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Journal of Business Research 66 (2013) 2153–2162

Contents lists available at SciVerse ScienceDirect

Journal of Business Research

Using mixed methods designs in the Journal of Business Research, 1990–2010☆

Robert L. Harrison III ⁎ Marketing Department in the Haworth College of Business at Western Michigan University, 3169 Schneider Hall, United States

☆ The author thanks John Creswell in the Office of Q Research, University of Nebraska–Lincoln for his trainin this project, and James W. Gentry, Ann Veeck, and Timoth ⁎ Tel.: +1 269 387 5261; fax: +1 269 387 5710.

E-mail address: [email protected].

0148-2963/$ – see front matter © 2012 Elsevier Inc. All doi:10.1016/j.jbusres.2012.01.006

a b s t r a c t

a r t i c l e i n f o

Article history: Received 1 May 2011 Received in revised form 1 November 2011 Accepted 1 January 2012 Available online 10 February 2012

Keywords: Mixed methods research Multiple methods research Multimethod research Research methods Marketing research

The purpose of this article is to examine the uses of mixed method research designs published in the Journal of Business Research. This study involves a content analysis of 2072 articles published between 1990 and 2010 in the Journal of Business Research. Seventeen mixed method studies implemented data collection procedures se- quentially (68%), six implemented them concurrently (24%), and two combined both sequential and concurrent procedures (8%). On the whole, priority skews more toward quantitative strands with ten articles prioritizing quantitative data (40%), three articles prioritizing qualitative data (12%), and twelve articles prioritizing both equally (48%). Business scholars recognize the benefit of mixing qualitative and quantitative research; however, as a discipline, we are not demonstrating knowledge of the mixed method literature or procedures; none of the articles recognized or mentioned knowledge of mixed method procedures or cited mixed method research. This study provides guidance for researchers in identifying design types appropriate for various research objectives as well as the models of different design types appearing in the Journal of Business Research.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

For decades, scholars in the social sciences have made use of mixed method research—that is, combining both qualitative and quantitative data in a single study. However, despite the call for its use in business research (Currall & Towler, 2003; Edmondson & McManus, 2007; Woodside, 2004, 2010), discussion about this distinct methodological approach by business scholars is scarce. While the mixing of qualitative and quantitative data is not new to business scholars, the use of mixed methods principles and design types is. Mixed methods researchers have suggested a need for understanding these principles and distin- guishing between studies that utilize the two types of data without se- rious integration and studies that “mix” the data sets effectively (Tashakkori & Creswell, 2007). That is to say, a study that includes both data types without integration is merely a collection of methods. Strong mixed methods studies, however, address the decision of how to integrate the data as well as timing and priority (Creswell & Plano Clark, 2011). Thus, there is a need for guidance in conducting mixed method research and for assessing the rigor of data collection and anal- ysis of both data types in business research. This study highlights these issues, regarding the tenets of mixed methods research and the use of mixed methods design types.

The mixing of research methods has been given many names includ- ing multiple methods, blended research, multimethod, triangulated

ualitative and Mixed Method g, support, and guidance with y M. Reilly.

rights reserved.

studies, and mixed research. In business, “multimethod” and “mixed method” research are the most commonly used labels. In the Handbook of Mixed Methods research, distinctions are made between these two terms (Morse, 2003). That is, multimethod research involves multiple types of qualitative inquiry (e.g. interviews and observations) or multi- ple types of quantitative inquiry (e.g. surveys and experiments) and (2) mixed methods which involve the mixing of the two types of data. Mixed methods research has become the most popular term for mixing qualitative and quantitative data in a single study (Johnson, Onwuegbuzie, & Turner, 2007) and the definition below, based on an analysis of definitions used by leaders in the field of mixed methods research, is used henceforth.

Mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quanti- tative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purpose of breadth and depth of understanding and corrobo- ration (Johnson et al., 2007, pp. 123).

To be clear, this study specifically investigates the use of both qualita- tive and quantitative components in a single study or project, (i.e. mixed methods) and not multiple methods that can include two different quan- titative component types or two different qualitative component types.

In addition to definitional issues, scholars expressed concern in the 1980s about the mixing of quantitative and qualitative data without ar- ticulating defensible reasons for doing so (Greene, Caracelli, & Graham, 1989), resulting in the development of a number of rationales for com- bining data collection methods and research questions particular to dif- ferent mixed method research designs. Bryman (2006) identifies 16

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rationales for conducting mixed method research, to which Harrison and Reilly (2011) add the appropriate mixed method design type for each rationale (see Table 1). Table 1 displays the use of different design types and descriptions of each design type and prescribed recommen- dations for employing each type are outlined in more detail in the find- ings section.

The existing marketing and management literature has taken the be- ginning steps towards understanding this methodological approach by first discussing the philosophical assumptions of such research (Bahl & Milne, 2006; Rocco, Bliss, Gallagher, & Pérez-Prado, 2003), and identify- ing trends as far as the numbers of studies employing the approach (e.g. Hanson & Grimmer, 2007; Hurmerinta-Peltomäki & Nummela, 2006). Bahl and Milne (2006) highlight the philosophical assumptions that guide qualitative, quantitative, and mixed method research approaches. That is, traditional assumptions guiding post-positivist research mandate an objective view of reality, in which research is aimed to measure or ex- plain, creating knowledge that is generalizable across different people, time, and place. Traditional assumptions guiding interpretive research assume the existence of socially-constructed, multiple realities and focus on understanding behavior rather than predicting it. Paradigmati- cally, mixed method research is linked to pragmatism as a system of phi- losophy (see Johnson & Onwuegbuzie, 2004 for a full description). The logic of pragmatic inquiry includes the use of induction (or discovery of patterns), deduction (testing of theories and hypotheses), and abduction (uncovering and relying on the best of a set of explanations for under- standing one's results) (Johnson & Onwuegbuzie, 2004; Morgan, 2007). Further, the basic pragmatic maxim translated to mixed methods re- search is to choose the mixture of methods and procedures that work best for answering research questions (Johnson & Onwuegbuzie, 2004).

Hanson and Grimmer (2007) and Hurmerinta-Peltomäki and Nummela (2006) consider the use of mixed methods research by busi- ness scholars, in terms of sheer number of studies. Molina-Azorin (2011) adds to the methodological conversation by discussing preva- lence rate and characteristics of mixed methods management research. What remains unclear is the use of mixed method designs by business scholars; that is, what types of mixed methods designs are being used and how are they being used in business research? Harrison and

Table 1 Rationale for mixed methods research and design types.

Rationalea Descriptiona

Triangulation Quantitative and qualitative combined to triangula corroborated.

Offset Combining strands offsets their weaknesses to dra Completeness Bringing together a more comprehensive account

research is employed. Process Quantitative provides an account of structures in s

sense of process. Different Research Questions Quantitative and qualitative each answers differen Explanation One is used to help explain findings generated by Unexpected Results When one strand generates surprising results that

the other. Instrument Development Qualitative is employed to develop questionnaire a Sampling One approach is used to facilitate the sampling of Credibility Employing both approaches enhances the integrity Context Qualitative providing contextual understanding co

externally valid findings or broad relationships am through a survey.

Illustration Qualitative to illustrate quantitative findings (putt quantitative findings).

Utility Among articles with an applied focus, the combini more useful to practitioners and others.

Confirm and Discover This entails using qualitative data to generate hypo research to test them within a single project.

Diversity of View Combining researchers' and participants' perspecti qualitative research respectively, and uncovering r through quantitative research while also revealing participants through qualitative research.

a From Bryman (2006). b From Harrison and Reilly (2011).

Reilly (2011) take a step in answering this question by updating the re- cent trends in the use of mixed methods research and identifying trends in terms of the types of mixed methods designs being employed in mar- keting research. The present study extends the discussion to the Journal of Business Research audience, posing similar questions. What types of mixed method designs appear in the Journal of Business Research? How do trends in JBR compare to the marketing journals previously ex- amined? How are scholars incorporating mixed methods techniques to achieve business research objectives?

While growing, the relative use of mixed methods research is com- paratively scarce in business disciplines. The general absence of mixed method research designs may be due to a number of factors including the historical precedent of favoring quantitative research in business (Hunt, 1994), the general lack of attention to interpretative methods in graduate education and training, and the difficulty in learning both qualitative and quantitative approaches. Another explanation might be the tendency for scholars to be “guilty of the ‘law of the instru- ment’—one uses the tool one has even if in context the tool's use is highly inappropriate” (Woodside, 2010, p. 66). Weick (1996) recog- nizes difficulties in learning to drop one's tools and pick-up tools more useful to the task-at-hand, such as maintenance of the status quo with regard to methodological preferences in certain disciplines.

In sum, scholars must be able to assess the appropriateness of the dif- ferent mixed method design choices and anticipate challenges with each choice. The study here addresses this issue by providing an overview of mixed method design types and an examination of how these designs are successfully in-use in the pages of the Journal of Business Research. This study provides a guide for future researchers conducting mixed method research.

To provide business scholars a resource for which to guide those in- terested in mixing qualitative and quantitative data, published business research will be evaluated on four tenets of mixed methods research, particularly addressing the use of two strands of data (i.e., qualitative and quantitative), the timing (i.e., sequential or concurrent), the priority given to each data type, and the integration (or mixing) of the data. In ad- dition, recommendations will be made for researchers interested in employing different mixed method design types.

Design Typeb

te findings to be mutually Convergent

w on the strengths of both. Convergent if both quantitative and qualitative Exploratory, Explanatory, or Convergent

ocial life but qualitative provides Exploratory or Explanatory

t research questions. Convergent the other. Explanatory can be understood by employing Explanatory, or Embedded

nd scale items. Exploratory respondents or cases. Exploratory or Explanatory of findings. Exploratory, Explanatory, or Convergent

upled with either generalizable, ong variables uncovered

Exploratory or Explanatory

ing ‘meat on the bones’ of ‘dry’ Explanatory

ng the two approaches will be Exploratory, Explanatory, Convergent, or Embedded

theses and using quantitative Exploratory

ves through quantitative and elationships between variables meanings among research

Convergent or Embedded

Table 2 Mixed method scoring scheme.

X Quantitative rigor X Qualitative rigor

Use of theory Emerging design+type of design mentioned (e.g., case study, ethnography)

Good sampling procedure Good sampling procedure Good sample size Good sample size Identification of instruments Questions for data collection Validity and reliability of scores Procedures for data collection Detailed procedures of data collection Coding/thematic development Level of sophistication of data analysis Advanced level of use themes

(e.g., taxonomy, chronology)

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The paper is structured as follows. Section 2 discusses the ratio- nales for conducting mixed methods research. Section 3 then pre- sents a content analysis of the mixed method research including outlines of the four major mixed method designs and recommenda- tions for when each design is best employed. The paper concludes with a comparison of our findings with a previous study, a discussion of the methodological implications, and a summary of recommenda- tions for employing mixed methods designs.

2. Content analysis and data collection procedures

A study in Qualitative Market Research reviews mixed method arti- cles across nine major journals from 2003 to 2009 (Harrison & Reilly, 2011). This study may be the first systematic evaluation of the mixed method designs in the marketing discipline. The present study is a follow-up to those findings, adding and comparing those results to re- search published in the Journal of Business Research—a multidisciplinary business journal. More specifically, the author will conduct a content analysis of mixed methods designs used in JBR from 1990 to 2010.

Content analysis is an observational technique which allows for a systematic evaluation of recorded communications (Kolbe and Burnett, 1991). In the current investigation the recorded communica- tions of interest is the Journal of Business Research. For the purpose of coding, articles are required to contain qualitative research centered on primary data collection in non-numerical form (words, images,

Table 3 Design features of mixed methods studies published in JBR (N=25).

Study Design Variant

Thakor et al. (2004) Sequential exploratory Instrume Guenzi and Troilo (2007) Sequential exploratory Instrume Piercy et al. (2002) Sequential exploratory Theory d Frazer and Winzar (2005) Sequential exploratory Theory d Sargeant et al. (2006) Sequential exploratory Theory d Rosenbaum and Montoya (2007) Sequential exploratory Theory d Ridgway et al. (2008) Sequential exploratory Theory d Wu et al. (2010) Sequential exploratory Theory d Barczak et al. (1997) Sequential exploratory Instrume Gruner and Homburg (2000) Sequential exploratory Instrume Bruhn et al. (2008) Sequential exploratory Instrume Gomez and Ranft (2003) Concurrent convergent Parallel d Adobor (2005) Concurrent convergent Parallel d Schelfhaudt and Crittenden (2005) Concurrent convergent Parallel d Luomala (2007) Concurrent convergent Parallel d Money (2004) Embedded Embedd Fong and Burton (2008) Embedded Embedd Chan and Li (2010) Embedded Embedd Lai and Cheng (2005) Sequential explanatory Follow-u Vandecasteele and Geuens (2009) Sequential explanatory Follow-u O'Connor et al. (2001) Hybrid Instrume LaTour et al. (2010) Hybrid Parallel d Koerner (1996, 2000)a Sequential exploratory Instrume Doherty et al. (1999, 2003)a Sequential exploratory Instrume Gould et al. (1993, 1997)a Sequential exploratory Instrume

a Indicate multiple publication mixed method projects.

symbols, etc.) and quantitative research centered on data collection in numerical form (Harrison & Reilly, 2011). To identify mixed method re- search studies, the author uses a coding scheme developed by the founding editor of the Journal of Mixed Methods Research, who used these criteria when judging article fit with that journal (see Table 2).

Consistent with this scheme and other (Bryman, 2006; Harrison & Reilly, 2011), studies that describe the use of both qualitative and quantitative data, but either report just the quantitative or qualitative data, are not included in the study. Further, the mere use of inter- views during data collection did not result in a mixed method distinc- tion, as reporting rigorous analysis of both data types source is required. Also as part of our inclusion criteria, content analyses or studies that quantified qualitative data are coded as quantitative if the results of the content analysis are reported numerically (Harrison & Reilly, 2011), and were excluded. The process of quanti- fying qualitative data (also called “quantitizing”), and the transform- ing of quantitative data in qualitative data (also called “qualitizing”) are the topic of debate among mixed methods scholars in terms of mixed method distinction and to prevent obscuring the central focus of the paper these studies were omitted.

To identify mixed method studies the author manually searched all 2072 articles published in the Journal of Business Research from 1990 to 2010 and from this search 106 articles were identified that mention the use of both qualitative and quantitative data. These arti- cles were then limited to include studies that report both qualitative and quantitative results, rather than merely mentioning that such data were collected, per our inclusion criterion. From this search, 25 articles met the definition of mixed methods research and are includ- ed in the purposive sample. These articles were then sorted to identi- fy examples of research employing different mixed method design types (presented in Table 3).

The design features of each study, including the priority given to the different data strands, are highlighted in Table 3. A procedural no- tation system developed by Morse (1991; 2003) that uses plus (+) symbols and arrows (→), and capital and lowercase letters to repre- sent different mixed method procedures, is also featured in Table 3. A plus sign indicates that both data strands were collected concurrently and an arrow indicates that the data were collected sequentially. Capital letters are used to indicate higher priority for a particular method, with

Priority

nt development qual → QUAN nt development qual→ QUAN evelopment qual→ QUAN evelopment QUAL→ QUAN evelopment qual → QUAN evelopment QUAL → QUAN evelopment qual→ QUAN evelopment qual → QUAN nt and theory development QUAL → QUAN nt and theory development qual → QUAN nt and theory development QUAL → QUAN atabases QUAL+QUAN atabases QUAL+QUAN atabases qual+quan atabases QUAL+QUAN

ed experiment QUAN (qual) ed methodology QUAL (quan) ed methodology QUAL (QUAN) p explanations QUAN→ qual p explanations QUAN → qual nt development and parallel databases (qual → quan) → (QUAL+quan) atabases and theory development (QUAL+quan) → (qual → quan) nt and theory development QUAL → QUAN nt and theory development QUAL → QUAN nt and theory development QUAL → QUAN

Table 4 Research approach rationales and design plan structure.

When to employ a mixed method approach…a Rationale description a Design plan b

When one data source may be insufficient One data type may not tell complete story. Post-hoc Researchers lack confidence that one data type addresses the research question. Predetermined Results from qualitative and quantitative results are contradictory, which could not be known collecting one data type.

Post-hoc

Type of evidence from one level of an organization (or population) may differ from other levels, requiring different perspectives (i.e. generalizability at one level and in-depth understanding at another)

Predetermined

When a need exists to explain initial results Second database needed to explain primary results. Predetermined or post-hoc When a need exists to generalize exploratory findings Researchers seeking generalizability do not know the questions needed to be asks,

the variables or constructs they need to measure, or theories that may guide the study Predetermined or post-hoc

When a study needs enhancement by a second method Provide in-depth understanding to phase in overall quantitative design (e.g. experiment or correlation)

Predetermined or post-hoc

Provide quantifiable results enhance phase in overall qualitative design (e.g. ethnography or case study)

Predetermined or post-hoc

a From Creswell and Plano Clark (2011). b Added classification.

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lowercase indicating lower priority. For example, QUAL → quan indi- cates a sequential study where qualitative data were collected prior to the quantitative data, and that top priority is given to the qualitative data. QUAL+QUAN indicates that both data strands were collected at the same time and were given equal priority.

Research questions best suited for mixed method inquiry include those in which one data source may be insufficient, results need to be explained, exploratory findings need to be generalized, a second meth- od is needed to enhance a primary method, and an overall objective can be best addressed with multiple phases or projects. These research de- sign concerns may emerge post-hoc, i.e. during the research process, or as a predetermined design (see Table 4). Further, the decision to employ a mixed method design may include salvaging intelligible results from the primary data strand, or adding a data strand at the behest of re- viewers. While one can speculate as to whether or not a systematic de- sign is employed prior to data collection, a predetermined design plan forces researchers to consider what can be learned by mixing data strands, improves an author's rhetoric as to why specific designs are used to address research problems, and reduces the overall time and resources expended in a post-hoc projects (Tables 5).

3. Findings

The timing, or sequence, in mixed method designs refers to the order in which researchers use the two data strands. Timing procedures vary by design type, with convergent designs employing concurrent data collection procedures, explanatory and exploratory designs employing sequential data collection, and embedded designs

Table 5 Major mixed methods design types.

Design type Variants Timing Weighting

Convergent • Parallel database Concurrent: quantitative and qualitative at the same time

Usually eq

Embedded • Embedded experimental

Concurrent or sequential Unequal

• Embedded correlation

♦ Embedded methodology

Explanatory • Follow-up explanations

Sequential: quantitative followed by a qualitative

Usually quantitati

Exploratory • Instrument development

Sequential: qualitative followed by a quantitative

Usually qualitativ

• Theory development

From Creswell and Plano Clark (2007, 2011). ♦ Added variant from Harrison and Reilly (2011).

employing either sequential or concurrent data collection procedures. In this study, sixteen mixed method studies implement data collection procedures sequentially (68%), four implement them concurrently (24%), and two combine both sequential and concurrent procedures (8%). On the whole, priority is skewed more toward quantitative strands, with ten articles prioritizing quantitative data (40%), three arti- cles prioritizing qualitative data (12%), and twelve articles prioritizing both equally (48%). This is not surprising as it has been suggested that most articles employing mixed methods in business literature have a positivist orientation (Currall & Towler, 2003).

Five mixed methods design types appear in the Journal of Business Research during the designated time period. These design types in- clude exploratory designs, explanatory designs, embedded designs, convergent designs, and hybrid designs.

3.1. Exploratory designs

In exploratory designs, researchers first collect qualitative data, analyze the qualitative data, and then build on the qualitative data for the quantitative follow-up. The building process can involve iden- tifying the types of questions that might be asked, determining the items/variables/scales for instrument design, and generating theories, typologies, or classifications (Creswell & Plano Clark, 2011). Explor- atory designs are the most common type of design used (56%, n=14). Exploratory designs are useful for exploring relationships when study variables are unknown; developing new instruments, based on initial qualitative analysis; generalizing qualitative findings; and refining or testing a developing theory (Creswell & Plano Clark,

Mixing Notation

ual Merging the data during the interpretation or analysis QUAN+QUAL

Embed one type of data within a larger design using the other type of data

QUAN (qual) or QUAL (quan)

ve Connect the data between the two phases QUAN → qual

e Connect the data between the two phases QUAL → quan

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2011). The two common variants of this design type are the instru- ment design model and the theory development model. In instru- ment development designs, qualitative findings are used to develop scale items for a quantitative survey instrument. In this variant, the qualitative data plays a secondary role (Creswell & Plano Clark, 2011). In theory development designs, qualitative results play a more primary role and are used to develop hypotheses or proposi- tions, or taxonomies (or classifications systems), and the secondary, quantitative phase tests or develops the emergent theory, or tests or studies the quantitative results in more detail (Creswell & Plano Clark, 2011).

Two studies employ the instrument development variant, both of which use interview data to generate specific items for the scale devel- opment process (see Guenzi & Troilo, 2007; Thakor, Borsuk, & Kalamas, 2004). In addition, each article also uses the qualitative data to validate propositions, or constructs, which developed from the literature. This process of proposition, or hypothesis, validation represents a sub- category of the theory development variant. Whereas the studies highlighted in the theory development section above use qualitative data to generate propositions, these two studies use qualitative data to validate propositions. Thus, the theory development variant can re- flect exploratory or validating qualitative goals, respectively.

Though JBR scholars rarely discuss rationales for mixed method procedures, Guenzi and Troilo (2007, pp. 101) describe explicitly their reasons for using both types of data:

Due to the lack of robust conceptual and empirical foundations for the topic under investigation, this study started with a qualitative step toward the goal to gain better insights…before running the quantitative survey.

As expected, the qualitative phase, in both studies, played a sec- ondary role in the development of a web-based survey (Thakor et al., 2004) and a traditional questionnaire (Guenzi & Troilo, 2007).

Six studies employ the theory development variant; however, var- iability exists as to how the authors develop theory. For example, Piercy, Harris, and Lane (2002) develop a qualitative case study in order to develop propositions. Rosenbaum and Montoya (2007) and Ridgway, Kukar-Kinney, Monroe, and Chamberlin (2008) both use thematic analysis of the qualitative data to generate propositions and hypotheses, respectively. Frazer and Winzar (2005) use qualita- tive data to develop a causal model and hypotheses that they then generalize and test with a survey. Wu, Steward, and Hartley (2010) use qualitative data to develop a taxonomy and use survey data to test hypothesized relationships among taxonomic classifications. Sargeant, Ford, and West (2006) develop two models, one from ex- tant literature and one from focus groups. The two models are com- pared via structural equation modeling to determine which model has the best fit. While the aim of all of the studies is to use the qual- itative data to develop theory in the form of theoretical models, prop- ositions, or hypotheses, in four of the six studies the qualitative data does not play a primary role.

Three studies incorporate both theory development and item gener- ation in their sequential exploratory designs; however, each set of au- thors utilize different techniques for doing so. For example, in Bruhn, Georgi, and Hadwich's (2008) study aimed at developing a formative, second-order construct to measure customer equity management (CEM), the authors use qualitative data to (1) confirm a proposed defi- nition, developed from the literature, (2) develop items for a question- naire, and (3) unpack the CEM construct to uncover its underlying dimensions. In the Barczak, Ellen, and Pilling (1997) study, the qualita- tive section is separated into two stages. The first stage develops a ty- pology of consumer motivations and the second stage develops survey items for the identified consumer motivation groups. To gener- alize the qualitative results to a larger population, a questionnaire is deployed and analyzed. In the Gruner and Homburg (2000) study, the

qualitative data are used to develop measures for relevant constructs and to develop a model for which the quantitative stage would then test. Of these three studies, only the Gruner and Homburg study does not heavily weight the qualitative portion of the study.

As in the Harrison and Reilly (2011) study, the exploratory design is the dominant design type used by business scholars. However, in the JBR there is a much broader usage of this design type in terms of its functioning. That is, the Harrison and Reilly study was dominated by designs aimed at the development of surveys or scales. The studies identified in JBR offer more variety in purposes, including creation of indices, taxonomies, typologies, and model exploration. While the mixed method literature suggests that sequential exploratory designs most often incorporate a single variant type (i.e. either theory devel- opment or instrument development) (Creswell & Plano Clark, 2011), JBR scholars are incorporating equal-weighted multi-variant sequen- tial exploratory designs expressed by studies aimed at both theory and instrument development.

Of the articles excluded from this study, most do not fully develop or analyze the qualitative strand of the study. The benefit of mixing meth- odologies is utilizing the strengths of each approach. Many of the stud- ies merely discuss conducting interviews to develop scales. The strength of qualitative research is the depth of knowledge gained from analyzing the experience of participants. Interviewing respon- dents only indicates a surface level of understanding a phenomenon. The multi-layered analytic approach used in qualitative methodologies allows for a much deeper understanding of a phenomenon and in turn should lead to the development of more specific and focused questions to be asked in the quantitative follow-up phase.

The exemplars of exploratory designs found in this review lead to some general recommendations when employing exploratory de- signs. Exploratory designs are most effectively utilized under follow- ing conditions: when research questions are more qualitatively oriented (i.e. discovering patterns and themes), when researchers do not know what constructs are important to study, and relevant quantitative instruments are not available, and when researchers identify new emergent research questions based on qualitative re- sults that cannot be answered with qualitative data (Creswell & Plano Clark, 2011).

3.2. Explanatory designs

In explanatory designs, researchers first collect and analyze quan- titative data, then build on those findings in a qualitative follow up, which seeks to provide a better understanding of the quantitative re- sults. Building can involve either using the quantitative data to select cases or to identify questions that need further explorations in the qualitative phase (Creswell et al., 2003). In explanatory designs, unequal priority is given to the quantitative data and as the label implies, the explanatory design is useful in explaining relationships or study findings. Explanatory designs are most often conducted when qualitative data are needed to help explain or build on initial quantitative data. Two variants of explanatory designs include follow-up explanations and participant selection models (Creswell, Plano Clark, Gutmann, & Hanson, 2003). In follow-up explanation models, specific qualitative results are used to explain or expand on quantitative results. For example, statistical differences among groups, individuals who scored at extreme levels, or unexpected results are explored qualitatively. A research question employing this design-type might read, “How do qualitative data explain the quantitative results?” In both exploratory and explanatory designs the data sets, or strands, are usually connected, or mixed, during the interpretation stage and in the discussion section. Two articles were found that employed explanatory designs (8%), both incorporating the follow-up explanation variant.

Vandecasteele and Geuens (2009) use an explanatory design to investigate whether homosexuals differ from heterosexuals in terms

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of consumer innovativeness. The results of their MANOVA, using sur- vey data suggests that gays and lesbians react completely different as compared to the heterosexual counterparts. “To find a plausible ex- planation for remarkable results…and to better understand the re- sults, three focus group discussions were organized and conducted” (Vandecasteele & Geuens, 2009, pp. 139). Illustrative quotes from focus group data highlight the existence of an important subcultural influence, that of family situation, as a viable explanation for the quantitative results.

Using survey data from organizations, Lai and Cheng (2005) test a structural equation model examining hypothesized relationships be- tween quality orientation, market orientation, and organizational performance. The quantitative results are then supplemented with qualitative data collected from in-depth interviews with selected re- spondent organizations. Four qualitative case studies are developed and, while the authors do not provide extensive individual qualitative case study findings, they present summaries of a cross-case analysis, designed to “enhance the output of the survey research…by provid- ing [additional] support for the findings” (Lai & Cheng, 2005, pp. 452).

As expected, in both studies the quantitative strand plays a prima- ry role and the qualitative strand is secondary. An overall trend in the business literature is an increasing tendency to question the validity of surveys on account of concerns with subjective collection and in- terpretation of data. The explanatory design is an alternative that may potentially address these concerns. The strength in survey research is to uncover generalizable trends in specific populations, and qualitative follow-up can be used to address gaps that result from unique findings (Harrison & Reilly, 2011). Thus, the explanatory design has the potential to illuminate the strengths of survey data.

The exemplars of explanatory designs found in this review lead to some general recommendations that might be followed when employing exploratory designs. Explanatory designs are most effec- tively utilized under following conditions: when research questions are more quantitatively oriented (i.e. testing theory/hypotheses), when researchers have the ability to return to participants for a sec- ond round of qualitative data collection, or when researchers develop new questions based on quantitative results, that cannot be answered with quantitative data (Creswell & Plano Clark, 2011).

3.3. Embedded designs

In embedded designs, researchers collect both quantitative and qualitative data either sequentially or concurrently with one form of data playing a supporting role, or both forms of data playing a sup- porting role in a larger design (Creswell & Plano Clark, 2011). In the former design, the qualitative data may play a supporting role within an experiment or correlational study. In the latter, the qualitative and quantitative data may play a supportive role in a case study, ethnog- raphy, narrative, or other qualitative research design. This latter variant is identified as embedded methodology in Table 1. A key ele- ment in identifying embedded designs is whether the secondary data type is playing a supplemental role; that is, would the results of the secondary data type be meaningful if it were not embedded within the other data (Creswell & Plano Clark, 2007)? Embedded designs are most often conducted when there are different questions requir- ing different data. For example, a research question employing this design-type might read, “How do the qualitative findings enhance the interpretation of the experiments, or correlational outcomes?” Three studies employ embedded designs (12%). Two embedded design examples are embedded methodology studies and one is an embedded experimental model.

With roots in the ethnographic methodology, netnography is a qualitative methodology that adapts ethnography to the internet en- vironment (Kozinets, 2002). Fong and Burton (2008) and Chan and Li (2010) both employ an embedded method design in a study that em- ploys netnography as the overarching qualitative design and included

embedded quantitative components in the analysis. In both studies, the data from both strands were collected simultaneously, then ana- lyzed and mixed in the results section. The difference between the two studies is that the Chan and Li study offers somewhat equal weighting to both data strands and thus could be considered a con- current convergent design (however, the study appears to be slightly netnographically-dominant), whereas Fong and Burton (2008) have a much more distinctive qualitative balance. Ethnography has a history of including a variety of data collection methods including surveys (Wolcott, 1999); thus, netnography is a design ripe for incorporation into a mixed method approach.

Money (2004) employs an embedded experimental design by using qualitative data to explore aspects of the phenomenon to deter- mine participant selection for the primary experiment. The qualita- tive data was presented and mixed in the “results and discussion” section.

The exemplars of embedded designs found in this review lead to some general recommendations that might be followed when employing exploratory designs. Embedded designs are most effective when utilized under the following conditions: when researchers have little prior experience with the supplemental method, or when re- searchers do not have adequate resources to place equal priority on both types of data (Creswell & Plano Clark, 2011).

3.4. Convergent designs

In convergent designs, researchers collect both qualitative and quantitative data simultaneously, analyze both data strands separate- ly, and then mix the databases by merging the data (Creswell & Plano Clark, 2011). Four articles were found employing concurrent designs (12%). Convergent designs are most often conducted to bring togeth- er the strengths of both data strands to compare results or to validate, confirm, or corroborate quantitative results with qualitative findings. In convergence design models, qualitative and quantitative data are analyzed separately and the different results are integrated during the interpretation (Creswell et al., 2003). A research question employing this design-type might read, “To what extent do the qual- itative results confirm the quantitative results?”

Two studies employ concurrent convergent designs asking similar questions qualitatively and quantitatively, but obtain different perspec- tives from different sample populations. For example, Gomez and Ranft (2003) conduct qualitative interviews with top managers and human resources managers, while the quantitative questionnaire is distributed to employees. Schelfhaudt and Crittenden (2005) also use this ap- proach, collecting qualitative data from business professionals and business school faculty members and quantitative data from business school students. The “findings from the interviews and the survey data were separated into three areas based upon the sample source and [were] discussed independently” (Schelfhaudt & Crittenden, 2005, pp. 950). The findings are then mixed in the conclusion section.

The other convergent design studies ask different, but related re- search questions in each data strand to the same population. For ex- ample Adobor (2005), describes the qualitative study as providing a deeper understanding of different aspects of the phenomenon than the quantitative study that is employed to test research hypotheses. The data in this study is collected simultaneously and the results for each data strand are presented separately and mixed in the research implications section. Similarly, Luomala (2007) conducts both focus groups and an experiment to investigate meanings and behaviors, respectively, from the same sample population. In discussing the research methodology, the author describes what amounts to a textbook rationale for conducting a convergent design:

This study contains two separate but related studies: the first is on the creation in the minds of consumers of meanings related to the origin of food, and the second is on the effects of food as a

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determinant of consumers’ actual food choices. This combines both qualitative and quantitative methodology in order to investigate dif- ferent aspects of the same phenomenon (Luomala, 2007, pp. 128).

The results of each data strand is presented separately and mixed in the discussion section.

The nature of the convergent design, with its equal weighting, lends itself to rigorous collection and analysis in both data strands. Unlike the previous study (Harrison & Reilly, 2011), wherein convergent designs often contained weak qualitative sections, or unequal balance, most all of the convergent studies in the JBR provide equal weighting.

The exemplars of convergent designs found in this review lead to some general recommendations that might be followed when employ- ing convergent designs. Convergent designs are most effectively utilized under following conditions: when researchers have limited time for col- lecting data and must collect both types of data in one visit to the field, when researchers feel that there is equal value for collecting and analyz- ing both quantitative and qualitative data to understand the problem, or when researchers have skills in both quantitative and qualitative methods of research and can manage extensive data collection and anal- ysis activities (Creswell & Plano Clark, 2011).

3.5. Hybrid design

Two studies incorporate aspects of multiple design types (i.e. hybrid designs (8%)). LaTour, LaTour, and Zinkhan (2010) conduct what amounts to a concurrent convergent design, with a sequential explor- atory follow-up. The authors collect both qualitative “memory stories” and quantitative survey data from 50 participants. Both data strands are presented separately and mixed in a discussion section. The authors then conduct a two-phase follow-up to investigate whether the memo- ry study method is “better” than more traditional methods (i.e., focus groups). Thus, a focus group is conducted and a summary of the results are provided. A survey (n=106) is then conducted to “determine what experiences brought back the most memories…those surfaced in the childhood memory session…the focus group…or recent advertising ef- forts” (LaTour et al., 2010, p. 334). The results for all forms and phases are presented separately and mixed in their entirety in another discus- sion section.

O'Connor, Luo, and Lee (2001) conduct what amounts to a sequen- tial exploratory-embedded design. The first phase of data collection includes qualitative interviews, specifically collected to assist in de- veloping a survey. The survey is then administered to participants; however, embedded in the survey development process are post- survey interviews, designed to provide construct validity. Both strands of data are presented separately; however, no clear mixing discussion appears.

3.6. Multiple publications

Identifying mixed method studies is complicated by mixed methods projects published in multiple outlets—that is the qualitative or quantitative results are published in different journals or, alterna- tively, only one strand of the results is published. By requiring both data types be present, studies that employ mixed methods designs, but present findings in different journal outlets, may have been missed. Three multiple publication mixed method projects are identi- fied in our search. All three are sequential exploratory designs build- ing on published qualitative studies. In addition, in all of these publications, the authors include statements highlighting the multiple-phase, multiple-outlet strategy. For example, Doherty, Ellis-Chadwick, and Hart (2003, p. 888) frame their study as building upon their “previous exploratory, qualitative research that identified a number of critical factors affecting Internet adoption,” published in another journal (see Doherty, Ellis-Chadwick, & Hart, 1999).

Another example from Koerner (2000, p. 269) states, “In phase one, qualitative research methods were used to explore the conceptu- al domain of service quality and clarify the service quality dimensions used by patients to evaluate an inpatient nursing experience. The qualitative findings of the study have been reported in detail previ- ously (Koerner, 1996) and, therefore, are summarized only briefly here. In phase two of the study, scale development and testing, quan- titative methods were used to develop an instrument to measure the dimensions identified in phase one.”

Gould, Considine, and Oakes (1993) and Gould, Oakes, and Considine (1997) also conduct a mixed method project wherein the results are published in separate journal outlets. While these authors specifically discuss this issue of multiple publications, other authors using similar strategies without explicit identification could not be identified in the search.

4. Discussion

Until recently, the extent to which marketing research uses mixed methods designs was unknown (see Harrison & Reilly, 2011). This follow-up investigation of the delineation of the major forms in mixed method designs in the JBR extends that framework for looking at such design types to a broader business context, providing exam- ples of research designs that are substantially different than single strand studies. The framework also helps advance a common lan- guage for business scholars using mixed methods techniques. Bryman (2006) finds that mixed methods scholars have a difficult time identifying exemplary mixed methods research and an absence of “best practice” templates from which to draw upon when it comes to combining findings. This study addresses this issue by pro- viding guidance and direction for researchers to design mixed methods studies, offering recommendations and examples of re- search employing different design types. An understanding of mixed method design types equips researchers with knowledge to decide and choose the appropriate design to address particular research questions, provides a methodological foundation for which to con- duct mixed method research, and helps anticipate and resolve chal- lenging issues. This study finds that business scholars do not demonstrate much knowledge of the mixed method literature or pro- cedures, as none of the studies recognize or mention an understand- ing of mixed method procedures, or cite mixed method research. Further, having a mixed methods design plan improves an author's rhetoric as to why specific designs were used to address research problems and force them to consider what can be learned by using both qualitative and quantitative data. As mixed method research is a growing methodological approach in several disciplines (Creswell & Plano Clark, 2011), another goal of this study is also to explore how business scholars are incorporating mixed method techniques to best fit business research objectives. The study here examines the extent to which mixed method designs types are in use, and identifies what trends, or patterns, can be found through an analysis of studies published in the JBR. An evaluation of studies asking similar questions in other disciplines suggests that dominant mixed method design types vary across disciplines. For example, other disciplines have been found to use predominantly convergent designs (Hanson, Creswell, Plano Clark, Petska, & Creswell, 2005), or an evenly distrib- uted combination of the concurrent and sequential designs (Plano Clark, Huddleston-Casas, Churchill, O'Neil Green, & Garrett, 2008). In marketing, Harrison and Reilly (2011) found that an overwhelming majority of the design types employed were sequential designs (Harrison & Reilly, 2011). The present study adds evidence that the trend in business research is to use qualitative data to develop quan- titative follow-ups. Explanations for this trend could be the influence of commercial business research with its focus on preliminary focus groups that lead to “empirical” quantitative investigations and

Table 6 Journal comparisons by design type.

QMR Results (Nine marketing journalsa) (2003–2009b) JBR Results (1990–2010)

Design type # of studies Percentage Design type # of studies Percentage

Sequential exploratory 22 51.1 Sequential exploratory 14 56 Sequential explanatory 10 23.3 Sequential explanatory 2 8 Embedded 6 14 Embedded 3 12 Convergent parallel 4 9.3 Concurrent convergent 4 16 Hybrid 1 2.3 Hybrid 2 8 Total 43 100 Total 25 100

a Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research, Journal of the Academy of Marketing Science, Journal of Retailing, Journal of Consumer Psychology, Marketing Science, International Journal of Research in Marketing, and European Journal of Marketing.

b Corresponding with the publishing of the Handbook of Mixed Method Research (2003).

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textbooks that suggest that the purpose of qualitative research is to develop hypothesis that can then be quantified (Tables 6).

Another trend suggests that business scholars, particularly in mar- keting, are incorporating mixed methods techniques with traditional marketing approaches. For example, the CIT (Critical Incident Tech- nique) and netnography represent traditional qualitative approaches that were augmented in Journal of Business Research studies to fit re- search questions requiring a mixed method evaluation. Though root- ed in psychology, the CIT method was made popular in business (see Bitner, Booms, & Tetreault, 1990) as a method of collecting direct ob- servations of human behavior that have critical significance. Netno- graphy is a method used to explore and understand online behavior that was developed by business scholars (see Kozinets, 2002). Both of these methods represent popular, traditional business research techniques that are being augmented to better fit and answer re- search questions that require the mixing of data types. In addition, as sequential exploratory designs were found to be the dominant de- sign used by business scholars, it was not surprising to discovery aug- mented variants that extend the boundaries of the sequential exploratory approach in JBR. Specifically, scholars are using qualita- tive data to develop theory and generate survey items in the same study. In addition, scholars may use the qualitative data to validate propositions developed from the literature, or develop propositions from the data.

A previous study finds that marketing scholars may not be maxi- mizing the extent to which they are using mixed method research (Harrison & Reilly, 2011) and this follow-up investigation suggests that similar concerns extend to a broader interdisciplinary context. For example, several authors mention collecting interview data prior to a quantitative follow-up; however, the presentation and method of qualitative data analysis is often not used, undefined, or unreported. In addition, a few mixed method articles present weak quantitative strands (e.g., simple percentages) and, though we do not suggest that these are weak studies, they merely are not strong mixed methods studies. With that being said, a potential weakness of the inclusion criteria could also involve quantitative studies where- in rigorous qualitative research is conducted as, for example, part of a scale development process, yet not reported. It is important to note, however, that pretesting instruments with informal focus groups and interviews to “member check” quantitative results, is not a quality mixed method study, due to lack of rigorous qualitative analysis. For ex- ample, if a researcher is employing an explanatory design, whereby qualitative data will explain quantitative findings, the qualitative data analysis should include textual coding, thematic development, and de- scriptions based on these codes. For designs more heavily qualitatively weighted, an analysis that includes more specified analytical outputs, such as an epoche, developed from a phenomenological approach, a theory of a process, developed from a grounded theory, or case study summary descriptions, would represent the most sound mixing of methods and most rigorous qualitative analysis. Further, most of the findings and analyses found in this study do not represent rigorous qualitative analysis, offering a single layer of abstraction, rather than

the multiple-layered analytical approach required for a deeper under- standing of meanings or behaviors, or particular phenomena. One ex- planation for this trend may be the lack of training in both methods. The tools required to conduct strong mixed methods research may re- quire the use of research teams that include experts in the different methodological approaches. Thus, a weakness in the inclusion criteria is the primary focus on reported data, rather than analysis. As the recog- nition and use of mixed methods procedures increases in business re- search, future research should also further scrutinize analytical rigor in both data strands.

The subjective nature of our selection criteria is acknowledged; however, the author believes this presentation of the state of mixed methods research design usage at the Journal of Business Research of- fers scholars a comprehensive review of the literature. It is important to note an additional weakness of the selection criteria involves the nature of the review process that may have resulted in the removal of qualitative or quantitative content, due to space limitations and resulting manuscript streamlining. Thus, the traditional journal arti- cle format in addition to the corresponding review process does not necessarily lend itself to the successful publishing of the weaving to- gether of multiple strands of research.

Freshwater (2007) criticizes mixed methods research because of its desire for certainty, suggesting that the inherent flaw in mixed methods text (and tenets of pragmatism) is the supposition that there be no gaps. This is based on the apparent goal of grasping the “whole” through mixed methods inquiry, with little critical reflection in the assumption that the “whole” is always partial in itself. Johnson and Onwuegbuzie (2004 p. 23) suggest correctly that qualitative, quantitative, and mixed method are “all superior under different circumstances” and researchers must determine how to use different approaches.

Quantitative research is more apt for answering questions about re- lationships between specific variables, and questions of who, where, how many, and how much. Qualitative research is more apt for answer- ing why and how questions. Understanding meanings attached to expe- riences of individuals and organizations is the hallmark of good qualitative research. Further, when researchers quantitatively examine many individuals, the voice of the individuals is diminished, and when researchers qualitatively examine a few individuals, the ability to gen- eralize the results to many is lost (Creswell and Plano Clark, 2011). Mixed method research provides strengths that offset both weaknesses. However, it is not the answer to every research problem, nor does it diminish the value of research conducted entirely quantitatively or qualitatively.

In sum, for scholars interested in mixing data types this paper high- lights and offers the following recommendations. First, understand the definition of mixed method research. Mixed methods research gathers and analyzes both data strands. As was mentioned, several studies were found that mentioned the use of qualitative data to develop an instrument without rigorous qualitative analysis. Understanding the tenets of both quantitative and qualitative analysis ensures the develop- ment of good mixed methods research.

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Second, identify the appropriate design type. In general terms, ex- ploratory designs are employed when researchers want to test or generalize qualitative results to a larger population, or when new emergent research questions based on qualitative results cannot be answered with qualitative data. Explanatory designs are employed when researchers want to investigate trends and relationships with quantitative data and explain reasons behind the quantitative results, or the researcher develops new questions based on quantitative re- sults, that cannot be answered with quantitative data. Embedded de- signs are employed when researchers have different questions that require different types of data in order to enhance the application of a primary quantitative or qualitative design, or researchers do not have adequate skills or resources to place equal priority on both types of data. Concurrent designs are employed when researchers feel that there is equal value for collecting and analyzing both quanti- tative and qualitative data to understand the problem and have skills in both quantitative and qualitative methods.

Third, incorporate mixed methods design procedures. Scholars should recognize, define, utilize, and cite mixed method principles. The classification of design types highlights the basic procedures for dif- ferentiating the designs. Specifically, scholars should determine the pri- ority of the different data strands, the timing of each data type (i.e., sequential or concurrent), and the integration (or mixing) of both data strands. Fourth, integrate both data strands. Scholars should devel- op mixed methods research questions (highlighted in the results sec- tion above) to aid in the synthesizing of the two data strands. Data strand integration is assured when answering mixed methods research questions. Fifth, recognize the challenges of incorporating mixed methods. Mixed methods research requires an investment in time, re- sources, and skills that may hinder the research process. However, for certain research questions it offers an advantage over other methodo- logical approaches.

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