Accounting information systems research opportunities using personality type theory and the Myers-Briggs Type Indicator.
Keywords: personality type; personality traits; type theory; Jungian psychology; cognitive science; Myers-Briggs Type Indicator (MBTI).
The cognitive paradigm, with its accompanying computational and information processing models of cognition, has long dominated accounting information systems (AIS) research One of the strengths of the cognitive paradigm is that it is quantitative, thus allowing one to make and test predictions. An additional advantage of the paradigm is its alignment with computer science and information processing theory via the use of an input/process/output/feedback framework (Newell and Simon 1976).
Cognitive models are universally used in behavioral research and are fundamental to understanding the human mind. There is, however, disagreement over how much of the mind these models explain. While some believe that they account for the majority of mental processes (Johnson-Laird 1988; Pinker 1997), others think that they account for only a small subset (Fodor 2000). Few researchers contend that cognitive psychology provides a complete explanation of mental processes, for it is constrained by restrictive parametric assumptions. A more inclusive paradigm of mind is needed--one that incorporates cognitive processes along with noncognitive processes, such as affectations, feelings, emotions, unconscious structures, and motivations. (1)
Personality type theory (PTT), an established theory within personality-based psychology (Hergenhahn and Olson 1999), is an alternative representation of the mind that can supplement cognitive science. Based on the work of Jung (1921), PTT is especially useful for AIS researchers, for it provides a framework in which cognitive processes may be understood within a larger context, as we will discuss throughout the article. PPT has generated several psychometric instruments (e.g., Myers-Briggs Type Indicator [MBTI], Keirsey Temperament Sorter [KTS], and Jungian Type Survey [JTS] that researchers can use experimentally for testing and measuring PTT constructs. The MBTI, in particular, has been extensively validated and is widely used in numerous areas of research and practice.
This paper identifies opportunities for personality-based AIS research by posing research questions whose answers would provide a rich contribution to the AIS literature. The paper adopts the perspective of Jung (1921) and uses the MBTI to illustrate how personality trait research can inform AIS. This perspective, however, is not the sole or necessarily the predominant viewpoint, but rather illustrates the possible contributions that can be obtained by incorporating personality theories into AIS research.
In the next section, we examine one of the seminal critiques of the role of personality in AIS research. In Section III, we discuss Jungian PTT and the MBTI. Finally, we identify future AIS education and research opportunities within the MBTI context.
II. HUBER'S (1983) CRITIQUE OF COGNITIVE STYLE RESEARCH
Huber's (1983) seminal critique of research on the relationship between cognitive style (which includes a strong personality component) and the use of information systems has had a significant impact on information systems (IS) and AIS research. Accordingly, we reconsider Huber's (1983) arguments as a necessary condition to proceeding forward with our thesis regarding the importance of personality-based research in AIS. Huber (1983, 568) lists the following reasons for his conclusion that cognitive style research is, and will continue to be, "weak and inconclusive":
1. The theory of cognitive style is inadequately developed;
2. The measurement instruments lack reliability and validity;
3. Much research in this area relies on faulty methodology and research designs;
4. Multiple user-characteristics in addition to cognitive style make it difficult to arrive at any conclusions from cognitive style research that inform IS design;
5. Differences between task requirements and user preferences leave IS designers with a dilemma as to which to emphasize;
6. Dysfunctional aspects of user preferences raise the question as to whether IS designers should build systems to match cognitive styles;
7. Advances in technology allow users multiple DSS [decision support systems] options.
PTT and the MBTI allow us to directly address the concerns expressed in reasons one and two. With respect to the first point, some researchers have considerably strengthened cognitive style theory by mapping personality types to styles (e.g., Chenhall and Morris 1991; Hunt et al. 1989; Ruble and Cosier 1990). Regarding Huber's (1983) second criticism, researchers have extensively validated and tested the reliability of the MBTI (Harvey 1996; Wheeler 2001). While the third reason can only be addressed one research project at a time, researchers can improve their research designs, increase the precision of their predictions, and perform more discriminating measurements by relying on validated theories and reliable instruments.
Reasons four through six involve design issues. In his response to Huber, Robey (1983, 580) argues that the inconclusiveness of cognitive style research has been beneficial in that it has "provided much of the rationale for a flexible DSS." Furthermore, the reasons listed for not engaging in cognitive style research may serve as an agenda for future research in this area. In particular, researchers need to determine empirically: (1) the relative importance of cognitive style and personality in relation to other user characteristics, (2) the interaction of task and user characteristics, and (3) the degree to which user preferences are dysfunctional.
We believe that reason seven is the harshest criticism of cognitive style research. Huber (1983) predicted that future systems would be configurable at the user level, which would eliminate many of the concerns with cognitive style research, as it would render irrelevant attempts to design the system to fit the user. As Huber (1983) anticipated, users can now configure many system interface features, but such flexibility raises new issues, many of which can only be answered by assessing user characteristics.
For example, will the choices users make among multiple IS features serve the best interest of the organization (Wheeler and Jones 2003)? If not, how might we restrict or direct users' choices? Robey (1983) observed that once systems with multiple features become a reality, either designers or users would choose which features to make available. In that case, who is to say that designers' preferences are more or less functional than users choices, or that designers have better insight into problem solving than users? Systems designers are trained in specialized technical areas (e.g., hardware design, software programming, and interface design) that generally do not include the psychology of problem solving, decision making, and human cognition. (2) Neither can we assume that users are aware of which interface features will enhance decision-making processes (Davis and Kotteman 1994). PTT and three decades of MBTI research can add useful insight into these areas.
III. JUNGIAN PERSONALITY THEORY AND THE MBTI
Behaviorism relies on a stimulus-response paradigm that focuses on external stimuli and observable behavior as the output/response. This approach tends to ignore psychological processes. Personality psychology and cognitive science view the human mind as mediating the effects of external stimuli on behavioral responses. Researchers can observe one facet of such internal mediation by examining the way in which humans process information. We believe it is also critical to consider other mediating processes, such as personality characteristics. Carl G. Jung (1875-1961) emphasized personality as a mediating and integrating factor for numerous psychological processes, e.g., information processing, individual development, and the role of the unconscious (Pascal 1992).
The relationship between Jungian personality theory (Jung 1921) and cognitive science is complex, but worth investigating because of the dominant role of the cognitive paradigm in AIS research. On one side, Jungian theory, like cognitive science, views the mental functions related to information acquisition and decision making as central to the personality. Researchers often use psychological instruments developed from Jungian theory to capture aspects of cognition for information processing research (Carey et al. 1989; Chenhall and Morris 1991; Kerin and Slocum 1981; Vassen et al. 1993). On the other side, Jungian theory, like other personality theories, insists that human cognition cannot be adequately understood in isolation, but must be placed within a broader context that includes aspects of personality. Thus, Jungian personality instruments include attitude scales that capture such attributes as introversion, extraversion, adaptability, organization, depth of concentration, feeling, intuition, and breadth of interests. Jungian theory does not repudiate the cognitive science approach, so much as insists that it should be grounded, expanded and integrated in a larger framework.
Jung's (1921) theory of personality analyzes the individual as either a whole personality (i.e., a type) or as a collection of characteristics (i.e., traits or preferences) that comprise a personality type. Jungian theory prefers the former approach to the latter because certain traits interact. For example, the type "introverted thinker supported primarily by sensory data" consists of various traits (e.g., introversion, thinking, sensing, and judging) that combine to form the personality. A trait in one personality type may have a different effect than the same trait in another type due to its interaction among traits. (3)
According to Jungian theory, there are eight personality traits. These eight traits comprise two bipolar pairs of mental functions and two bipolar pairs of attitudes. (4) The resulting four bipolar pairs are:
* Attitude Pairs -- Extraversion Introversion (EI) -- Judging Perceiving (JP) * Mental Function Pairs -- Sensing Intuition (SN) -- Thinking Feeling (TF)
To some extent, individuals possess all eight traits; however, one trait from each of the four bipolar pairs dominates; i.e., each individual has a predisposition toward one of the two traits in each bipolar pair. These four preferred traits (preferences) interact to define the predominant characteristics of the personality type. Nevertheless, an individual may be competent using the four nonpreferred traits. (5)
Jung (1921) postulated three bipolar dimensions, resulting in eight personality types. The fourth bipolar scale, the JP attitude, was added by Myers and Briggs, doubling the number of distinct personality types to 16 (Myers et al. 1998). The JP bipolar scale was developed to help differentiate how people deal with the external world and respond to mixed results with psychometric instruments using only Jung's original three bipolar scales (Meier and Wozny 1978; Rosenak and Shontz 1988).
Type descriptions can be very complex and may be described in terms of occupational and organizational traits, educational traits and learning styles, and decision-making traits and cognitive styles (Pascal 1992; Myers and Myers 1995; Myers et al. 1998). Table 1 presents personality descriptions of the 16 types, emphasizing cognitive characteristics and occupational tendencies that are particularly applicable to AIS research. More detailed personality descriptions are available that include other traits, such as learning styles and noncognitive or irrational mental traits (Myers et al. 1998).
Attitudes: EI and JP Bipolar Pairs
In Jungian theory, attitudes reflect an individual's fundamental views of the physical and mental aspects of the world. Extraversion (E) and Introversion (I) describe the individual's approach toward the internal and external aspects of the world. Extraversion indicates that the individual's attention is primarily directed at the external world; e.g., people and objects. Introversion, on the other hand, indicates that the individual tends to focus on the inner, subjective world of body and mind. Note that in Table 1 that introverted types are viewed as more detached and contemplative, while extraverted types are more personable and outgoing. It is important to understand that there are no value judgments associated with these characteristics.
The second attitude in Jungian theory consists of Judging (J) and Perceiving (P) traits. The JP attitude indicates which mental function dominates when dealing with the external world, regardless of whether the individual is predominantly oriented toward the external environment (Extravert) or the internal environment (Introvert). It thus shows an overall attitude toward the two mental functions. When dealing with the external world, individuals with a judging-preference plan ahead, organize their activities, and work problems to their solutions. Individuals with perceiving-preference tend to approach problems in an open and ad hoc manner, gather as much information as possible, and often leave problems unsolved if feasible. Examining Judging and Perceiving types in Table 1 indicates that Judging types are more decisive and committed, while Perceiving types are more open-minded and questioning.
Mental Functions: SN and TF Bipolar Pairs
Jung (1921) contended that differences in the way individuals perceive and form judgments about the world account for many observed personality and behavioral differences. This information processing and decision-making orientation explains why Jungian theory places great emphasis on mental functions. This is also one reason why we believe that Jungian theory and the MBTI hold such great potential for AIS research.
In Jungian theory, there are two bipolar mental functions: Perceiving and Judging. The Perceiving mental function has two traits, Sensing (S) and Intuition (N). These two traits primarily serve as input channels that provide the data processed by the Judging function. Both traits collect data from the physical senses and various internal organs. Sensing perceptions are more discrete and atomic, whereas Intuition perceptions rely more on structures and relationships among sensations and experiences. Metaphorically, Sensing types "see the trees," while Intuition types "see the forest." A comparison of Sensing types to Intuition types in Table 1 suggests that Sensing types are more factual and observant, while Intuition types are more insightful and creative.
The Judging function is predominantly a processing function that transforms data provided by the Perceiving function into useful outputs congruent with the objectives of the system. Judging has two traits, Thinking (T) and Feeling (F). In Thinking, the individual connects ideas and experiences together by logic. Thinking is often an impersonal procedure. On the other hand, Feeling is a decision-making process that incorporates personal and group values. Comparing these two types in Table 1 reveals Thinking types tend to reflect logical and rational natures, while Feeling types are more idealistic and compassionate.
The Myers-Briggs Type Indicator (MBTI)
The Myers-Briggs Type Indicator (MBTI) is the primary psychometric instrument for measuring Jungian theory constructs and determining personality types. (6) The MBTI questionnaire is arranged in a forced-choice format. It has undergone numerous revisions since its initial publication in 1962. The MBTI versions used for research are Forms F, G, J and M, with Forms G and M still in use. In contrast to Form G (published 1977), Form M (published 1998) has updated wording, increased reliability and validity via pilot testing, and an improved scoring method. Form M consists of 93 questions and administration requires approximately 25 minutes (Myers et al. 1998). Form M is available in paper and computer formats from Consulting Psychologists Press (http://www.cppdb.com). (7)
The MBTI classifies each person into one of the 16 personality types by first identifying each individual's four preferences; i.e., whether the person prefers E or I, S or N, T or F, and J or P. The four preferences are then combined into the personality type via a four-way interaction. Thus, the test is primarily a sorting indicator that categorizes each participant into a personality type based on the results obtained from four bipolar scales. According to PTT, the four-way interaction is the most meaningful level of analysis. The importance of type, however, should not be taken as implying that the bipolar preferences are meaningless in isolation or that research cannot be done using the traits as noninteractive or continuous variables. (8)
Limitations of the MBTI
The MBTI has limitations. One limitation arises from Jung's (1921) hypothesis that preferences and types are innate and invariant in individuals. This hypothesis has received empirical support, with temporal stability studies spanning up to 50 years (Myers et al. 1998). This aspect of the personality restricts use of the MBTI; that is, the MBTI is not, for example, suitable for experiments looking for before and after treatment effects. However, theory does maintain that strength with which individuals prefer certain traits varies over time. The MBTI measures this aspect of personality with a preference clarity index (see Myers et al. 1998). Typically, preference clarity increases through adolescence, peaks during early adulthood and middle age, and declines again in seniors. Researchers can use the preference clarity index to identify variation within a preference (Jones 1994).
A second limitation of the MBTI is the bipolar nature of the scales. The MBTI captures the direction of a preference rather than its strength; i.e., the MBTI is appropriate for sorting, but it does not provide continuous measurement (Lawrence 1986). This characteristic, however, does not preclude the interpretation of MBTI scales as continuous measures (Myers et al. 1998). In fact, researchers have used such continuous interpretation in prior research (e.g., Jones 1994; Landry et al. 1996).
Third, PTT and the MBTI offer AIS researchers two general approaches for conducting investigations. Researchers may either use the whole type--the four-way combination of the preferred bipolar pairs (e.g., INTJ), or a single, two- or three-way combination of the traits or preferences (e.g., N, NT, or NTJ). Researchers can use the former approach to investigate the distribution of types in a sample or population. The studies noted in the next section examine the dominant traits, combination of traits, and types among accountants and accounting students.
The latter approach generally uses the individual traits or their two- or three-way combinations to investigate specific characteristics of individuals in relation to particular tasks or unique situations. For example, the two mental functions (NS and TF), either singly or in two-way combinations, can proxy for cognitive style, as in Chenhall and Morris (1991) and Vassen et al. (1993).
Finally, while researchers have established the MBTI's reliability and validity, and the instrument has been used by researchers, there is a risk that the instrument may not provide measurable personality differences in relation to other variables under investigation. This is true, of course, for any instrument--especially psychometric instruments that attempt to measure complex mental processes and phenomena along a small set of dimensions. Such instruments cannot measure nor do they claim to capture all aspects of mind or personality. Accordingly, MS researchers should consider whether the MBTI, compared to other psychometric instruments, is the best tool based on all of these considerations.
In the following sections, we will discuss in detail when the MBTI is an appropriate instrument to use in AIS research. At this point let us note, in general, that the MBTI should be used when the researcher is interested in investigating more than stimulus-response (or input-output) aspects of behavior; i.e., when the goal is to obtain an understanding of mental processes occurring between the stimulus-response (i.e., inside the black box). However, the MBTI is not appropriate in this regard if the researcher is investigating before and after differences in the individual's personality or mind since MBTI-measured traits tend not to change over time. Thus, the MBTI is best used when comparing differences among, but not within, individuals.
The MBTI is appropriate for cognitive research, as reflected in the TF and SN bipolar dimensions, because these dimensions can be used to directly measure cognitive style and problem-solving capabilities. However, the chances of finding measurable problem-solving or decision-making differences among individuals with personality differences are low when simple tasks are considered. However, the likelihood increases with task complexity because most complex tasks are solvable from several different approaches (i.e., complex problems encapsulate larger problem spaces that simple problems), thereby allowing for the emergence of individual differences. Similarly, the MBTI is most appropriate if the researcher is investigating the context in which tasks are being solved. For instance, the EI and JP bipolar dimensions allow the researcher to examine noncognitive aspects of problem-solving behavior.
Summary of MBTI-Based Accounting Research
Table 2 summarizes the results of over two decades of research in accounting practice and education using the MBTI. Overall, the research indicates a high degree of stability in the personality types of accounting participants, especially those working in the profession. Across time, location, and firm size, the STJ combination of preferences has remained dominant. Jacoby (1981) and Otte (1984), however, found differences in the personality types of accountants among functional specialties of firms and the profession. Further, results from Satava (1996) and Schloemer and Schloemer (1997) found that Extraversion is more prevalent in national firms than in local firms, and that this difference has increased over time.
Undergraduate accounting students appear to hold personality type preferences similar to those of accounting professionals and dissimilar to those of other college students (Laribee 1994). Wolk and Nikolai (1997) found differences among the preferences of accounting undergraduates, graduates, and faculty. Interestingly, undergraduate accounting students mirror the personality structures of accounting professionals more than graduate accounting students and accounting faculty. Laribee (1994) indicated that a filtering process may occur during undergraduate accounting education, which serves to decrease the E, N, F, and P preferences and increase the I, S, T and J preferences, with the final outcome being the "typical" accountant, i.e., STJ. Kovar et al. (2003) found over an eight-year period that those with S,T, and J preferences still dominate undergraduate accounting in terms of those who are both attracted to and retained in the accounting curriculum, despite the Accounting Education Change Commission's (AECC) initiatives for diversity. Kovar et al.'s (2003) results also suggest that the filtering process found by Laribee (1994) is still active in three of the bipolar dimensions (EI, SN, and JP).
Studies by Chenhall and Morris (1991) and Vassen et al. (1993) indicated significant correlations among preferences, decision making, and cognitive style in practitioner accountants. Cheng et al. (2003) found significant relationships between trait preferences and group decision making using undergraduate accounting students. However, studies in the relationship between preferences and academic performance in accounting were mixed. Nourayi and Cherry (1993) found a significant relationship between performance and the SN scale in accounting majors. A study by Oswick and Barber (1998), however, indicated no significant correlation between preferences and performance, although their sample included nonaccounting majors.
Ott et al. (1990) indicated that Intuition type accounting students reacted more favorably than Sensing type students to computer-assisted versus lecture-based teaching. Sensing type students, who are predominant in accounting, performed better with the lecture method. Similarly, the results of Landry et al. (1996) indicated that Intuition type students express less computer anxiety than Sensing types. However, the two studies offer conflicting results with regard to the relationship between computer usage and the TF-scale, although the two studies examined different aspects of computer usage.
IV. AIS RESEARCH OPPORTUNITIES USING THE MBTI
AIS Education Research Opportunities
One promising research area in AIS education relates to a finding by Laribee (1994); a filtering process seems to occur in accounting education that creates selection pressures toward I, S, T, and J preference students. Since some information technology (IT) skills appear to be stronger in non-STJ types than in STJ types (Landry et al. 1996; Ott et al. 1990), we suspect that this filtering might be harmful to AIS education because STJ types, who typically self-select into accounting, might not be favorably predisposed toward learning computer-based skills. However, it is possible that filtering processes occurring in AIS education are different than those observed by Laribee (1994) in non-AIS courses. Researchers can address these issues empirically.
We need to understand why filtering processes occur and what, if any, are the potential harmful effects on the profession. Do they result primarily from students' interaction with the content of courses, or from interaction with teaching styles? Attenuating or leveraging filtering may help provide the profession with the wider variety of skills needed in the profession's new environment (AICPA 1999).
Two studies involving undergraduate accounting courses arrived at conflicting results concerning the relationship of academic performance and personality (Nourayi and Cherry 1993; Oswick and Barber 1998). Nourayi and Cherry's (1993) findings indicated a significant relationship between personality traits and performance, while Oswick and Barber's (1998) results did not. This raises questions concerning the relationship between personality and performance in AIS-oriented courses. For example, does performance in AIS courses determine who pursues AIS, or are aspects of personality the primary determinant?
Learning styles vary among personality types and affect how people perform academically. The MBTI has been used to investigate learning style issues, such as written versus oral presentation modes, team versus individual learning preferences, concrete versus theoretical content, and structured versus open-ended problems (Myers et al. 1998). Our literature search revealed only two studies investigating accounting learning style issues that use the MBTI, and interestingly, both investigations involved aspects of AIS. Landry et al. (1996) investigated the concept of "computer phobia," and Ott et al. (1990) examined student receptiveness to lecture-style classes versus computer-assisted instruction. Research into the relationships between student preferences and learning styles can help AIS instructors design better curricula and teaching methods. For example, if AIS students have J-dominant preferences and as such prefer highly structured problems, then AIS instructors might minimize open-ended problems in the curriculum or provide the students with additional instruction in how to approach open-ended problems.
The relationship between teaching methods and learning styles is complicated by differences in the personality types of teachers and students. Wolk and Nikolai (1997) found differences among the personalities of undergraduate accounting students, graduate accounting students, and accounting faculty. This study did not include teaching method as a variable, although this variable would be a natural extension of the investigation. It is possible that instructors prefer to teach AIS in ways contrary to the learning styles of typical (STJ) accounting students. For example, accounting instructors tend to be N-dominant, preferring to teach using theories and abstractions, while accounting students tend to be S-dominant, preferring to learn using facts and examples. Accordingly, AIS research can shed light on how judicious use of the MBTI can improve our knowledge of AIS classroom dynamics by investigating interactions among AIS course content, AIS professors' teaching preferences, and accounting students' learning styles.
AIS Basic Research Opportunities
As a starting point for AIS researchers using the MBTI, we can compare the typical accounting student to the typical IS student. Prior studies in accounting show a persistence of STJ as the dominant type in the accounting profession. Nonaccounting MBTI research indicates that students attracted to IT or who possess higher than average computer skills tend to prefer distinctive personality traits that are more intuitive than sensing (Bishop-Clark and Wheeler 1994; Evans and Simkin 1989; Hester 1989; Jones 1994). Nonaccounting students in these categories also tend to reflect Thinking types, although some research suggests that Feeling types may be better at certain aspects of programming (Evans and Simkin 1989; Hester 1989). From non-MBTI streams of research, several AIS authors suggest that the skills of those engaged in accounting tasks that rely on IT differ from the traditional skills of accountants; i.e., skills not grounded in IT (Stone et al. 1996; Summers et al. 2000; Viator 2001). The former group appears to be more innovative, more abstract, less linear, and less fact-oriented than the latter. MBTI studies indicate that the latter have STJ preferences, which tend to inculcate mental processes that are predominantly concrete, linear, and fact-oriented.
The MBTI provides a means for investigating such issues from diverse streams of research. Since AIS spans both accounting and IT, two sets of research issues arise. The first set reflects demographics, which focus on the distribution of types and preferred traits among accountants engaged in AIS activities. The second set of issues is trait-oriented, which examines how effectively different types and traits perform AIS tasks. We consider this second area more promising than the type-distribution approach both in terms of contribution quality and topical relevance. To discuss this area in more detail, we divide the following section into three categories: cognition, affect, and motivation. This tripartite approach highlights the ability of PTT and the MBTI to empirically address issues.
Researchers can use the MBTI to investigate linkages between human cognition and AIS in several ways. First, there is a close relationship between PTT and the input/process/output/feedback model of cognition, which is foundational to systems theory and AIS. (9) Researchers regard the NS bipolar pair primarily in terms of input mental functions, while they describe the TF bipolar pair as processing mental functions (Myers et al. 1998). Second, the MBTI discrimination can serve as a proxy for cognitive style, which has long been an important research topic as reflected in our earlier discussion of Huber (1983). Third, the MBTI provides a means for investigating context or framing aspects of cognition--a central problem in cognitive science (Fodor 1987). The IE and JP bipolar pairs frame the predominantly NS and TF mental functions, adding irrational and noncomputational personality aspects to human cognition.
Some cognitive topics of interest to AIS researchers--hut investigated in non-AIS contexts using the MBTI--are: the input/process/output/feedback model of problem solving (Huitt 1992); modes of information presentation (Campbell and Kain 1991); perceptual style (Holsworth 1985); cognitive style and decision strategies (Hunt et al. 1989); the role of learning in structured decision making (Remus and Kotteman 1998); cognitive style as field dependence-independence and cognitive complexity (Carey et al. 1989); group decision making (Cheng et al. 2003; Daigle et al. 1999); and cognitive style and learning (Ruble and Cosier 1990). While this list is far from complete, it reveals the potential of the MBTI for the AIS researcher.
We are aware of only two cognition-based studies using the MBTI that relate to the task performance of professional accountants (Chenhall and Morris 1991; Vassen et al. 1993). These studies investigated auditors performing traditional (i.e., non-AIS) tasks, and used the SN and TF scales as noninteractive variables (i.e., as single traits). Certainly, other professional areas in accounting are open to research on the relationships among personality characteristics, task performance, and cognition; especially in light of the numerous recent changes in the tasks, responsibilities, and expectations of accountants in areas involving IT (e.g., assurance services, information technology consulting, enterprise risk services, and IT risk management). In particular, research in these areas using preference interactions and not just single traits would be valuable. We believe that MBTI-based research in the next two areas, affect and motivation, hold the greatest potential for AIS research.
While the cognitive aspects (i.e., the NS and TF mental functions) are at the core of personality theory, the two attitudinal aspects (the EI and JP dimensions) provide the framework in which cognition occurs. The four preferred traits from the four bipolar scales interact in such a way that the mental and cognitive aspects of personality become more than just computational processes. It is from this interactive combination that the personality as a whole becomes evident. Affect and motivation are two fundamental features of humans that distinguish them from purely computational entities such as computers (Damasio 1999).
Researchers distinguish affect from cognition and motivation by the presence of feeling, preference, emotion, intentionality, and belief. Preference is a primary part of personality theory in that PTT contends that each individual prefers one of the two traits present in each of the bipolar scales. The source of these preferences is not computational or utilitarian, but is innate. The TF dimension uses the trait "Feeling" to indicate a preference by the individual for including personal values and beliefs (as opposed to logic and pragmatism) in decision making.
The frame problem represents an example of how researchers may use the MBTI to investigate a pressing AIS issue. The frame problem arises from computational aspects of mind (Fodor 1987; Ketelaar and Todd 2001). Problem solvers face many difficulties, such as how to limit the large number of possible solutions that researchers need to explore for a given problem and how to anticipate the large number of possible consequences (particularly unintended) that can result from the potential solutions. Researchers can use framing to place the problem in context, and thus reduce the size of the solution-consequence matrix. However, framing problems arise when the decision maker excessively narrows the potential solution and diverts attention to inaccurate and misaligned attributions. Such problems can be further exacerbated when decision makers confront real-world problems that require swift action. Thus, the framing challenge is how to focus attention on relevant information and keep the information set small enough for the mind to work within a given time.
While the flame problem has many aspects (cognitive, affective, and motivational) like most AIS issues, herein we focus on affect. MBTI-based research may assist in investigating this issue in two ways. First, heuristics are one way of limiting the cognitive load when performing computations. The types of heuristics individuals use may vary among personality types. For example, Intuitive types seek patterns in inputs more so than Sensing types, and pattern-matching is an information-search strategy that may be effective in limiting the information set of a problem. Second, Ketelaar and Todd (2001) indicated that individuals use emotions to guide information search strategies and to focus mental computations on the information most useful for the problem. The MBTI suggests significant differences in the emotional make-up of various personality types, thus implying that emotional differences among personality types affect search strategies and limit computational loads.
AIS researchers might also investigate how non-AIS (AIS) individuals understand and project affect onto others who perform AIS (non-AIS) activities (e.g., how financial auditors feel about IT auditors or how IT auditors believe they are viewed by financial auditors). One example of how the MBTI can assist the AIS researcher investigating this area is related to attribution theory. Attribution theory delineates the process by which we attribute causes for personal actions, especially in others (Hewstone and Fincham 1996). It has been used in accounting research by Nouri et al. (1999). The MBTI has been used in investigations of attributions of this nature (Thomson and Martinko 1995). Thus, researchers might use the MBTI to provide measurements of our understanding of others, which would be of interest to the AIS researcher investigating individual, management or group aspects of the AIS environment.
Motivation and Morality Issues
Although the distinction between motivation and affect is unclear, motivating factors have an external quality that is lacking in affect. The former typically includes incentives, rewards, peer pressure, and ethics. Traditionally, AIS researchers have had a strong interest in motivating factors because the impact of external factors on observable behavior is the leading paradigm in behavioral research. In AIS research, motivating factors relate to broad issues like IT acceptance and usage and change management in systems implementation.
The importance of researchers devoting time to the study of ethical issues in accounting and AIS cannot be overestimated, particularly in light of recent events involving accounting scandals. While IT is morally neutral, the manner in which people apply technology can have moral implications. The morality of users determines the moral impact of IT and the conscientiousness with which an organization integrates IT into the organization, among other factors. Knowledge of trait preferences increases our understanding of why people prefer, value, and evaluate IT artifacts differently. Ackoff (1989) argued that the highest level of system understanding involves evaluative understanding--beyond knowing how to do things right lies knowing how to do the right things. Researchers have used the MBTI to capture moral aspects of individuals (Faucett et al. 1995, Otis and Quenk 1989), indicating that it may also be used by researchers investigating ethical issues in AIS.
Group research illustrates the potential of the MBTI for investigating motivation in AIS (Hammer and Huszczo 1996). (10) The AICPA's Vision Project (AICPA 1999) emphasizes an understanding of effective teamwork "within diverse, cross-functional teams" as a necessary skill set for the new accounting environment. Practitioners in AIS are typically involved in group tasks, such as systems design and implementation consulting. Beyond the problem-solving capability of groups (which may be examined via the distribution of mental functions and information among group members), there are also issues concerning the motivational dynamics of the group. For example, Extraverts and Introverts have different reactions to working with others (Daigle et al. 1999). Extraverts find the process energizing, while Introverts find it demanding. The differences between these two preferences also affect group communications, which in turn impact overall group performance. Similarly, another area open to MBTI-based research is the interplay of personality structures among clients and auditing/consulting groups. (11)
Practical Implications of MBTI Research
We have indicated some of the practical implications of MBTI-based AIS research. Here we summarize the more general points. First, personality theory contends that types and trait preferences do not change over time. Thus, even if research uncovers correlations among personality factors and various AIS issues, the question arises as to why this is of interest--beyond academic curiosity-since we cannot change the personalities involved. However, while types and traits remain relatively stable, the level of awareness the individual has of the nature of their type and trait structure can change over time, as indicated by the preference clarity index in the MBTI. Individuals can learn the strengths and weakness of their preferences. For example, they may learn how to effectively use their nonpreferred traits and how to leverage their preferred traits.
The predominance of a trait does not carry with it, according to theory, any value judgment as to whether the trait is good or bad, or whether the trait is used effectively. A person who is predominantly an Introvert may be either effective or ineffective with this trait. Thus, for example, research can find ways for an individual to be more effective as an Introvert, understand and deal with weaknesses associated with this trait, and exhibit extroversion under appropriate conditions.
One of the strongest findings in MBTI-based accounting research is the homogeneity of personality types attracted to and retained in accounting (Kovar et al. 2003). We expect the same is true, by extension, of AIS, although this issue has not been empirically investigated. As Kovar et al. (2003) observed, this homogeneity is especially disturbing in light of the AECC's emphasize on the need for diversity in the profession. The need for diversity is quite relevant to AIS due to the requirement for skills not typical of other accounting specialties. But beyond knowing who selects into accounting and AIS, we need to know why. The MBTI can be used to shed light on these issues.
Groups can learn how to design and more effectively integrate collective decision processes via PTT and the MBTI. Additionally, because groups are composed of individuals who can be added or subtracted during the life cycle of the group, practical possibilities arise that are not available to the individual. For example, Cheng et al.'s (2003) finding that groups with mixed S and N preferences outperformed groups consisting solely of S-preference individuals (but not solely of N-preference individuals) suggests such an MBTI-based approach to forming groups. Similarly, a group of all Introverts may need the presence of an Extravert to foster communication; a group of all Feeling types may need some Thinking types to provide more logical structure to decision-making processes.
Finally, organizations sometimes use the MBTI to evaluate people for jobs and tasks. Employers should use this procedure with care, especially considering that trait preferences do not imply competence with a trait. Interestingly, Myers et al. (1998) caution against using the MBTI for job placement. Nevertheless, the assumption in many applications of the MBTI is that, on average, certain traits correlate with better performance with certain jobs, tasks, and technologies. This area also is worth further investigation in AIS.
Extant behavioral AIS research is dominated by the cognitive science paradigm. However, while cognitive science is the predominate paradigm in psychology, a growing number of academics question the extent to which mind and behavior are fully explained by cognition. Personality type theory (PTT), which is rooted in the work of Jung (1921), reflects an alternate perspective that researchers can use in conjunction with cognitive science. PTT and a related measurement instrument (Myers-Briggs Type Indicator [MBTI]) can be valuable in a variety of AIS research investigations, particularly in the area of cognitive style.
In this paper, we suggest many ways in which PTT is applicable to AIS educators, professionals, and researchers. For instance, there are a variety of pedagogical issues regarding the types of students who gravitate toward AIS, the nature of faculty who teach AIS, and the instructional tools and methods we integrate into AIS education. There are also many practical implications associated with personality types, such as how effectively AIS graduates perform their professional tasks, how easily they work with others in collective decision-making environments, and how successfully they integrate into different accounting and organizational cultures.
There are numerous future research opportunities as well that can rely on PTT and use the MBTI. In particular, PTT can help to further understand the complex interrelated tripartite of cognition, affect, and motivation--especially the latter two constructs. Some of the issues that AIS researchers can examine include: obtaining deeper insight into the intricacies of human-machine interactions, understanding the impacts of mood and affect on cognition and behavior, aligning motivational incentives with personality types, developing more effective group decision support systems, and directing discretionary user interface choices. As illustrated, PTT and the MBTI are appropriate to a wide variety of issues in AIS.
Huber (1983) suggested that inadequate theory development, unreliable measurement instruments, and poor research designs are among the problems that hamper cognitive style research. We suggest that researchers can overcome these objections to some extent by grounding such studies in PTT and using the MBTI instrument. While researchers in nonaccounting domains have touted the value of PTT in this regard and have incorporated the MBTI into many of their studies, the amount of published MBTI-based research in accounting is very small by comparison. Although PTT and the MBTI have restrictive limitations, they nevertheless offer the AIS researcher many opportunities; especially since PTT subsumes an underlying human processing model similar to the input/process/ output/feedback model prevalent in information systems research, and provides a contextual frame in which to embed information-processing-based AIS research. While we acknowledge that cognitive science and PTT do not fully explain the human mind, we suggest that complementary strengths offered by both perspectives help to create a multidimensional map of the mind that cannot be represented by either viewpoint in isolation.
TABLE 1 The 16 Personality Types with Cognitive Characteristics and Occupational Tendencies Sensing (S) Thinking (T) Feeling (F) Judging ISTJ ISFJ (J) Practical, sensible, Practical, concrete, decisive, logical, cooperative, sensi- detached. Input- tive. Input-oriented. oriented. Management Education, health and administration. care, and religion. Introver- sion (I) Perceiving ISTP ISFP (P) Detached, logical Trusting, kind, problem solvers, sensitive, observant, pragmatic, factual. practical, concrete. Process-oriented. Process-oriented. Skilled trades Health care and and technical business. fields. Perceiving ESTP ESFP (P) Observant, active, Observant, specific, rational problem active, sympathetic, solvers, assertive. idealistic, warm. Input-oriented. Input-oriented. Marketing, business, Health care and skilled trades. and teaching. Extra- version (E) Judging (J) ESTJ ESFJ Logical, decisive, Factual, personable, objectively critical, cooperative, prac- practical, systematic. tical, decisive. Process-oriented. Process-oriented. Management and Education, health administration. cart, and religion. Intuition (N) Feeling (F) Thinking (T) Judging INFJ INTJ (J) Insightful, symbolic, Insightful, long- idealistic, committed, range thinkers, compassionate. Input- clear, rational, oriented. Religion, detached. Input- counseling, and oriented. Science, teaching. computers, and technical fields. Introver sion (I) Perceiving INFP INTP (P) Sensitive, caring, Logical, curious, idealistic, curious, detached, insightful, creative, visionary. contemplative. Process-oriented. Process-oriented. Counseling, writing, Scientific and and arts. technical fields. Perceiving ENFP ENTP (P) Curious, creative, Creative, imagi- energetic, friendly, native, theoretical, cooperative, warm. analytical, rational, Input-oriented. questioning. Input- Counseling, oriented. Science, religion, and management, and teaching. technology. Extro- version (E) Judging (J) ENFJ ENTJ Compassionate, loyal, Analytical, asser- imaginative, likes tive, conceptual variety, supportive. thinkers, innovation Process-oriented. planners. Process- Arts, religion, oriented. Management and teaching. and leadership. Cognitive characteristics are shown in regular font style; occupational tendencies are shown in italics. Source: Adapted from Wheeler (2001, 128). TABLE 2 Accounting Research Using the MBTI Panel A: Professional Accountants Reference Sample Shackleton (1980) 91 accountants and financial managers Jacoby (1981) 333 accountants in three Big 8 firms Otte (1984) 494 CPAs in small, local firms Descouzis (1989) 36 H & R Block tax preparers Kreiser et al. (1990) 182 CPAs; 92 bank officers; 113 FEI members Chenhall and 64 middle- to senior-level managers Morris (1991) Vassen et al. (1993) 25 experienced auditors from three large firms in The Netherlands Scarbrough (1993) 255 mid-level Big 8 accountants Satava (1996) 439 CPAs in local and national firms Schloemer and 125 accountants from Big 6 and Schloemer (1997) large local firms Panel B: Research on Accounting Students and Faculty Reference Sample Ott et al. (1990) 89 undergraduate students taking first elementary accounting course Nourayi and 103 undergraduate accounting Cherry (1993) majors (completed first intermediate accounting course) Laribee (1994) 320 undergraduate students Landry et al. (1996) 88 junior- and senior-level students Wolk and 152 undergraduate students; Nikolai (1997) 94 graduate students; and 98 faculty Oswick and 344 U.K. undergraduate students Barber (1998) (completed introductory-level accounting course) Kovar et al. (2003) 3 samples consisting of 149, 161, and 150 undergraduate students from 1992 to 2000 Cheng et al. (2003) 94 3rd-year Australian undergraduate management accounting students Panel A: Professional Accountants Reference Findings Shackleton (1980) Subjects were predominantly STJ (38.5%). ISTJs were 25.3% and ESTJs were 13.2% of the sample. Introvert comprised 58% of the sample. Jacoby (1981) STJ was dominant (33.6%; ISTJ at 19.8%; ESTJ at 13.8%). Audit and tax partners and managers were more likely TJ than those at junior professional levels. Audit partners and managers were more likely is than partners and managers in tax and consulting. Otte (1984) CPAs in small local firms were primarily STJ (45.9%). Descouzis (1989) 100% of tax preparers were Sensing type. 58% were Introverts; 81 % were Judging type; 44.4% were predominantly STJ. Kreiser et al. (1990) CPAs were perceived by the other professionals as primarily Introverts with STJ preference. These perceptions were stronger than actual percentages among CPAs. Chenhall and Intuition type managers included Morris (1991) opportunity cost in resource allocation decisions more often than Sensing types. Vassen et al. (1993) Subjects were primarily Sensing type (68%). There were more Thinking types (56%) but not significantly. Thinking types accessed more information, took longer to process information, and showed a lower tolerance for ambiguity. Scarbrough (1993) Judging types showed marginally greater job satisfaction. Gender was a significant factor, with females showing higher job satisfaction along several dimensions (I, T, and J scales, and ISTJ). Satava (1996) No differences on SN, TF, and JP scales between local and national firms. STJ was predominant both types of firms. Extroverts tended to prefer national firms, whereas Introverts tended to prefer local firms. Schloemer and CPAs were primarily STJ. Meta-analysis Schloemer (1997) indicated an increase in Extroverts over the past decade. Panel B: Research on Accounting Students and Faculty Reference Findings Ott et al. (1990) Sensing type and Thinking type students performed better with a lecture method. Intuition type and Feeling type students performed better with a computer- assisted method. Nourayi and No significant results except on the SN Cherry (1993) scale. Sensing type students performed better than Intuition type students in three of the seven accounting courses and in an overall accounting average score. Laribee (1994) Accounting students were predominantly STJ, which differentiated them from other undergraduate students. A filtering-out process caused E-, N-, F, and P-preferences to decline and I-, S-, T-, and J- preferences to increase. Landry et al. (1996) STJ was dominant (42%). Intuition type and Thinking type students showed less computer anxiety. Wolk and Undergraduate students were different Nikolai (1997) from graduate students and accounting faculty along several dimensions. Graduate students and faculty showed no significant differences. Oswick and No significant relationships between Barber (1998) preferences and performance were found. Kovar et al. (2003) Of the three samples, the S-preference constituted 80-86%, the T-preference constituted 58-64%, and the J-preference constituted 68-80%. The STJ combination was dominant in 38-46%. Cheng et al. (2003) Dyads consisting of S-preference and N-preference subjects outperformed dyads consisting of only S-preference subjects. However, no difference in performance was found between dyads consisting of S-preference and N-preference subjects and dyads consisting of only N-preference subjects. Source: Adapted from Wheeler (2001, 137-138) with additions.
Thanks to the editor (Dan Stone) and the anonymous Associate Editor and two reviewers whose insightful comments improved this paper.
(1) While some define cognition in a broad sense to include all mental processes, we use a more restrictive definition that limits cognition to thought (cogito) and the use and handling of knowledge. Accordingly, there are many mental processes, as listed in the text, that are not cognitive; i.e., that do not involve thought or knowledge. We use this restricted definition because it allows for distinctions useful for research.
(2) The IS field of human computer interaction (HCI), including human factors and engineering psychology, addresses this deficiency. However, HCI typically approaches the human aspect in terms of the information processing paradigm, thereby (1) failing to embed cognition and information processing in a larger context and (2) perpetuating a circular form of reasoning that addresses HCI issues by treating both the human and computer entities as essentially similar information processors. Even within these constraints, the accomplishments of HCI have been impressive.
(3) These interactions cause traits in the personality to be dominant, inferior, auxiliary, and tertiary. We recommend that the researcher develop a working knowledge of this aspect of personality theory before conducting research using the MBTI. See Pascal (1992) and Myers et al. (1998).
(4) Bipolar pairs are also referred to as dichotomies or dimensions in the literature.
(5) Preference and nonpreference may be demonstrated with left-handedness and right-handedness. Individuals show a preference for one hand over the other, which is not consciously chosen. Abilities with the preferred and nonpreferred hands vary greatly within and between individuals. For example, some individuals can use tools better with their nonpreferred hand than others with their preferred hand.
(6) There are several related instruments available; e.g., the Jungian Type Sorter, the Singer-Loomis Inventory, and the Keirsey Temperament Sorter. These instruments have not been as thoroughly tested for reliability and validity as the MBTI. See Karesh et al. (1994) and Wheeler et al. (2002).
(7) Form M of the MBTI is $80 for packages of 10, while Form G is $96 for packages of 10. Other packages for various organization sizes are available for both Form M and Form G. See http://www.cpp-db.com.
(8) Myers et al. (1998, 147, 149) provide a formula for converting MBTI scores to continuous scores.
(9) Nevertheless, the AIS researchers should be cautious in applying the input-process distinction too simplistically to the MBTI, as illustrated by the following remark: "After 30 years of research on type and management, we can draw the following conclusions: When it comes to decision making, all four functions (Sensing, Intuition, Thinking, and Feeling) may have some impact on all steps in the decision-making process; we should not assume that only the S-N dimension affects information gathering and only the T-F dimension affects information processing, as has been hypothesized" (Walck 1996, 71; italics added). This should not be read as implying that no results have been found in this area or that fruitful research cannot be done using this paradigm appropriately.
(10) AIS researchers may also examine cognition and affect aspects of group behavior using the MBTI. For example, researchers might investigate various combinations of cognitive styles among the group members performing different AIS tasks (decision support system use, database development and design, etc.).
(11) See Gardner and Martinko (1996); Hammer (1996); and Myers et al. (1998) for more examples of MBTI-based nonaccounting research on these topics.
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Patrick R. Wheeler
University of Missouri-Columbia
James E. Hunton
Stephanie M. Bryant
University of South Florida