Experiential Learning Potential as a Function of Psychological Predispositions and Demographic Variables Page 34 - Developments in Business Simulation and Experiential Learning, Volume 43, 2016 ABSTRACT This paper addresses experiential learning as a function of psy- chological predispositions and demographic variables. Litera- ture is lacking in these areas and factors have not been consid- ered in the research of experiential learning. We include an analysis of psychological factors such as regulatory focus, lo- cus of control, tolerance for ambiguity, need for structure, and narcissism and consider how these factors impact demographic variables in the context of experiential learning. Based on the literature and the logic that we provide, we suggest several are- as for future research, as well as providing propositions that we believe will uphold pending future research. Keywords: Experiential learning; psychological variables; demographic variables INTRODUCTION The pursuit of the benefits of experiential learning has gone on now for generations. ABSEL started in the early 1970’s and is in its fifth decade of researching, philosophizing about, and striving to refine experiential learning theories and practices. Kolb (1984) published his landmark book describing the Kolb experiential learning model 32 years ago. Myriad research pro- jects and countless applications of the Kolb model have prolif- erated over those 32 years. It is also possible to trace the evolu- tion of ABSEL scholarship over the years as well, and to see ABSEL based concepts proliferate. For example, the concept of whole person experiential learning has been an ABSEL main- stay conceptual framework, and recently appears in academic literature in applications in fields such as management develop- ment (MacGregor & Semler, 2012) and the study of family businesses (Barbera, Bernhard, Nacht & McCann, 2015). Despite the wide acceptance of the experiential learning approach in business education, areas of possible exploration and research remain. One area of possible research in experien- tial learning revolves around experiential learning potential as a function of psychological predispositions and demographic var- iables. As the study and application of the experiential learning approach has developed over the last several decades, it is also possible to observe that our universities and colleges have changed, sometimes dramatically. Along with systemic changes in higher education, the com- position of the targets of experiential learning, our student pop- ulation, has also changed. Certainly more women attend colleg- es and university classes, especially in business school settings, than they did decades ago. The number of international students and students with English as a second language also has flour- ished. As MBA programs have grown in size and in number, more mature students with prior work experience have found their way into our classrooms, especially in settings such as weekend MBA programs, executive programs and evening pro- grams. As our educational reach has expanded to include re- mote education and online classes, the student population has become more and more diverse as the online environment pro- vides access to higher education to a large number of student population segments that were possibly unreachable before. Furthermore, it is also time to recognize that the students in our 2016 classrooms come to the educational setting of the modern classroom not just with the characteristics of their unique generation, but also unique life experiences sourced in technology-based lives unprecedented in human history. The current generation of students has been labeled the Millennial Generation, but they could also be called the Google Search Generation. This is because they have grown up in a world not where internet search was simply a new option to embrace (as it was and continues to be for those who will be reading this pa- per). Rather, internet search has always existed for them. Stu- dents in our classrooms today have never known a world where virtually infinite information has not been available to them at the touch of a keystroke and now the touch of a screen. As a result of these phenomena and the challenges faced by ABSEL educators as we progress further into the 21st century, we feel that it is time to conduct new research into the psycho- logical predispositions of our current and future student popula- tions. Since new demographics are in play, new research should examine demographic variables not previously considered. Moreover, the psychological variables used in new research should look at variables that are more aligned with the realities of the information processing marvels that our modern students have become. This paper begins with an overview of several psychological measures that are a better fit for current research, followed by an examination of demographic variables that are more appropriate for the study of modern students, and con- cludes with sample propositions as examples of the types of Experiential Learning Potential as a Function of Psychological Predispositions and Demographic Variables Lori Tribble Texas Tech University Loril.tribble@ttu.edu J. Duane Hoover Texas Tech University Robert C. Giambatista Robert.giambatista@scranton.edu University of Scranton mailto:Loril.tribble@ttu.edu mailto:Robert.giambatista@scranton.edu Page 35 - Developments in Business Simulation and Experiential Learning, Volume 43, 2016 research questions that could be pursued. POSSIBLE PSYCHOLOGICAL MEASURES TO CONSIDER Since the new generation of students is more information centric, it could be productive to examine psychological predis- position measures that relate to the manner in which infor- mation is received, internalized, processed, and actualized by modern (Google Search) students. Some of the measures high- lighted here are relatively new measures (e.g. regulatory focus), while others are time tested measures that have not yet been held up to the light of their implications for Google Search stu- dent populations (e.g. locus of control). In the following sec- tions, we will explore these measures. Regulatory focus Regulatory focus theory (RFT) is a goal pursuit theory re- garding how individuals make decisions. RFT takes into ac- count the motivation of a person and how people self-regulate in order to achieve their desired goals. (Higgins, 1997). By self- regulate, we mean how people “monitor their own behaviors and make adjustments to those behaviors in the pursuit of goals” (Levy, 2010: 242). Drawing on the needs-based psycho- logical theories of Bowlby (1969), Maslow (1955), and Rogers (1960), Higgins (1997) posits there are two self-regulatory ori- entations in order to satisfy individual’s needs: prevention and promotion. While the prevention focus is related to safety and responsibility and the predominant interest is to follow the rules in order to maintain safety and responsibility, the promotion focus is more related to hopes and accomplishments with the main interest here being advancement (Higgins, 1997, 1998). Prevention focus allows individuals to avoid situations that would inhibit them from reaching their end goal, while promo- tion focus allows individuals to behave in ways that would move them closer to their end goal (Lanaj, Chang, & Johnson, 2012). For example, students who want to pass an exam can take a promotion focused approach (studying notes from the class) or a prevention focused approach (abstain from going out with friends before the exam). The 18-item Regulatory Focus Questionnaire (RFQ) was developed to measure an individual’s subjective history of their promotion or prevention success in goal-attainment (Higgins, et al. 2001). The RFQ measures on both a promotion subscale and a prevention subscale. According to Higgins, et al. (2001:6), “higher scores on either the Promotion or Prevention subscale reflect individuals' sense of their history of promotion or pre- vention success in goal attainment”. The Regulatory Focus Questionnaire includes questions to measure prevention and promotion. Sample questionnaire items include “In general, I am focused on preventing negative events in my life”, and “I frequently imagine how I will achieve my hopes and aspira- tions.” The first example is a prevention focused statement and the second is a promotion focused statement. Locus of control Locus of control can be defined as the degree “to which people believe that they have control over their own fate” (Ng, Sorensen, & Eby, 2006: 1057). Rotter (1966) observes that peo- ple can either have an internal or external locus of control. He distinguishes between a person who perceives that rewards are contingent upon her own actions versus a person who perceives that rewards are controlled by some other factor, independent of her actions. According to Rotter (1966), the former person would have an internal locus of control, while the latter would have an external locus of control. The significance of locus of control has been tested in many scenarios. Yukl and Latham (1978) found that people with an internal locus of control set more difficult goals and have a stronger need to achieve these goals than people with an external locus of control. People with an internal locus of control have been reported to have more ability to delay gratification, perceive opportunities, and apply more effort to work tasks (Phares, 1976; Lefcourt, 1976; Ko- berg, Boss, Senjem, & Goodman, 1999). Phares (1957) was the first researcher to try to measure locus of control. He used a Likert scale with 13 items measuring external attitudes and 13 items measuring internal attitudes. However, this test was later broadened and transformed in order to show higher reliability and validity (Rotter, 1966). Rotter’s scale is the most common measure of locus of control. The scale directs participants to choose which statement is a more accurate reflection of their beliefs. For example, a participant would be asked to choose which of the following statements most accurately reflect their beliefs: (a) “Many of the unhappy things in people’s lives are partly due to bad luck.”, or (b) “People’s misfortunes result from the mistakes they make.” Participants who choose the first statement would be classified as having an external locus of control, while participants who choose the second statement would be classified as having an internal locus of control. Tolerance for ambiguity Tolerance for ambiguity is a construct that has been studied since the 40’s (Frenkel-Brunswik, 1949). Frenkel-Brunswik (1949: 115) described intolerance to ambiguity as a “tendency to resort to black-white solutions, to arrive at premature closure as to evaluative aspects, often at the neglect of reality, and to seek for unqualified and unambiguous overall acceptance and rejection of other people.” Budner (1962) described tolerance of ambiguity as the tendency for a person to see a stimuli as desir- able, while intolerance of ambiguity would be seen as threaten- ing. Individuals can either see unfamiliar stimuli in an open way or in a threatening way. When people see these ambiguous stimuli in a more open way, they are said to have a high toler- ance for ambiguity. These individuals see these stimuli as chal- lenging and interesting, instead of threatening. In contrast, indi- viduals who view the stimuli in a threatening way are said to have a low tolerance for ambiguity. These individuals experi- ence stress and try to avoid the perceived threatening stimuli. Many have tried to measure tolerance for ambiguity, but these tests suffered from low reliability and validity (Martin & Westie, 1959; Budner, 1962, Rydel & Rosen, 1966). Budner’s test for tolerance for ambiguity (1962) consists of 16 statements that a participant would rate from strongly disagree to strongly agree. For example, a person with high tolerance for ambiguity would most likely strongly agree with the following statement: “It is more fun to tackle a complicated problem than to solve a simple one.” On the other hand, a participant with a low toler- ance for ambiguity would most likely strongly agree with the following statement: “What we are used to is always preferable to what is unfamiliar.” Personal need for structure People have a basic need for structure, especially if the work is complex or there are difficulties associated with pro- cessing an abundance of information at once. Because of this Page 36 - Developments in Business Simulation and Experiential Learning, Volume 43, 2016 information challenge, people may try to reduce the amount of information they actually have to process in order to make this information processing simpler (Neuberg & Newsom, 1993). Using this simple structuring of information has been positively associated with attention, memory, and interpretation of infor- mation (Fiske & Taylor, 1991; Higgins & Bargh, 1987). Ac- cording to Neuberg and Newsom (1993), people differ in their motive to structure their worlds in simple ways. Personal need for structure refers to “those individual differences in the degree to which a simplified structure is actively made and ap- plied” (Wiebenga, 2006: 10). When individuals have a high need for structure, they are more likely to avoid complex struc- tures and to seek out situations that dictate thinking that is more straightforward, because they do not function well with this type of unstructured complexity. When individuals have a low need for structure, they typically are more willing to use com- plex structures in more complicated ways. Neuberg and Newsom (1993) developed a scale for meas- uring personal need for structure. It measures individual differ- ences in desire for high structure or desire for low structure. The scale asks participants to rate statements on how strongly they agree or disagree with the particular statement. For example, people with high need for structure would rate the following question as “strongly agree”: “It upsets me to go into a situation without knowing what I can expect from it.” People with low need for structure would rate the following question as “strongly agree”: “I am not bothered by things that interrupt my daily routine.” SOME RELEVANT DEMOGRAPHICS AND VARIABLES FOR CONSIDERATION Individual differences in regulatory focus have been stud- ied extensively. It has been shown by many researchers that culture plays a large role in an individual’s regulatory focus. Researchers have shown that people from non-western cultures tend to have a more prevention regulatory focus, while people from western cultures, such as the United States and Western Europe, have a more promotion regulatory focus (Lalwani, Shrum, & Chiu, 2009; Elliot, Chirkov, Kim, & Sheldon, 2001; Lee, Aaker, & Gardner, 2000; Hamilton & Biehal, 2005). Research has shown that the type of regulatory focus a per- son has influences their goal-attainment process. Shah, Higgins, & Friedman (1998) found that people with a more promotion regulatory focus are focused on accomplishments and are there- fore interested in achieving goals as a source of attainment and accomplishment. Prevention regulatory focused individuals frame their goals in terms of safety and fulfillment of obliga- tions. Therefore, a prevention-focused person would be less interested in accomplishing and excelling in their goals, and would instead be interested in simply not failing. Based on this research, one would expect there to be significant differences between western and non-western cultures in the behavioral benefits a person would receive from experiential learning. This would be an interesting aspect to take into consideration of the design of experiential learning for different student populations, and to take into account in future research studies. There are also many cultural differences in an individual’s locus of control. According to Smith, Trompenaars, and Dugan (1995), western and non-western cultures differ in Rotter’s lo- cus of control scale with non-western cultures having a more external locus of control and western cultures having a more internal locus of control. Intensifying the research of the indi- vidual differences of culture in locus of control have been stud- ies regarding African Americans as compared to white Ameri- cans in the United States. Studies have shown that African Americans and white Americans differ in their locus of control, with African Americans having a more external locus of control and white Americans having a more internal locus of control (Barroso et al. 2000; Tashakkori & Thompson, 1991; Cox & Nkomo, 1991). Having an external locus of control has been related to learned helplessness and passivity (Rotter, 1992). Having con- trol over one’s environment has previously been shown to be related to one’s self-efficacy (Wood & Bandura, 1989), which led Phillips and Gully (1997) to purport and show evidence for internal locus of control being positively related to increased self-efficacy. Self-efficacy also plays an extremely important role in engagement (Linnenbrink & Pintrich, 2003). In addition, Hoover (2014) states that this self-efficacy must be reality- based. This is one of the reasons why self-efficacy has been related to experiential learning success (Ng, Van Dyne, & Ang, 2009). Because of the preceding logic, it would be interesting to see the differences in success of experiential learning based on one’s locus of control. One would expect that international stu- dents and African American students would receive less behav- ioral benefits from experiential learning based on their external locus of control. However, this has not been previously studied, and the field would greatly benefit from more knowledge in this area. Need for structure has been shown to have great cultur- al differences. Hofstede (1989) classifies these differences based on how avoidant of uncertainty certain cultures are. For example, Korea and Japan are classified as having very strong uncertainty avoidance, while Great Britain and the United States are classified as having very weak uncertainty avoidance (Hofstede, 1989: 393). This essentially means that countries like Great Britain and the United States are more uncomfortable with firm rules, while places like Japan and Korea are extreme- ly comfortable with firm rules. Hofstede (1989) states that this determines a country’s need for structure. In the experiential learning field, it is purported that that there must be ambiguity in the experiential learning so that the learners can apply their knowledge to the specific situation (Dennehy, Sims, & Collins, 1998). This could be a problem for individuals from a culture with a high need for structure. It would be interesting for future researchers to assess if cultures with higher need for structure would actually receive less benefit from experiential learning, as the previous logic would seem to imply. Tolerance of ambiguity has been shown to vary among individuals based on their work experience. For example, Ly- sonski (1985) suggested that experienced product managers would have a higher tolerance of ambiguity. This suggestion stems from more experienced learners being able to apply them- selves more fully to novel exercises. A person’s work experi- ence has not been extensively studied in combination with their tolerance for ambiguity. Though Lysonski (1985) suggests that more experienced property managers would have a higher toler- ance for ambiguity, he did not specifically test this assertion. Furthermore, tolerance for ambiguity with individual differ- ences in work experience, has not be studied with the benefits of experiential learning. Would an experienced person fare bet- ter with experiential learning? It would be interesting to test if students with more work experience have an increased tolerance for ambiguity and then test the implications for experiential learning. Since high tolerance for ambiguity has been associated with high performance (Schwenk, 1982), one would think that students with more work experience would receive more bene- fits from experiential learning. Page 37 - Developments in Business Simulation and Experiential Learning, Volume 43, 2016 Finally, narcissism could also have many individual differ- ences when applied to experiential learning. For example, age has been studied in connection with narcissism. Westerman, Bergman, Bergman, & Daly (2010) found that Millennial stu- dents (born from 1977 to 2000) have higher levels of narcissism that past college students. In addition, Westerman et al. (2010) found that student that were business majors exhibited higher narcissism than students that were psychology majors. It would be interesting to research the differences between different ma- jors and those effects on experiential learning. Foster, Campbell, & Twenge, (2003) found a wide variety of individual differences in narcissism, including decreased narcissism with increased age, increased narcissism among males, increased narcissism for African Americans, and in- creased narcissism in more Western cultures. These findings have significant implications for experiential learning. Since experiential learning may require trial and error, it would seem that narcissism would dilute the positive benefits a student would receive from experiential learning. However, these indi- vidual differences connecting narcissism and experiential learn- ing have not been extensively studied. Furthermore, gender dif- ferences in experiential learning has not been addressed at all. It would benefit the field of experiential learning to actually study these effects and see what differences there actually may be. In addition to psychological constructs, demographic indi- vidual difference variables might play a role in experiential learning efficacy. For example, gender was identified as a mod- erator of social loafing’s effect size (Karau & Williams, 1995). Specifically, men were found to engage in greater loafing gen- erally and the authors attributed this to men being more strate- gic in allocating effort to work, and therefore more likely to refrain from effortful engagement if they do not see the benefit to themselves. Furthermore, gender-based socialization and role stereotyping has been associated with motivation and direc- tion of effort (Shashaani, 1993; Eccles, Jacobs, & Harold, 1990). Experiential learning in management is a soft skill and an implicit mental model many students would be likely to hold would be one of “playing nice.” As these attributes are closer to traditional female socialization than to male socialization, men would be more likely than women to be reluctant to fully engage in experiential learning activities. Men might also be more likely to perceive such activities as “blow off” or non- value adding, and following from Karau & Williams (1995), less likely to legitimize the activity itself. Ethnicity or nation of origin could also be correlated with experiential learning. EL activities and the pedagogies that sup- port them are relatively common in the U.S. but still uncommon in many nations and cultures. Students who come to the U.S. for their undergraduate or graduate business education are al- ready dealing with various linguistic and cultural challenges requiring time and effort to master. These challenges are, of course, above and beyond the learning challenges facing all students. When pedagogical novelty and complexity are added to the mix, cognitive overload, confusion, and alienation are likely as possible outcomes. Finally, age or work experience is likely to be correlated with experiential learning. Students who come into a learning environment with greater personal history and a wider array of experiences are generally going to relate to behavioral simula- tions and role plays more readily than traditional and younger students. One reason could be simply the way such simulations are concocted is frequently based in re-creating everyday work experiences. Such scenarios are more familiar to older and more experienced students. Beyond familiarity, the impact of such situations on their careers is likely to be more established, so the significance and importance of such activities does not need to be “sold” to them. PROPOSITIONS Based on the literature and arguments presented above, we propose the following: Proposition 1: Individuals with a promotion regulatory focus will show greater experiential learning out- comes. Proposition 2: Individuals with an internal locus of control will show greater experiential learning out- comes. Proposition 3: Individuals with a high tolerance for ambiguity will show greater experiential learning out- comes. Proposition 4: Individuals with a low need for structure will show greater experiential learning outcomes. Proposition 5: Individuals with lower narcissism will show greater experiential learning outcomes. Proposition 6: Females will show greater experiential learning outcomes. Proposition 7: Compared to students from nations from non- Western and non-English speaking cultures, U.S. students will show greater experiential learning outcomes. Proposition 8: Older students will show greater experiential learning outcomes. Proposition 9: Individuals with more work experience will show greater experiential learning outcomes. DISCUSSION Experiential learning in management is widely perceived as an area requiring more in-depth research (Trank & Rynes, 2003). For example, experiential learning, while widely be- lieved to be an important tool for management educators, has not yet been conclusively determined to increase learning out- comes, though some studies have been conducted that indicate it does (Hoover et al., 2010). While it may be somewhat prem- ature to extend the literature to consider individual difference variables, we believe the field’s general lagging behind practice and the beliefs of educators is a temporary condition. Further, individual differences and related constructs are likely to impact the efficacy of experiential learning activities and pedagogies. Increasing our understanding of which constructs matter and to what extent will provide a valuable set of tools for experiential- ly prone educators. Implications for practice would include the application of self-assessment into experientially-based classes. Consequently, students will be more aware of which personal factors might enhance or inhibit their learning and, if the latter is the case, educators or students can then take corrective action. For ex- ample, if men are found to be less likely to be motivated to en- gage in EL, an educator might consider group composition and try to avoid all-male groups, or to pair males not only with fe- males, but also non-traditional students who are more likely to see the learning value of such exercises and pedagogies. Inter- national students could be given greater coaching or support before and during exercises. A class whose enrollment is domi- nated by international students might benefit from leveraging less experiential content, or changing the nature of the activity from role-play to something more passive like discussion of Page 38 - Developments in Business Simulation and Experiential Learning, Volume 43, 2016 film clips. As to the psychological constructs, self-assessment could be combined with reflection and in-class discussion specifically addressing the learning implications for a given construct and how to engage in behavior to offset or cope with some of the problematic aspects. In general, an experientially-based peda- gogy might be well-suited to dovetail with a skills-based peda- gogy such as that offered by Whetten and Cameron (2011). 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