Science has built a foundation on past achievement. Kuhn [1996, p. 5] stated, “Normal science, the activity in which most scientists inevitably spend almost all their time, is predicated on the assumption that the scientific community knows what the world is like.” Guidelines and procedures have been developed for conducting research by the scientific community to maintain integrity and stability. When new knowledge is created, based on past accomplishments, it becomes more acceptable to the community. Kuhn [1996, 95] said, “A new phenomenon might emerge without reflecting destructively upon any part of past scientific practice.” New knowledge is accepted by the community when research methods adhere to established guidelines and procedures. The scientific community has suppressed and rejected concepts that have challenged the basic assumptions of the community, especially when the guidelines have not been followed.
There are two main research methodologies: quantitative and qualitative. A third methodology, a combination of the two, is gaining acceptance as a way to improve and substantiate research findings. The quantitative method, which has its origin based in the scientific method, relies on statistical procedures for data analysis. In contrast, qualitative methods rely on the descriptive narrative for data analysis [Berrios & Lucca, 2006]. Based in long standing tradition, quantitative studies dominate the research literature. But, qualitative studies are starting to be recognized as an important source of knowledge. A mixed methods approach has recently emerged which combines quantitative and qualitative methods into a new methodology.
Choice of methodology is heavily influenced by the research question. The research question is an overlying question that moves the researcher from a dilemma observation to exploratory examination. Cooper and Schindler [2006, p. 59] categorize management questions into three broad categories: “[1] choice of purposes or objectives, [2] generation and evaluation of solutions, and [3] troubleshooting or control situation.” Proper research questions provide direction and focus in shaping data collection and analysis procedures.
This paper will review major methods used in quantitative and qualitative research, followed by a discussion of mixed methodology. An analysis and comparison of research methodologies will be discussed, including an evaluation of methodologies to be used when measuring leadership effectiveness.
Quantitative Research Methodology
Because quantitative methods are based in scientific discovery, the “notion among scholars that the traditional scientific method is the best, if not the only, legitimate way to conduct scholarly research” prevails [Gerdes & Conn, 2001, p. 1]. So, the scientific community has developed clear guidelines and procedures for implementation of quantitative methods. Quantitative, similar to the word quantity, implies using numerical data. So, quantitative methods rely on experiments and surveys to collect measurable data such that statistical processes can be applied [Creswell, 2003]. A major advantage of quantitative methods is that the results are usually generalizable to larger populations. The following paragraphs will discuss experimental and non-experimental design methods.
Experimental design.
The experimental design is conducted in a laboratory setting which controls for variability [Brown & Lord, 1999]. The primary benefit of an experimental design is that it may lead to causation. When one variable acts on another it may be deduced that the resulting state is caused by the action. Experimental methods provide an environment in which the unique characteristics or qualities of variables may be studied and their contribution may be measured. Laboratory settings are environments that lessen potential bias that occurs in natural settings where variables act as parts of a whole system. When working within a system it becomes difficult to isolate individual contributions and measurements for some variables. But, experimental methods have been criticized for removing the variable from its natural setting where it may not react the same way. Experimental designs have the potential to bias variable action by reducing or stopping the interaction among other variables.
Non-experimental design.
Non-experimental designs include observation, survey, and other related methods of collecting numerical data that do not involve laboratory settings. The measurement of processing time, supply chain efficiency, product supply and demand, and price elasticity lend themselves to quantitative methods. An example, supply chain processing which was traditionally viewed as a linear process was measured during its transition to Internet-based ordering [Keskinocak & Tayur, 2001].
Surveys are used to collect information about variables from participants in a population. Surveys have been used to collect political poll behaviors, marketing research, and a variety of other useful information. External validity is improved when data are collected using field surveys. External validity implies that the results will generalize to a larger population because the findings are applicable across different settings and participants [Egan, 2005]. Surveys include using questionnaires or structured interviews for data collection. Quantitative methods used structured interviews which repeat the same questions without deviation to each participant. Structured interview questions aim to maintain consistency and avoid bias. Surveys are instruments that collect data which makes statistical calculations possible. Statistics make use of sampling to infer results to a larger population. Surveys can be cross-sectional which collect data at one point in time or longitudinal which collects data over time. Fink [as cited in Creswell, 2003] identifies four data form collection instruments: self-administered questionnaires, interviews, structured record reviews, and structured observations. Survey development and validation is beyond the scope of this paper, but it is important to understand that surveys must have reliability and validity to be considered a feasible instrument. Most surveys have been tested for validity which means that significant and useful scores can be derived from the instrument. In other words, the instrument is measuring what it intended to measure. Validity is comprised of content validity [measurement of intended purpose], predictive validity [correlation with other results using the same instrument], and construct validity [measurement of constructs or concepts] [Creswell, 2003]. Surveys must also be reliable which means that the results are repeatable and that retests provide stable responses.
Qualitative Research Methodology
Qualitative methodologies are used to analyze and evaluate non-numerical information. Quantitative studies try to understand intangible evidence, such as emotion and behavior. Qualitative methods are applicable to studies that involve relationships between individuals, individuals and their environments, and motives that drive individual behavior and action. Berrios and Lucca [2006, p. 174] claimed that qualitative methods provide for a “better understanding of human development.”
Qualitative methods do not impose rigid rules and procedures similar to quantitative methodologies. Qualitative methods derive the research process from the data itself. One process will usually lead to development of the next step. Qualitative methods allow “richness of the personal experience” by providing in-depth information in the natural language of the experience. This allows data categorization by witnessing the experience in its natural setting, disallowing preconceived hypotheses, and using critical researcher judgment [Berrios & Lucca, 2006, p. 181].
Gerdes and Conn [2001] suggested that qualitative methods allow looking at the “whole rather than the parts”. Gerdes and Conn [2001] also claimed that hypothesis testing, as specified in quantitative research methods, may be testing the wrong questions. It was asserted that “it is far better to have an approximate answer to the right question than to have an exact answer to the wrong question” Gerdes and Conn [2001, p. 184]. This statement may offend quantitative researchers, but it does stress the significance of stating the proper research question when committing to a quantitative method. Qualitative methods permit flexibility and procedure change because the process emerges from patterns found in the data. The following paragraphs will briefly describe major qualitative methodologies.
Narratives.
Narrative research involves asking individuals to share stories about their lives and deriving meaning from the experiences mentioned [Creswell, 2003; Berrios & Lucca, 2006]. The aim of the narrative approach is to combine the story’s chronological events with that of the researcher’s personal experience. Narratives may be supplemented with historical investigation, use of documents, and media sources. Speaking about counseling, Berrios and Lucca [2006, p. 178] stated, “The oral histories offered by prominent figures constitute a valuable contribution to the history of the profession.”
Phenomenologies.
Phenomenology is not a research method that provides formal rules and guidelines for inquiry. Phenomenology is more of a mental mindset which searches for meaning through perception. Phenomenology asserts that experience is more than what the “physical senses can apprehend’ [Budd, 2005, p. 45]. Experience is understood through perception, intuition, and cognition. It is believed that the world has no meaning except from our consciousness and that our consciousness has direction and purpose. Budd [2005] claimed that phenomenology explained intentionality because our perceptions have direction. The researcher becomes a participant by using self perceptions of the world in the phenomenological approach. Phenomenology is never considered finished because each individual is a living being whose life is in progress. Phenomenology can also be accomplished through sharing narratives. Kuper [2005, p. 120] claimed, “Narratives can become media for expressing, processing and sharing contents, structures and experience of implicit knowing-acting.”
Ethnographies.
The ethnographic methodology involves a researcher collecting observational data of an intact cultural group in their natural setting over time [Creswell, 2003]. Ethnography has been described as the study of human cultures and producing a “descriptive work from such research” [Rudkin & Deo, 2006, p. 21]. Ethnography uses non-numerical, context specific data which can not be reproduced [Rudkin & Deo, 2006]. But, ethnographic studies need not be confined to observational information alone. Ryan and Bernard [2006] constructed an ethnographic decision model which was able to predict at least eighty percent of the studied behavior. The decision tree was built based on small sample interviews [20 to 60 persons] and then tested on an independent sample. The model consisted of five steps: [1] select the behavior to be studied and select a convenience sample, [2] select the decision criteria, [3] use the data from steps 1 and 2 to build a hierarchical decision model, [4] test the model on an independent sample, and [5] validate the model by asking individuals why they acted the way they did [Ryan & Bernard, 2006]. The model was most successful when using decisions that called for a “yes” or “no” answer and “if-then” statements. The model contributed to understanding human behavior and human choice selection. One of the criticisms of ethnographic studies is that they are not based on representative samples. But, it has been argued that most people are not average; therefore, the stories and interviews collected by ethnographic studies present reality [Gilmore, 2002].
Grounded theory.
Grounded theory is a method in which one step of the process predicates the actions of the next step. The researcher does not exhaust the literature before conducting research as in quantitative methodology. The literature is consulted as part of an iterative process of data collection [Goulding, 2005]. The aim of grounded theory is to derive theory based on emerging patterns from the views of study participants [Creswell, 2003]. In such a situation, the data analysis determines the next step of the process rather than predefined rules and procedures. The researcher need to conduct several survey waves or interviews as more information is needed based on the results of earlier data collection. Creswell [2003] asserted that two primary characteristics of grounded theory are that there is a constant data comparison with emerging categories from the collection process and sampling of different groups to highlight similarities and differences among data. Data reduction is derived from the perspective of the researcher based on experience, reading, and research. The researcher is an active participant in the process who introduces self bias. As many methodologies predefine sampling populations, grounded theory researchers select informants that are most likely to provide early information which is used to further define sampling populations and survey questions.
Opponents to grounded theory claim that researchers have preconceived bias about the study subject, the knowledge produced is not applicable in the real world, and data coding may be subjective and sway the outcome of the study [Selden, 2005]. One example of an alternative outcome was revealed by Herek, Janis, and Huth [1987] when recoding quantitatively driven comparative analyses on groupthink.
Case studies.
Case studies allow in-depth understanding of participants, events, behaviors, and feelings that occur during specific experiences and specific timeframes. Yin [as cited in Woodside and Wilson, 2003, p. 493] stated, “A case study is an empirical inquiry that investigates a contemporary phenomenon within its real-life context; especially when the boundaries between phenomenon and context are not clearly evident.”
Woodside and Wilson [2003] used several methods which allowed triangulation. The triangulation model included direct observation of the case by the researcher in its natural environment, asking questions of participants for clarification and interpretation, and analysis of written documents to clarify and substantiate findings. Such processes and methods lead to a deep understanding of the mental model possessed by participants [Woodside & Wilson, 2003].
Mixed Methods Research
The mixed methods approach collects and uses quantitative and qualitative data in the same study. Many researchers believe this is a new methodology, but quantitative and qualitative data have been collected by researchers for many years. The combination of the two methods is a recent event. Creswell and Clark [2007, p. 5] defined mixed methods research as follow:
Mixed methods research is a research design with philosophical assumptions as well as methods of inquiry. As a methodology, it involves philosophical assumptions that guide the direction of the collection and analysis of data and the mixture of qualitative and quantitative approaches in many phases in the research process. As a method, it focuses on collecting, analyzing, and mixing both quantitative and qualitative data in a single study or series of studies. Its central premise is that the use of quantitative and qualitative approaches in combination provides a better understanding of research problems than either approach alone.
Hurmerinta-Peltomakl and Nummeia [2006] claimed that using one method alone would only provide a small view of the whole picture when studying complex issues. Mixed methods provide information on different levels of understanding. When methods are combined, qualitative methods may provide in-depth understanding of the variables that lead to quantitative numerical findings. Mixed methods may also be used for triangulation or to improve the validity of research [Hurmerinta-Peltomakl & Nummeia, 2006]. It has been proposed that triangulation should test for consistency of methodology rather than reaching the same results using different data sources or approaches [Rocco, Bliss, Gallagher, & Perez-Prado, 2003]. Mixed methodology is also used to lessen bias as researchers have accepted that all processes have underlying biases.
Mixed method studies may start with qualitative methodology to define research questions or acquire subject familiarization. The researcher is able to more accurately interpret research finding by gaining a greater understanding of the research subject. There are no prescribed processes or rules for combining quantitative and qualitative methodologies. But, Creswell [2003] has categorized six mixed method variations of data collection and analysis. First, the sequential explanatory strategy collects and analyzes quantitative data followed by collection and analysis of qualitative data. Second, the sequential exploratory strategy collects and analyzes qualitative data followed by collection and analysis of quantitative data. Selden [2005, p. 117] claimed, “A qualitative analysis may build on qualitative and quantitative data, but mainly on the former. Qualitative research hardly uses quantitative data.” Third, the sequential transformative strategy provides for data collection and analysis of either type of data before combining the data during the interpretation phase of the study. This methodology is guided by a theoretical perspective. Fourth, the concurrent triangulation strategy collects data concurrently and tries to “confirm, cross-validate, or corroborate findings within a single study” [Creswell, 2003, p. 217]. Fifth, the concurrent nested strategy collects both data types concurrently and embeds one methodology within a more predominant method. The researcher may address different questions from the hierarchical question ladder when applying this methodology. Sixth, the concurrent transformative strategy collects each type of data concurrently and combines the findings during the analysis phase of the study.
Quantitative, Qualitative, and Mixed Method Analysis and Comparison
Creswell and Clark [2007, p. 130] stated, “exploring the data means [a] examining the data with an eye to developing broad trends and the shape of the distribution or [b] reading through the data, making memos, and developing a preliminary understanding of the database.” Quantitative methods call for the application of statistical processes to refine and show patterns that emerge from the data. Qualitative methods call for coding the data, which involves dividing the data into smaller units or categories based on phrases, ideas, sentences, or other logical units. A major difference between quantitative and qualitative research methods is that quantitative methods take more effort during the beginning research phase while qualitative methods take more effort during the final phase. Quantitative methods call for survey preparation, testing, validation, sample identification, and a myriad of procedures. In contrast, qualitative methods allow more flexibility during the beginning phase of the process.
Quantitative and qualitative methods are scrutinized for apparent validity. Arbnor and Bjerke [1997, p. 232] claimed that validity is the “extent to which the indicators of a measuring instrument correspond to a definition”. Validity is improved through continuous adjustment between theory construction methodology and research methodology. This involves being flexible and able to adapt to changing conditions. If results lead to unforeseen conclusions, the hypothesis or research question may need revision. In contrast, methods must change if the original hypothesis needs substantiation. This includes data collection processes or restructuring survey questions. But, it is important to develop methods that lower bias. Arbnor and Bjerke [1997, p. 233] define validity as “absence of systematic bias.” Reaching inaccurate research conclusions is largely a result of bias.
Opponents of quantitative methods suggest that statistics do not represent the real world. Criticism suggests that statistics revolve around a numerical central tendency [mean, mode or median] and that the methodology tries to categorize participants more average than not [Gerdes & Conn, 2001]. So, quantitative methods do not encourage the recognition of exceptional or above average performance.
Another criticism waged against quantitative methods is that the possibility of testing the wrong or inappropriate research question exists. Because quantitative methods follow a rigid method of inquiry, selection of the wrong question can produce a devastating effect. In contrast, qualitative methods allow adjustment and realignment of methodology for changes in the research question.
Experiments and surveys are primary quantitative data collection methodologies. Schafer and Crishlow [1996] suggested that experimentation may contribute to knowledge and understanding, but that it is far removed from real world experience. Schafer and Crishlow [1996] alluded that experimentation does not provide causation, but that it only addresses some of the processes. One aim of quantitative methods is to diminish variability [Gerdes & Conn, 2001]. As reducing variability also reduces bias and provides focus on specific variables, it also changes the attributes of the variable because the variable is not being observed in its natural setting. But, it has been suggested that external validity can be reduced when discovery or innovation is a research objective [Brown & Lord, 1999]. Experiments call for laboratory environments, special equipment, and selective identification of participants or variables. These conditions contribute to the extremely high cost of performing experimental research designs.
Quantitative methods, when using surveys, assume that respondents understand and accurately answer questions [Brown & Lord, 1999]. It is possible for respondents to provide different answers to the same questions based on emotional status, health condition, learning, and fatigue. Validity and substantiation of accepted perceptions of reality are reached when many respondents answer the questionnaire in a similar fashion over several iterations, thereby negating the effects of change in personal condition. Allen and Austin [2001, p. 395] stated, “Survey research methods are, in essence, procedures for collecting data based on expectations and perceptions rather than observed transactions or behaviors.” Surveys may provide indicators of behaviors and intentions, but they are not observable actions of reality.
Survey composition must also be considered when evaluating effectiveness. Surveys that are too long or complicated may cause respondent irritation or fatigue. Such conditions may bias the responses in a negative way. This is true for extended interviews, whether they are quantitative or qualitative. Quantitative or qualitative interviews and surveys may be conducted in person, over the phone, by Internet, or by mail. One consideration to take into account is the cost of administering the survey, interview, or questionnaire. Quantitative methods need specific minimum sample sizes to provide significant test results. Conclusions can not be drawn from statistical results that are not statistically significant. Sample size contributes to a variable cost in administering the interview or survey. Survey or interview cost climbs as more participants or respondents are added to the study.
Qualitative research methods are used to understand complex issues [Trim & Lee, 2004]. Such methods also allow researchers to draw insights from related bodies of knowledge. Because of the narrow focus used in qualitative research, applicability of the findings is narrow. Oral histories, case studies, and grounded theory use small samples which can not be generalized to a larger population. Such methods are usually applicable to specific settings and conditions from which the information was gathered. In contrast, statistical methods are usually generalizable to larger populations.
Qualitative research methods have been criticized for being subjective and biased. It is important that the population and measurement instrument do not introduce bias [Allen & Austin, 2001]. But, because qualitative methods use unstructured processes of data collection it becomes difficult to distinguish between fact and bias.
Trevino, Brown, and Hartman [2003] suggested that qualitative methodology was proper for preliminary research for issues where empirical research was not conclusive. This suggests that the true research methodology is quantitative and qualitative is a precursor to quantitative. This sentiment most likely traces its origins back to the scientific method. But they also concluded that complex or subjective issues are compatible with quantitative methodologies. Generalizability is not achievable with qualitative methods because reproducibility is not possible. As reproducibility is unlikely, qualitative research is likely to result in different findings when conducted by different researchers.
Mixed methods research avoids many criticisms by cancelling the effects of one methodology by including the other methodology. Mixed methods research triangulates results that offer higher validity and reliability. One downside to using mixed methodologies is that it takes more time and effort, besides adding potential cost.
Measuring Leadership Effectiveness
Ciulla [2004, p. xv] stated, “Leadership is not a person or a position. It is a complex moral relationship between people, based on trust, obligation, commitment, emotion, and a shared vision of the good.” Leadership responsibilities include building interpersonal relationships and striving to reach organizational goals. Relationships are built with peers, followers, and superiors. Phillmore [2003, p. 100] claimed, “Whatever is emphasized, measured, and rewarded – that’s what people do, and that’s what they keep getting better at doing.” Organizational goals achievement improves as leaders build quality interpersonal relationships. The phrase “do the right things, not just do things right” stresses the significance of providing positive leadership which does the right things for the individual and the organization.
Qualitative methods are excellent ways to explore relationships because they lead to understanding behavior and emotion. Narratives, grounded theory, case studies, and ethnographies provide in-depth insight and understanding to the human relationship. Storr [2004, p. 424] claimed that qualitative methods are the best methods to understand the “nature of human perceptions, thoughts and ideas, which recognize the complex and dynamic quality of the interpersonal world.” Human beliefs, which are intrinsic to each individual, serve as a source of motivation which leads to action. Qualitative methods are suitable for exploring intrinsic beliefs.
Mason and Pauleen [2003] suggested that most management approaches to knowledge transfer involve innovation and creativity. Past studies have used survey data for these issues. In contrast, quantitative methods are fitting for some leadership topics. Leaders are responsible for productivity, employee turnover rates, inventory turnover, cost control, profit margins, and other financial goals. Such issues call for statistical and trend analysis. So, effective leadership measurement calls for a mixed methodology that includes quantitative methods and qualitative methods. Quantitative methods are used to measure and analyze organizational and company goals while qualitative methods are used to explore and understand the leader-follower relationship. These two sides of leadership are not mutually independent because the quality of the leader-follower relationship can lead to improved productivity, low employee turnover, improved profit margins, and reaching organizational goals. The mixed methods approach can be conducted using a concurrent nested strategy. It should be remembered that the research question will determine the method to be applied. The methodology used will be dependent on defining management effectiveness as described by the researcher.
Conclusion
This paper has explored quantitative, qualitative, and mixed methods approaches to research. It has been shown that the research question drives the methodology to be used in the research process. Quantitative methods have dominated the research community because of its roots in scientific discovery. Quantitative methods identify hypotheses and statistically test variables against such hypotheses. Statistical tries to find patterns in the data, describe the data, or to draw inferences about the population from a sample. Such methods are usually generalizable to a larger population. In contrast, qualitative methods use techniques such as narratives, phenomenologies, ethnographies, grounded theory, and case studies. Such methods try to understand the deeper meaning of behaviors, relationships, and emotions. These types of issues can not be described by numbers alone. Qualitative methods use coding techniques to organize and categorize data into meaningful divisions for further analysis.
Subjects, such as leadership effectiveness, may call for using quantitative and qualitative methods. Use of the two methods together is called a mixed methods approach to research. The mixed methods approach has the potential to overcome individual bias and the criticisms pointed at each methodology individually. Mixed methodologies and qualitative methodologies have been dominated by quantitative methodologies for decades. But, is appears that mixed methods research will play a greater role in future research.
References
Allen, M. T., & Austin, G. W. [2001]. The role of formal survey research methods in the appraisal body of knowledge. The Appraisal Journal, 69[4], 394-403.
Arbnor, I., & Bjerke, B. [1997]. Methodology for creating business knowledge [2nd ed.]. Thousand Oaks, CA: SAGE Publications.
Berrios, R., & Lucca, N. [2006]. Qualitative methodology in counseling research: Recent contributions and challenges for a new century. Journal of Counseling & Development, 84[1], 174-186.
Brown, D. J., & Lord, R. G. [1999]. The utility of experimental research in the study of transformational/charismatic leadership. Leadership Quarterly, 10[4], 531-539.
Budd, J. M. [2005]. Phenomenology and information studies. Journal of Documentation, 61[1], 44-59.
Ciulla, J. B. [2004]. Ethics: the heart of leadership [2nd ed.]. Westport, CT: Praeger.
Cooper, D. R., & Schindler, P. S. [2006]. Business research methods [9th ed.]. Boston: McGraw-Hill Irwin.
Creswell, J. W. [2003]. Research design: Qualitative, quantitative, and mixed methods approach [2nd ed.]. Thousand Oaks, CA: Sage Publications, Inc.
Creswell, J. W., & Clark, V. L. [2007]. Designing and conducting mixed methods research. Thousand Oaks, CA: Sage Publications, Inc.
Egan, T. M. [2005]. Factors influencing individual creativity in the workplace: An examination of quantitative empirical research. Advances in Developing Human Resources, 7[2], 160-181.
Gerdes, D., & Conn, J. [2001]. A use-friendly look at qualitative research methods. Physical Educator, 58[4], 183-190.
Gilmore, D. [2002, May/June]. Understanding and overcoming resistance to ethnographic design research. Interactions, 29-35.
Goulding, C. [2005]. Grounded theory, ethnography and phenomenology: A comparative analysis of three qualitative strategies for marketing research. European Journal of Marketing, 39[3/4], 294-308.
Herek, G. M., Janis, I. L., & Huth, P. [1987]. Decision making during international crises: Is quality of process related outcome? The Journal of Conflict Resolution, 31[2], 203-226.
Hurmerinta-Peltomakl, L., & Nummeia, N. [2006]. Mixed methods in international business research: A value-added perspective. Management International Review, 46[4], 439-459.
Keskinocak, P., & Tayur, S. [2001]. Quantitative analysis for Internet-enabled supply chains. Interfaces, 31[2], 70-89.
Kuhn, T. S. [1996]. The structure of scientific revolutions [3rd ed.]. Chicago: The University of Chicago Press.
Kuper, W. [2005]. Phenomenology of embodied implicit and narrative knowing. Journal of Knowledge Management, 9[6], 114-133.
Mason, D., & Pauleen, D. J. [2003]. Perceptions of knowledge management: A qualitative analysis. Journal of Knowledge Management, 7[4], 38-48.
Phillmore, E. M. [2003]. How we’re fixing up Tyco. Harvard Business Review, December, 96-103.
Rocco, T. S., Bliss, L. A., Gallagher, S., & Perez-Prado, A. [2003]. Taking the next step: Mixed methods research in organizational systems. Information Technology, Learning, and Performance Journal, 21[1], 19-29.
Rudkin, K., & Deo, H. [2006]. Ethnographic methodology and its implications for banking studies. The Business Review, Cambridge, 6[2], 20-25.
Ryan, G. W., & Bernard, H. R. [2006]. Testing an ethnographic decision tree model on a national sample: Recycling beverage cans. Human Organization, 65[1], 103-114.
Schafer, M., & Crishlow, S. [1996]. Antecedents of groupthink. The Journal of Conflict Resolution, 40[3], 415-435.
Selden, L. [2005]. On grounded theory - with some malice. Journal of Documentation, 61[1], 114-129.
Storr, L. [2004]. Leading with integrity: A qualitative research study. Journal of Health Organization and Management, 18[6], 415-434.
Trevino, L. K., Brown, M., & Hartman, L. P. [2003]. A qualitative investigation of perceived executive ethical leadership: Perceptions from inside and outside the executive suite. Human Relations, 56[1], 5-37.
Trim, P. R., & Lee, Y. [2004]. Enhancing customer service and organizational learning through qualitative research. Qualitative Market Research, 7[4], 284-292.
Woodside, A. G., & Wilson, E. J. [2003]. Case study research methods for theory building. Journal of Business & Industrial Marketing, 18[6/7], 493-508.