academy of Management Learning & Education, Organization, Management Learning and the British Journal of Management. Emma is currently joint Vice-Chair Research and Publications of the British Academy of Management and a Fellow of the Academy of Social Sciences.

Alan Bryman was Professor of Organizational and Social Research at the University of Leicester from 2005 to 2017. Prior to this he was Professor of Social Research at Loughborough University for thirty-one years. His main research interests were in leadership, especially in higher education, research methods (particularly mixed methods research), and the 'Disneyization' and 'McDonaldization' of modern society. Alan was also the author of the bestselling textbook Bryman's Social Research Methods (Oxford University Press, 2021) as well as contributing to a range of leading journals: he was an extraordinarily well-cited and internationally-renowned social scientist.

Student Resources

Business Research Methods , sixth edition, comes with complimentary student resources. These include:

• Video tutorials covering SPSS, Nvivo, R, and Stata.
• Self-test multiple choice questions with answer feedback
• Research project guide
• Video interviews with students and lecturers
• Links to additional resources (articles, data repositories, and third-party guides)
• Guide to using Excel in data analysis Flashcard glossary

Teacher Resources

The following resources are available for lecturers who Business Research Methods , sixth edition, for their course:

• PowerPoint presentations
• Additional case studies
• Discussion questions
• Lecturer's guide (includes suggested lecture outlines, problem-spotting, and practical teaching tips)
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• Figures from the text

211 Business Topics For Research Paper [Updated]

Are you looking for intriguing business topics to explore in your research paper? Whether you’re a student delving into the world of business studies or a seasoned professional seeking fresh insights, selecting the right topic is crucial. In this blog, we’ll walk you through a diverse array of business topics for research paper. From management strategies to emerging trends like sustainability and digital transformation, there’s something for everyone. Let’s dive in!

What Are The Characteristics of Business Research Topics?

Table of Contents

Business research topics possess several key characteristics that distinguish them from other types of research topics. These characteristics include:

• Relevance: Business research topics should address current issues, trends, and challenges facing the business world. They should be of interest to academics, practitioners, and policymakers alike.
• Practicality: Business research topics should have real-world applicability and relevance to industry practices. They should offer insights that can be implemented to improve organizational performance, decision-making, and strategy.
• Interdisciplinary Nature: Business research often draws from multiple disciplines such as economics, management, marketing, finance, and psychology. Topics should be interdisciplinary in nature, incorporating insights from various fields to provide comprehensive analysis.
• Data-Driven: Business research relies heavily on empirical evidence and data analysis. Topics should lend themselves to quantitative, qualitative, or mixed-method research approaches, depending on the research question and objectives.
• Innovation and Creativity: Business research topics should encourage innovative thinking and creative problem-solving. They should explore emerging trends, disruptive technologies, and novel approaches to address business challenges.
• Ethical Considerations: Ethical considerations are paramount in business research. Topics should adhere to ethical principles and guidelines, ensuring the protection of participants’ rights and the integrity of research findings.
• Global Perspective: Business research topics should consider the global context and implications of business decisions and practices. They should explore cross-cultural differences, international markets, and global trends shaping the business landscape.
• Impact: Business research topics should have the potential to generate meaningful insights and contribute to the advancement of knowledge in the field. They should address pressing issues and offer practical solutions that can drive positive change in organizations and society.

By embodying these characteristics, business research topics can effectively address the complexities and challenges of the modern business environment, providing valuable insights for academic scholarship and practical application.

211 Business Topics For Research Paper

• The Impact of Leadership Styles on Employee Motivation
• Strategies for Managing Multicultural Teams Effectively
• The Role of Emotional Intelligence in Leadership Success
• Marketing Strategies for Small Businesses on a Limited Budget
• The Influence of Social Media Marketing on Consumer Behavior
• Brand Loyalty: Factors Influencing Consumer Purchase Decisions
• Ethical Considerations in Advertising Practices
• Financial Risk Management in Multinational Corporations
• Corporate Governance and Financial Performance
• The Role of Financial Derivatives in Hedging Market Risks
• Success Factors for Startups in Competitive Markets
• Innovation and Entrepreneurship: Key Drivers of Economic Growth
• Challenges and Opportunities in Scaling a Business Globally
• Ethical Dilemmas in Business Decision-Making
• Corporate Social Responsibility Practices and Brand Image
• Balancing Profit Motives with Social and Environmental Concerns
• The Business Case for Sustainability Initiatives
• Renewable Energy Adoption in Businesses
• Circular Economy Models and Business Sustainability
• The Impact of Digital Technologies on Traditional Business Models
• E-Commerce Trends and Consumer Preferences
• Cybersecurity Challenges in E-Commerce Transactions
• The Benefits of Diversity in the Workplace
• Strategies for Promoting Gender Equality in Leadership Roles
• Addressing Unconscious Bias in Recruitment Processes
• The Impact of Remote Work on Employee Productivity
• Flexible Work Arrangements and Work-Life Balance
• The Role of Corporate Culture in Employee Engagement
• Talent Management Strategies for Attracting and Retaining Top Talent
• Performance Appraisal Systems: Best Practices and Challenges
• Workplace Diversity and Inclusion Initiatives
• Employee Training and Development Programs
• Change Management Strategies for Organizational Transformation
• Crisis Management and Business Continuity Planning
• Supply Chain Resilience: Lessons Learned from Disruptions
• Sustainable Sourcing Practices in Supply Chain Management
• Inventory Management Strategies for Reducing Costs
• Logistics Optimization for Efficient Operations
• The Impact of Globalization on Supply Chain Networks
• Strategic Alliances and Collaborative Partnerships in Business
• Mergers and Acquisitions: Drivers and Challenges
• Corporate Restructuring Strategies for Turnaround Success
• The Role of Corporate Social Responsibility in Building Customer Trust
• Reputation Management in the Digital Age
• Crisis Communication Strategies for Managing Reputational Risks
• Customer Relationship Management: Strategies for Enhancing Customer Loyalty
• Personalization Techniques in Marketing and Customer Service
• Omnichannel Retailing: Integrating Online and Offline Channels
• The Future of Brick-and-Mortar Retail in the Digital Era
• Pricing Strategies for Maximizing Profitability
• Revenue Management Techniques in Hospitality Industry
• Brand Extension Strategies and Brand Equity
• Customer Experience Management: Best Practices and Trends
• The Impact of Artificial Intelligence on Business Operations
• Machine Learning Applications in Marketing and Sales
• Automation and Robotics in Manufacturing Processes
• Blockchain Technology: Opportunities and Challenges for Businesses
• Augmented Reality and Virtual Reality in Marketing
• Data Privacy and Security Concerns in the Digital Age
• The Role of Big Data Analytics in Business Decision-Making
• Predictive Analytics for Sales Forecasting and Demand Planning
• Customer Segmentation Techniques for Targeted Marketing
• The Influence of Cultural Factors on Consumer Behavior
• Cross-Cultural Marketing Strategies for Global Brands
• International Market Entry Strategies: Modes of Entry and Risks
• Exporting vs. Foreign Direct Investment: Pros and Cons
• Market Entry Strategies for Emerging Markets
• The Impact of Political and Economic Factors on International Business
• Foreign Exchange Risk Management Strategies
• Cultural Intelligence and Global Leadership Effectiveness
• The Role of Multinational Corporations in Economic Development
• Corporate Governance Practices in Different Countries
• Comparative Analysis of Business Laws and Regulations
• Intellectual Property Rights Protection in Global Business
• The Influence of Cultural Differences on Negotiation Styles
• Cross-Border Mergers and Acquisitions: Legal and Cultural Challenges
• International Trade Agreements and Their Impact on Businesses
• The Role of Non-Governmental Organizations in Sustainable Development
• Corporate Philanthropy and Social Impact Investing
• Microfinance and Economic Empowerment of Women
• Entrepreneurship Ecosystems and Innovation Hubs
• Government Policies and Support for Small Businesses
• Venture Capital Financing and Startup Growth
• Crowdfunding Platforms: Opportunities for Entrepreneurs
• Social Entrepreneurship: Business Models for Social Change
• Innovation Clusters and Regional Economic Development
• Angel Investors and Their Role in Startup Funding
• Technology Incubators: Nurturing Startup Innovation
• Intellectual Property Rights Protection for Startup Innovations
• Business Model Innovation: Disrupting Traditional Industries
• The Impact of Climate Change on Business Operations
• Green Technologies and Sustainable Business Practices
• Carbon Footprint Reduction Strategies for Businesses
• Environmental Management Systems and Certification
• Corporate Reporting on Environmental Performance
• Circular Economy Business Models: Closing the Loop
• Sustainable Supply Chain Management Practices
• The Role of Renewable Energy in Achieving Carbon Neutrality
• Smart Cities and Sustainable Urban Development
• Green Building Technologies and Sustainable Construction
• The Influence of Cultural Factors on Entrepreneurship
• Gender Differences in Entrepreneurial Intentions and Success
• Social Capital and Networking for Entrepreneurial Ventures
• Family Business Succession Planning and Governance
• Corporate Entrepreneurship: Fostering Innovation Within Organizations
• Franchising: Opportunities and Challenges for Entrepreneurs
• Online Platforms and the Gig Economy
• Digital Nomads: Remote Work and Entrepreneurship
• The Sharing Economy: Business Models and Regulation
• Blockchain Applications in Supply Chain Traceability
• Cryptocurrency Adoption in Business Transactions
• Initial Coin Offerings (ICOs) and Tokenization of Assets
• Decentralized Finance (DeFi) and Its Implications for Traditional Banking
• Smart Contracts and Their Potential in Business Operations
• Privacy-Preserving Technologies in Data Sharing
• Cryptocurrency Exchanges: Regulation and Security Issues
• Central Bank Digital Currencies (CBDCs) and Monetary Policy
• The Impact of Artificial Intelligence on Financial Services
• Robo-Advisors and Algorithmic Trading in Wealth Management
• Fintech Startups and Disruption in Traditional Banking
• Peer-to-Peer Lending Platforms: Opportunities and Risks
• Digital Identity Management Systems and Security
• Regulatory Challenges in Fintech Innovation
• Financial Inclusion and Access to Banking Services
• Green Finance: Sustainable Investment Strategies
• Socially Responsible Investing and ESG Criteria
• Impact Investing: Financing Social and Environmental Projects
• Microfinance Institutions and Poverty Alleviation
• Financial Literacy Programs and Consumer Empowerment
• Behavioral Finance: Understanding Investor Behavior
• Risk Management Strategies for Financial Institutions
• Corporate Fraud Detection and Prevention Measures
• Financial Market Volatility and Risk Hedging Strategies
• The Role of Central Banks in Monetary Policy Implementation
• Financial Stability and Systemic Risk Management
• Corporate Governance Practices in Banking Sector
• Credit Risk Assessment Models and Default Prediction
• Asset Allocation Strategies for Portfolio Diversification
• Real Estate Investment Strategies for Wealth Accumulation
• Commercial Property Valuation Methods
• Real Estate Crowdfunding Platforms: Opportunities for Investors
• Property Management Best Practices for Rental Properties
• Real Estate Development and Urban Planning
• Mortgage Market Trends and Homeownership Rates
• Affordable Housing Initiatives and Government Policies
• The Impact of Interest Rates on Real Estate Investments
• Sustainable Architecture and Green Building Design
• Real Estate Investment Trusts (REITs) and Tax Implications
• The Influence of Demographic Trends on Housing Demand
• Residential Property Flipping Strategies and Risks
• Health and Wellness Tourism: Trends and Opportunities
• Medical Tourism Destinations and Quality of Care
• Wellness Retreats and Spa Resorts: Business Models
• The Impact of Technology on Healthcare Delivery
• Telemedicine and Remote Patient Monitoring
• Healthcare Data Security and Privacy Regulations
• Healthcare Financing Models: Insurance vs. Out-of-Pocket
• Value-Based Healthcare Delivery and Payment Models
• Healthcare Workforce Challenges and Solutions
• Healthcare Infrastructure Development in Emerging Markets
• The Role of Artificial Intelligence in Healthcare Diagnosis
• Precision Medicine: Personalized Treatment Approaches
• Pharmaceutical Industry Trends and Drug Development
• Biotechnology Innovations in Healthcare Solutions
• Mental Health Awareness and Support Services
• Telehealth Adoption and Patient Engagement
• Chronic Disease Management Programs and Prevention
• Health Information Exchange Platforms: Interoperability Challenges
• Patient-Centered Care Models and Outcomes
• The Influence of Healthcare Policies on Access to Care
• Human Resource Management in the Hospitality Industry
• Employee Training and Development in Tourism Sector
• Quality Service Delivery in the Hotel Industry
• Revenue Management Strategies for Hospitality Businesses
• Destination Marketing and Tourism Promotion Campaigns
• Sustainable Tourism Practices and Eco-Friendly Resorts
• Technology Integration in Travel and Tourism Services
• Cultural Heritage Tourism and Conservation Efforts
• Adventure Tourism: Risks and Safety Measures
• The Role of Online Travel Agencies in Tourism Distribution
• Sustainable Transportation Solutions for Tourism
• Food and Beverage Management in Hospitality Operations
• Wellness Tourism: Trends and Market Segmentation
• Airbnb and Short-Term Rental Market Dynamics
• Wellness Retreats and Spas: Market Positioning Strategies
• Community-Based Tourism Development Initiatives
• Luxury Travel Market: Trends and Consumer Preferences
• Aviation Industry Trends and Airline Marketing Strategies
• Sustainable Event Management Practices
• Convention and Exhibition Tourism: Economic Impact
• Destination Management Organizations and Tourism Planning
• Customer Relationship Management in the Tourism Sector
• Online Reputation Management for Hospitality Businesses
• Accessibility and Inclusivity in Tourism Infrastructure
• Cultural Tourism: Heritage Preservation and Promotion
• Agritourism: Farm-to-Table Experiences and Trends
• The Impact of Climate Change on Tourism Destinations
• Wildlife Tourism: Conservation and Responsible Practices
• Wellness Tourism in Developing Countries: Challenges and Opportunities
• The Role of Tour Operators in Sustainable Tourism Development
• Virtual Reality Applications in Tourism Marketing
• The Rise of Medical Tourism: Market Growth and Challenges
• Responsible Travel and Ethical Tourism Practices
• Event Marketing Strategies for Business Success
• Sponsorship and Partnership Opportunities in Event Management
• Technology Integration in Event Planning and Execution
• Event Risk Management and Contingency Planning
• Corporate Event Planning: Trends and Best Practices
• Trade Show Marketing Strategies for Exhibitors
• Sports Event Management : From Planning to Execution
• Sustainable Event Certification Programs and Standards

How To Prepare Research Paper?

Preparing a research paper involves several key steps, from selecting a topic to writing and formatting the final document. Here’s a comprehensive guide on how to prepare a research paper:

• Select a Topic: Choose a topic that interests you and aligns with the requirements of your assignment or research objectives. Consider the scope of the topic, its relevance, and the availability of resources for conducting research.
• Conduct Background Research: Read up on books and studies that talk about the same things you want to research. This will help you see what people already know, find out where there are still things we don’t know, and make your research questions or ideas better.
• Develop a Research Question or Thesis Statement: Formulate a clear and focused research question or thesis statement that guides your study. Your research question should be specific, relevant, and capable of being answered through empirical investigation.
• Create an Outline: Organize your ideas and research findings into a logical structure by creating an outline for your research paper. Outline the introduction, literature review, methodology, results, discussion, and conclusion sections, along with any subheadings or subsections.
• Write the Introduction: Begin your research paper with an interesting introduction. Share some basic info about your topic, explain why your study is important, and clearly state what you’ll be focusing on in your research. The introduction should also outline the structure of the paper.
• Review the Literature: Conduct a comprehensive review of relevant literature to provide context for your study, support your arguments, and identify gaps in existing research. Summarize key findings, theories, and methodologies from previous studies in your literature review.
• Describe the Methodology: Clearly explain the research design, methods, and procedures used to collect and analyze data. Include details on the population/sample, data collection instruments, data analysis techniques, and any ethical considerations.
• Present the Results: Report the findings of your study in a clear and concise manner. Use tables, graphs, or charts to present quantitative data, and provide descriptive analysis for qualitative data. Ensure that your results are relevant to your research question or thesis statement.
• Discuss the Implications: Interpret the results of your study and discuss their implications that are for theory, practice, or policy. Analyze the strengths and limitations of your research, address any unexpected findings, and propose recommendations for future research or action.
• Write the Conclusion: Summarize the key findings and contributions of your study in the conclusion section. Restate your research question or thesis statement, review the main points that you have discussed in the paper, and highlight the significance of your research in advancing knowledge in the field.
• Revise and Edit: Review your research paper for clarity, coherence, and accuracy. Ensure that your arguments are well-supported by evidence, your writing is concise and precise, and your paper follows the appropriate style and formatting guidelines.
• Cite Sources: Acknowledge the contributions of other scholars by properly citing their work in your research paper. Use a consistent citation style (e.g., APA, MLA, Chicago) and include a reference list or bibliography at the end of your paper.
• Proofread: Carefully proofread your research paper to correct any spelling, grammar, or punctuation errors. Pay attention to formatting details such as margins, font size, and line spacing to ensure consistency throughout the document.
• Get Feedback: Seek feedback from peers, instructors, or mentors to improve the quality of your research paper. Consider their suggestions for revision and make appropriate changes to strengthen your arguments and clarify your writing.
• Finalize the Paper: Make any final revisions or edits based on feedback and proofreading, and then finalize your research paper for submission. Double-check all formatting requirements and ensure that your paper is properly formatted and ready for submission.

Final Thoughts

Researching business topics offers a unique opportunity to delve into the complexities of the modern economy and explore innovative solutions to real-world challenges.

Whether you’re passionate about leadership, marketing, finance, entrepreneurship, or corporate social responsibility, there’s a wealth of knowledge waiting to be discovered. So roll up your sleeves, sharpen your analytical skills, and get ready to make your mark in the world of business research! I hope you find the best and most relevant answer to business topics for research paper. 

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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How to Write a Research Paper: A Comprehensive Step-by-Step Guide

Preliminary research stands as a cornerstone in crafting a compelling research paper, serving as the blueprint for making informed decisions and setting a strong foundation.

This initial step resembles a small-scale experiment, pivotal in shaping the direction of your research by identifying viable paths and avoiding potential pitfalls. It's a phase where time and effort invested pay dividends, leading to efficient planning, potential funding opportunities, and a streamlined research process. 

We will explore critical stages from choosing a topic, conducting thorough research, to drafting and concluding your paper effectively. This guide will also illuminate the essentials of constructing a research paper, including formatting, outlining, and the importance of a compelling research paper conclusion. 

For those wondering how to start a research paper, this article promises clarity, direction, and a host of research paper examples to inspire confidence at every stage of your writing process.

Understanding the Assignment

Key elements of the assignment.

• Identify the Purpose and Audience : Understanding the purpose of your research paper and who the intended audience is will guide your approach to the topic. Whether it's to inform, argue, or analyze, the purpose will shape your writing style and content.
• Review Assignment Details : Carefully read the assignment guidelines to grasp the requirements such as length, formatting, and citation style. This includes specifics like whether footnotes, subtitles, and headings are needed, and if the text should be double-spaced.
• Confirm Source Requirements : Determine the types and quantity of sources allowed, such as websites, books, and articles. This ensures that the research foundation you build is robust and adheres to the guidelines provided.

Planning and Preparation

• Create a Timeline : Establish a schedule that outlines key milestones leading up to the due date. This helps in managing time effectively, ensuring there's ample time for research, writing, and revision.
• Select a Suitable Topic : Choose a topic that fits within the parameters set by the assignment. It should be broad enough to explore extensively, yet focused enough to provide detailed analysis and discussion.

Execution and Compliance

• Consult with Your Professor : Before finalizing your topic and beginning in-depth research, run it by your professor to confirm its suitability.
• Understand Citation Styles : Familiarize yourself with the preferred citation style for the assignment. This is crucial for avoiding plagiarism and ensuring that all sources are properly credited.
• Adapt to Assignment Length : The required length of the paper often dictates the scope of the topic. Shorter papers need a narrow focus, while longer papers can cover more ground.

By adhering to these guidelines, students can ensure that their research paper not only meets the academic standards but also communicates their arguments effectively to their intended audience.

Choosing a Topic

Selecting the right topic is crucial for writing a successful research paper. This choice not only impacts your research direction but also influences how engaging and insightful your paper will be. Here's how to navigate the process of choosing your topic:

Generate and Refine Your Idea

• Start with Your Interests : Identify topics that spark your curiosity and passion. This makes the research process more enjoyable and engaging.
• Brainstorm Ideas : Discuss potential topics with peers or mentors, and use brainstorming techniques to expand your ideas.
• Explore Existing Research : Review current literature to find gaps in knowledge or emerging issues that interest you.
• Narrow Your Focus : Avoid topics that are too broad; instead, aim for a specific angle that allows for deep exploration.

Develop a Research Strategy

• Background Research : Gather preliminary information to understand the broader context of your topic.
• Formulate a Research Question : Define a clear, focused question that your research will answer.
• Consult Experts : Engage with academic advisors or experts in the field to refine your question and approach.
• Consider Multiple Perspectives : Look at historical, geographical, and sociological aspects to broaden your approach.

Choose and Validate Your Topic

• Confirm Information Availability : Ensure there are sufficient resources and research available to support your paper.
• Check for Originality : Aim for a unique angle that can provide new insights or solutions.
• Adaptability : Be prepared to tweak your topic as you delve deeper into research and discover new information.

By following these steps, you can select a topic that is not only interesting but also manageable and researchable, setting the stage for a compelling research paper.

Conducting Preliminary Research

Embarking on preliminary research is akin to setting the stage for a successful performance. This initial phase is crucial in determining the direction and depth of your research paper. Here’s a streamlined approach to conducting effective preliminary research:

Step 1: Diverse Source Exploration

• Gather a Wide Range of Materials : Start by collecting information from a variety of sources such as books, peer-reviewed journals, and reputable websites. This broad spectrum ensures a comprehensive understanding of the subject.
• Library and Online Databases : Utilize your library’s electronic databases to access academic articles and consult the reference section for specialized titles. Online search engines and directories can also offer valuable information.
• Document Your Sources : Keep meticulous records of all sources, noting down important citations. This will be invaluable when you need to reference these materials in your research paper.

Step 2: Refining Your Research

• Early Research Benefits : Begin your research early as this allows you to refine your topic and develop a strong thesis statement. This early start also helps in identifying whether the available information meets the needs of your research scope.
• Evaluate and Select Information : Assess the quality and relevance of the information gathered. This involves checking the credibility of sources and ensuring the data supports your research objectives.
• Organizational Skills : Organize the collected information systematically. This helps in identifying patterns or gaps in data which could guide further research or question formulation.

Step 3: Preliminary Analysis

• Initial Data Analysis : Start analyzing the data from a smaller sample to understand trends and outcomes. This can guide the feasibility of expanding the research.
• Formulate Research Questions and Objectives : Based on the initial findings, develop specific research questions and set clear objectives to maintain focus.
• Choose Research Methods : Decide whether your study will adopt a qualitative or quantitative approach and select appropriate methods for data collection and analysis.

By meticulously following these steps, you lay a solid foundation for your research paper, ensuring that each subsequent phase builds upon a well-researched and organized base.

Creating an Outline

Creating a well-structured outline is a pivotal step in the process of writing a research paper. It acts as a blueprint, helping you organize your thoughts and ensuring your research is systematic and coherent. Here’s how to effectively construct your outline:

Step-by-Step Breakdown of Outline Creation

• Start with the Thesis Statement : Place your thesis statement at the beginning of your outline. This statement should encapsulate the central argument of your paper and guide the development of your main points.
• List Main Topics with Roman Numerals : Use Roman numerals (I, II, III, etc.) to denote major sections or main topics that support your thesis. These represent the backbone of your research paper.
• Add Supporting Ideas : Under each main topic, list the supporting ideas or arguments using uppercase letters (A, B, C, etc.). These should include key evidence, arguments, or points that back up each main topic.
• Further Breakdown with Details : For more detailed structuring, sub-divide each supporting idea with Arabic numerals (1, 2, 3, etc.), followed by lowercase letters (a, b, c, etc.) if needed. This helps in organizing finer details and data which support your arguments.

Outline Formats and Their Uses

• Alphanumeric Outline : This is the most commonly used format, where main topics are listed as Roman numerals, subtopics as capital letters, specific points as Arabic numerals, and further details as lowercase letters.
• Full-Sentence Outline : Similar to the alphanumeric, but each point is written as a full sentence, providing a clearer picture of what each section will discuss.
• Decimal Outline : More precise, using a system of numbers that includes decimal points to reflect the hierarchy of information, suitable for complex papers with multiple layers of subtopics.

Organizing and Refining Your Outline

• Organize Logically : Arrange the main topics and subtopics in a logical order that makes sense for the presentation of your argument. Ensure there is a natural flow from one section to the next.
• Balance Your Sections : Try to ensure that each section of your outline has roughly the same amount of information. This balance will contribute to a more coherent and evenly developed paper.
• Review and Revise : After your initial outline is complete, go back to refine and rearrange sections as necessary. Add more details or remove redundant information to maintain focus and clarity.

By following these steps, you can create a comprehensive and effective outline that will serve as a reliable roadmap for writing your research paper. This structured approach not only helps in organizing your thoughts but also ensures that all critical points and supporting evidence are included before you begin the actual writing process.

Drafting the Research Paper

Writing the first draft.

• Begin with Clarity : Open your draft with the thesis statement, ensuring it's clear and assertive. Follow up with secondary information that sets the stage for your argument.
• Organize Your Thoughts : Structure the body of your research paper by organizing the information logically. Use topic sentences at the beginning of each paragraph to guide the reader through your arguments, ensuring each one supports your thesis.
• Smooth Transitions : Ensure that transitions between paragraphs and sections are smooth, maintaining the flow of thoughts and enhancing readability.

Revising the Draft

• Alignment with Vision : Compare your first draft with the initial outline and make necessary adjustments to better align with your research goals.
• Enhance Content Quality : Check each paragraph against your thesis and introduction. Make sure the content supports the topic sentences and contributes to your overall argument.
• Proofread and Edit : Look for typos, grammatical errors, and cut unnecessary words. Ensure consistency in heading formatting, spellings, and citation styles.

Finalizing Your Research Paper

• Critical Review : Reassess the organization and logical flow of your paper. Confirm that every section, from the introduction to the conclusion, cohesively supports and builds upon your thesis.
• Feedback and Revision : Seek feedback from peers or mentors and be open to making revisions. This can provide new insights and help refine your paper to better meet academic standards.
• Final Touches : Ensure that the final draft is well-formatted according to assignment requirements, including a cover page and a works cited page. This not only enhances readability but also demonstrates professionalism and attention to detail.

The emphasis on careful planning, thorough research, and meticulous organization underlines the essence of producing a scholarly paper that not only meets academic standards but also contributes meaningful insights to the chosen field of study. It reinforces that writing a research paper is a systematic process that, when executed with dedication and focus, can turn into an intellectually rewarding endeavor.

In closing, the guide underscores the vital role of persistence and attention to detail in achieving a well-written research paper. The various stages outlined, from understanding the assignment to finalizing the paper, serve as a roadmap for students and researchers alike, guiding them towards clarity, coherence, and scholarly integrity in their academic writing. Embracing these guiding principles promises not only academic success but also the cultivation of skills essential for lifelong learning and inquiry.

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Business Research Methods Notes, PDF I MBA 2024

• Post last modified: 5 April 2022
• Reading time: 11 mins read
• Post category: MBA Study Material

Download Business Research Methods Notes, PDF, Books, Syllabus for MBA 2024. We provide complete business research methods pdf. Business Research Methods study material includes business research methods notes, book , courses, case study, syllabus, question paper, MCQ, questions and answers and available in business research methods pdf form.

Business Research Methods subject is included in MBA so students are able to download business research methods notes for MBA 1st year and business research methods notes for MBA 2nd semester.

Table of Content

• 1 Business Research Methods Syllabus
• 2 Business Research Methods Notes PDF
• 3 Business Research Methods Notes
• 4 Business Research Methods Questions and Answers
• 5 Business Research Methods Question Paper
• 6 Business Research Methods Books

Business Research Methods Notes can be downloaded in business research methods pdf from the below article.

Business Research Methods Syllabus

A detailed business research methods syllabus as prescribed by various Universities and colleges in India are as under. You can download the syllabus in business research methods pdf form.

• Unit 1: Introduction Business Research: Definition-Types of Business Research. Scientific Investigation: The Language of Research: Concepts, Constructs, Definitions, Variables, Propositions and Hypotheses, Theory and Models. Technology and Business Research: Information needs of Business – Technologies used in Business Research: The Internet, E-mail, Browsers and Websites. Role of Business Research in Managerial Decisions Ethics in Business Research.
• Unit 2: The Research Process: Problem Identification: Broad Problem Area-Preliminary Data Gathering. Literature Survey, Online Data Bases Useful for Business Research, Hypothesis Development, Statement of Hypothesis, Procedure for Testing of Hypothesis. The Research Design: Types of Research Designs: Exploratory, Descriptive, Experimental Designs and Case Study, Measurement of Variables, Operational Definitions and Scales, Nominal and Ordinal Scales, Rating Scales, Ranking Scales, Reliability and Validity.
• Unit 3: Collection and Analysis of Data : Sources of Data-Primary Sources of Data, Secondary Sources of Data, Data Collection Methods, Interviews, Structured Interviews and Unstructured Interviews, Face to face and Telephone Interviews. Observational Surveys, Questionnaire Construction, Organizing Questions, Structured and Unstructured Questionnaires, Guidelines for Construction of Questionnaires.
• Unit 4: Data Analysis: An overview of Descriptive, Associational and Inferential, Statistical Measures.
• Unit 5: The Research Report: Research Reports, Components, The Title Page-Table of Contents, The Executive Summary, The Introductory Section, The Body of the Report, The Final Part of the Report, Acknowledgements, References, Appendix, Guidelines for Preparing a Good Research report Oral Presentation, Deciding on the Content, Visual Aids, The Presenter, The Presentation and Handling Questions.

Business Research Methods Notes PDF

Business research methods notes.

Business Research Methods Questions and Answers

If you have already studied the business research methods and notes , then it’s time to move ahead and go through previous year business research methods question papers .

• What is information? Discuss the type of information need to run the Business.
• Define the term ‘Research’, Enumerate the characteristics of research. Give a Comprehensive definition of research.
• What do you mean by scientific investigation and explain them in detail.
• Indicate the sources of research process. Enumerate the steps of the research process.
• Give the sources of research problem. How a problem is identified? Enumerate the criteria for the selection of a problem.
• How is a problem stated? Describe the various ways of defining a problem. Discuss characteristics of good problem and criteria for evaluating a problem.
• Define the term ‘Review of literature’, how is it different from traditional meaning? Enumerate the objectives and significance of review of literature.
• What do you mean by ‘Sample Design’? What points should be taken into consideration by a Researcher in developing a sample design for this research project.
• How would you differentiate between simple random sampling and complex random sampling Designs? Explain clearly giving examples.
• Why probability sampling is generally preferred in comparison to non-probability sampling? Explain the procedure of selecting a simple random sample.
• Explain the phrase ‘Analysis of Data’ or ‘Treatment of Data’. Indicate the need and importance of data analysis.
• Differentiate between descriptive statistical analysis and inferential statistical analysis.
• Distinguish between parametric statistics and non-parametric statistics. Indicate their uses in different types of data or researches.
• Indicate the basis for selecting a statistical technique in analyzing data for educational research.
• What do you understand by research report or thesis? Indicate its need and importance in the research work.

Business Research Methods Question Paper

If you have already studied the business research methods and notes , then it’s time to move ahead and go through previous year business research methods question and answers .

It will help you to understand the question paper pattern and type of business research methods question and answer asked in MBA 1st year business research methods exam. You can download the syllabus in business research methods pdf form.

Business Research Methods Books

Below is the list of business research methods books recommended by the top university in India.

• Green and Tull, Research Markets Decisions, PHI.
• Tull Donald and Hawkins De, Marketing Research, PHI.
• G.C.Beri, Marketing Research, Tata McGraw- Hill Publishers.
• Luck David and Rubin Ronal, Marketing Research, PHI.
• Naresh Malhotra, Marketing Research, Pearson Education. Green E. Paul, Tull S. Donald & Albaum, Gerald, Research for Marketing decisions, 6th Ed, PHI, 2006.

In the above article, a student can download business research methods notes for MBA 1st year and business research methods notes for MBA 2nd semester. Business Research Methods study material includes business research methods notes , business research methods books , business research methods syllabus , business research methods question paper , business research methods case study, business research methods questions and answers , business research methods courses in business research methods pdf form.

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Received : 25 January 2023

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DOI : https://doi.org/10.1038/s41586-024-07219-0

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Homelessness and the Persistence of Deprivation: Income, Employment, and Safety Net Participation

Homelessness is arguably the most extreme hardship associated with poverty in the United States, yet people experiencing homelessness are excluded from official poverty statistics and much of the extreme poverty literature. This paper provides the most detailed and accurate portrait to date of the level and persistence of material disadvantage faced by this population, including the first national estimates of income, employment, and safety net participation based on administrative data. Starting from the first large and nationally representative sample of adults recorded as sheltered and unsheltered homeless taken from the 2010 Census, we link restricted-use longitudinal tax records and administrative data on the Supplemental Nutrition Assistance Program (SNAP), Medicare, Medicaid, Disability Insurance (DI), Supplemental Security Income (SSI), veterans’ benefits, housing assistance, and mortality. Nearly half of these adults had formal employment in the year they were observed as homeless, and nearly all either worked or were reached by at least one safety net program. Nevertheless, their incomes remained low for the decade surrounding an observed period of homelessness, suggesting that homelessness tends to arise in the context of long-term, severe deprivation rather than large and sudden losses of income. People appear to experience homelessness because they are very poor despite being connected to the labor market and safety net, with low permanent incomes leaving them vulnerable to the loss of housing when met with even modest disruptions to life circumstances.

The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applies to this release, authorization number CBDRB-FY2022-CES005-015. We thank the U.S. Census Bureau for their support, as well as John Abowd, Mark Asiala, George Carter, James Christy, Dennis Culhane, Kevin Deardorff, Conor Dougherty, Ingrid Gould Ellen, Anne Fletcher, Katie Genadek, Tatiana Homonoff, Kristin Kerns, William Koerber, Margot Kushel, Larry Locklear, Tim Marshall, Brian McKenzie, Brendan O’Flaherty, James Pugh, Trudi Renwick, Annette Riorday, Nan Roman, William Snow, Eddie Thomas, Matthew Turner, and John Voorheis for providing feedback and answering questions. We also thank participants in seminars at Yale University (Labor/Public Economics Workshop), the University of Chicago (Demography Workshop), APPAM, NTA, NBER Labor Studies, IRS/Census (Income Measurement Workshop), and the Institute for Research on Poverty. Ilina Logani and Mandana Vakil provided excellent research assistance. We appreciate the financial support of the Alfred P. Sloan Foundation, the Russell Sage Foundation, the Charles Koch Foundation, the Menard Family Foundation, and the American Enterprise Institute. Wyse thanks the National Institute on Aging for their support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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