23 Mar 2015
While it is valuable to study what business practitioners and organisations actually do, as well as how successful what they do is for attaining business goals, characteristically using positivist or interpretive empirical research approaches and methods, such research is not the only possible topic for business research. Significantly, the methods, approaches, tools, techniques, practices, procedures, and technologies which are used to do what businesses and practitioners do, need to come from somewhere, otherwise there is little or no progress. The formation, development, and expansion of such new technologies and practices are itself a valid topic of research. It is not reasonable to rely on business practitioners to conceive new technologies and practices (Venable, 2011).
Empirical management accounting research more and more mix qualitative and quantitative methods and join theories normally associated with well-established and arguably incommensurable paradigms. However, the methodological debate in the management accounting literature has been characterized by substantial polarization and a wide-ranging lack of communication between paradigms, particularly the functionalist and interpretive paradigms. Mixed methods research spanning these paradigms may provide a more complete understanding of management accounting practice and comprises a potentially influential means of validating research findings. However, this kind of research has also received significant criticism in the wider social sciences for failing to present a logical philosophical foundation for producing valid knowledge claims. This is particularly the case where the notion of triangulation is invoked as a means of validation (Modell, 2007).
There is a considerable tension between the qualitative and quantitative methodologies, and the researchers who deploy them. It has been suggested that the qualitative methodologies are best used when an area is little known, and so hypotheses cannot be generated for testing by those who support the hypothetico-deductive method. But this viewpoint negates the place of qualitative research as a methodology in its own right. Strong qualitative methodologists suggest that in quantitative research, the positivist view of facts leaves no place for participants as agents, and that many constructs do not exist except in the social world, and so cannot be investigated outside social interaction. Pragmatists suggest that quantitative methods, on the other hand, should be best deployed when more is known, so that hypotheses and research questions can be formulated, and easily tested. Researchers intending to use any methodology need to have very clear ideas about the questions they need to address, and the most appropriate ways of investigating them (Garwood, 2006).
Quantitative research design is an outstanding way of confirming outcomes and verifying or invalidating a hypothesis. The configuration has not altered for centuries, and is typical across a lot of scientific fields and disciplines. Following statistical analysis of the outcomes, a complete answer is arrived at, and the results can be rightfully talked about and published. Quantitative experiments also sift out outside issues, if correctly designed, and so the consequences achieved can be seen as genuine and impartial. Quantitative experiments are practical for testing the outcomes achieved by a sequence of qualitative experiments, leading to a final conclusion, and a paring down of probable directions for follow up research to take (Shuttleworth, 2008).
The greatest benefit of quantitative research is the fact that the data obtained using these methods can be subject to substantial statistical analysis, can oversimplify beyond the sample under investigation, allowing the testing of hypotheses, and the assessment of the efficacy of interventions in a variety of area of interest, including social policy. Additionally, experimentation would have no meaning without quantitative research methods (Garwood, 2006).
Quantitative experiments can be complex and costly and require a lot of time to carry out. They must be cautiously planned to make sure that there is absolute randomization and correct description of control groups. Quantitative studies typically necessitate wide-ranging statistical analysis, which can be tricky, due to the fact that a lot of scientists are not statisticians. The field of statistical study is an entire scientific discipline and can be hard for non-mathematicians
Additionally, the necessities for the triumphant statistical verification of outcomes are very strict, with very few experiments expansively proving a hypothesis; there is generally some vagueness, which necessitates retesting and enhancement to the design. This entails another venture of time and resources must be devoted to fine-tune the outcomes. Quantitative research design also tends to produce only confirmed or unconfirmed results, with there being very little opportunity for grey areas and ambiguity (Shuttleworth, 2008).
Qualitative researchers tend to disapprove of these methods on the basis that most sources of data are not quite what they appear to be. They do not pay attention to social meanings and the ways in which the world is socially assembled. Also, from the perspective of critical researchers, the data are attained using methods where the person or group under study are given no status, being subject to uneven power relations (Garwood, 2006).
Mixed Research, or what is referred to as mixed methods research, involves "mix[ing] or
combin[ing] quantitative and qualitative research techniques, methods, approaches, concepts
or language into a single study" (Johnson & Onwuegbuzie, 2004, p. 17). As noted by Collins,
Onwuegbuzie, and Sutton (2006), mixed research studies contain 13 steps-each of which take place at one of the following three phases of the mixed research process: research conceptualization, determining the rationale of the study and rationale for mixing quantitative and qualitative approaches, determining purpose of the study and the purpose for mixing quantitative and qualitative approaches, determining the mixed research questions, research planning and research implementation. Of these 13 steps, analyzing data in a mixed research study potentially is the most complex step because the researcher(s) involved has to be adept at analyzing both the quantitative and qualitative data that have been collected, as well as integrating the results that stem from both the quantitative and qualitative analysis "in a coherent and meaningful way that yields strong meta-inferences (i.e., inferences from qualitative and quantitative findings being integrated into either a coherent whole or two distinct sets of coherent wholes; Tashakkori & Teddlie, 1998)" (Onwuegbuzie & Combs, 2010, p. 398). As such, guidelines and exemplars are needed for conducting mixed analyses.
Conventionally, as noted by Creswell and Plano Clark (2007), "Data analysis in mixed methods research consists of analyzing the quantitative data using quantitative methods and the qualitative data using qualitative methods" (p. 128). However, mixed analyses also can entail the chronological analysis of one data type, which is referred to as sequential mixed analyses, in which data that are produced from the initial analysis then are transformed into the other data type. For instance, a researcher could carry out a qualitative analysis of qualitative data followed by a quantitative analysis of the qualitative codes that materialize from the qualitative analysis and that are transformed to quantitative data. Such conversion of qualitative data into numerical codes that can be examined quantitatively is known as quantitizing. On the other hand, a researcher could conduct a quantitative analysis of quantitative data followed by a qualitative analysis of the quantitative data that surface from the quantitative analysis and that are transformed to qualitative data (Onwuegbuzie & Combs, 2011).
While empirical accounting studies conventionally have been based on either quantitative or qualitative methods, triangulation or mixing of such methods in the data collection, analysis and interpretation also has been called for (Creswell, 2009). Such mixed methods research has been proposed for the following reasons: to advance validity of theoretical propositions; and to attain a more complete and less prejudiced picture of the phenomenon under study than is possible with a narrower methodological approach. It also has been measured to be useful in specifying research questions, familiarizing the scholar with the subject and context, and in authenticating that all respondents understand the concepts and measures in a comparable way. Mixed methods research has been recommended in unexplored regions where theoretical roadmaps do not yet exist, but where it is significant to apply several methods to stay on firm ground to arrive securely at the destination. Triangulation of methods can facilitate a case researcher to address a broader range of historical, attitudinal and behavioural issues, and to develop converging lines of inquiry that can be used to make case study findings and conclusions more believable and precise. Triangulation in its different forms has also been considered useful in improving the reliability of a study. Some other rationales for conducting mixed methods research are: participant enrichment, instrument fidelity, treatment integrity, and significance enhancement (Ihantola & Kihn, 2011).
Accounting research can be broken down into three forms: basic, applied and usable research. A fundamental or pure research is an empirical or non-empirical research carried out without any exact practical use in view. It does not have to solve any realistic problems but only needs to discover a new problem or develop a new theoretical approach to solve formerly known problems. An applied research tests solutions to problems and produces theory from existing practices, with a view to ultimately solving practical problems, though the impact on practice may not be instant. The third category described as usable or practical research does not entail expanding or testing knowledge but rather it recognizes and distributes information from basic and applied research that is of instant value to accounting practice. This classification of accounting research not only widens the definition of research and expectations from researchers, it also has insinuations on the design and evaluation of research. It further suggests that all policy initiatives to support accounting practitioners to read and transfer research findings to practice (Gordon & Porter 2009) should proceed from the premise that some research publications are not intended for practitioners in the short or medium term horizon.
The current classifications of research methodology in accounting are presented in Table
5. In total, there are approximately twenty methodologies: Analytical, Archival, Behavioral,
Capital market, Case study, Content analysis, Discussions, Economic modeling, Empirical, Ethnography, Experiment, Field study, Internal logic, Normative, Opinion, Review, Simulation, Statistical, Survey and Theoretical. What is found here is essentially a conflation of empirical (e.g. experiment and case study) and non-empirical (e.g. internal logic and analytical) research strategies; a motley of research strategy (e.g. ethnography and experiment) and data collection (e.g. archival and survey) and analysis (e.g. statistical and content analysis) methods.
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