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HPT ARTICLE of INTEREST The Cane Model of Work Motivation: By: Dr. Richard E. Clark University of Southern California Why is enthusiastic commitment to work goals so difficult to achieve, even when we pay people well? Why is it difficult, and sometimes impossible, to convince people at work to chose and actively persist at vital workgoals when they encounter distracting and less important alternatives? Is the motivation of "knowledge work" similar to the motivation of "physical work"? Why is it that people often fail to invest enough effort to achieve the knowledge goals they believe to be important to them and to their organizations? These are the questions that trouble psychologists and the managers of modern organizations. The goal of this discussion is to attempt to answer these and other questions about motivation at work by describing a general cognitive motivation model which, if adopted at work, may help achieve work goals (Graham & Weiner, 1996). For the purposes of this discussion, motivation is defined as "...the process whereby goal-directed activity is instigated and sustained" (Pintrich & Schunk, 1996, p. 4). Motivation is also concerned with the amount and quality of the "mental effort" people invest in achieving goals. Mental effort is defined as "the number of non-automatic elaborations necessary to solve a problem"(Salomon, 1984, p. 231). These two elements of motivation, active and sustained goal pursuit on the one hand, and mental effort, on the other hand, are the primary outcomes investigated in motivation research (Pintrich & Schunk, 1996). Mental effort is the engine for the "knowledge work" that is the primary source of the new products and services that support large and small business and government organizations (Caisco, 1995). The Value of Increased Motivation at Work The value of increasing motivation in work settings is obvious to most managers. If employees were strongly committed to organizational goals and investing mindful effort to achieve those goals, many of the most difficult challenges facing organizational managers would be eliminated. Pay schemes simply are not enough to produce the level of motivation needed to compete (Cascio, 1995). One of the primary tasks for most organizations is to select managers who are able to motivate the people who work in them. In their review of the research on organizational leadership effectiveness, Hogan, Curphy and Hogan (1994) provide evidence that the most effective managers are able to foster strong commitment to work goals. They also suggest that only about 30 to 40 percent of managers in modern organizations are even marginally effective as motivators. We have no estimates of the cost of motivation problems at work but Gilbert (1996) estimates that increases in work motivation have increased worker productivity from 30 percent to 70 percent. Motivational studies have found that cognitive motivation techniques account for between 12% (Helmke, 1987) and 38% (Fyans & Maehr, 1987) of the variance in performance improvement on academic tasks. Since much of the work accomplished in modern organizations is "knowledge work", motivation to persist and invest intelligent effort in the achievement of organizational goals accounts for a significant portion of the cost and profit of business organizations and the efficiency of government organizations. Why Have We Not Used Cognitive Motivation Theory for Work? One of the major problems with the past two decades of cognitive motivation research is that it has largely failed to find it’s way into practice at work. With a few exceptions, most practice-based models for enhancing work motivation are based on behavioral research conducted over 50 years ago (Graham & Weiner, 1996) or on craft-based approaches such as "employee empowerment" or "quality circles" that are not research tested or theory based. A number of the current best-selling books in English on work motivation cite only behavioral studies conducted in the 50's and 60's and "best practice" examples but contain few research references after the 1970's. By way of explanation, Offermann & Gowing (1990) suggest that most Organizational Psychologists have been trained in older behavioral theories and are largely unaware of the newer cognitive research on motivation. They also point out that motivation psychologists are generally not skilled at "speaking the language of business" (1990, p. 104). Part of this communication problem is our inability to focus on the necessary procedures for diagnosing motivation problems and designing and evaluating motivational interventions at work. We also tend to ignore the very important issue of the real costs and benefits of using modern cognitive models of motivation to diagnose and solve performance problems at work. Another problem common to most work settings is the strong inclination of managers to avoid deep analysis of performance problems and to solve shallowly analyzed problems with "training", even if the problem is not caused by a lack of knowledge (Stolovitch and Keeps, 1992). Another major cause of our reluctance to apply cognitive theory to work settings is that most of the cognitive motivation studies have been conducted with college age adults or children studying in school contexts. Managers seem to primarily value studies conducted in work settings and are reluctant to generalize the results of academic research to similar contexts. Gagne and Medsker (1996) suggest that this reluctance to accept school-based motivation research is inconsistent. They note that most of the training strategies used at work have been directly derived from research on instruction and learning conducted in schools. Cook & Campbell’s (1979) discussion of external validity suggests that if there is no compelling reason to suspect that young adults in academic settings are motivated differently than older adults in work settings, a cautious generalization is acceptable. In fact, most of the research on developmental differences in motivation (e.g. Heckhausen and Schulz, 1995) and social psychology (Brown, 1991; Erez & Earley, 1993) indicates that age and culture differences do exist in the areas of motivational intensity, attributions for success and failure, strategies for achieving control and in the development of specific values. However, there appears to be no compelling evidence that motivational differences between younger and older adults require age-based theoretical models.. Research barriers to employing cognitive motivation research for any purpose were described a decade ago by D'Wdewalle (1987) and earlier by Norman (1981). Both of these researchers concluded that motivation research is largely conducted in laboratory settings where constructs "multiply more rapidly than ever, but they all manage to look remarkably similar" (D'Wdewalle, 1987, p. 193) and where self-report measures dominate experiments. Nearly a decade after D’Wdewalle’s and Norman’s complaints, Graham & Weiner (1996) report a continuing concern with "the theoretical overlap between constructs" (p. 80). They advise an increased concern with research models that help us make "motivation change" for individuals and groups. Weiner, 1990) has called for more general models and have voiced concerns about the construct validity of a number of motivation variables. For a variety of reasons, very little research is conducted in work settings. And when research evidence for motivation programs is gathered, it is not often reported because of fears that such information would provide business competitors with valuable information or subject managers of government offices to criticism. The general thrust of these critical reviews of motivation research is supportive of the development of general models of motivation that can be applied at work. How should motivation be implemented at work? Modern, multi-national organizations applying cognitive models of motivation (e.g. Hewlett Packard Co., AT&T, Motorola, Wells Fargo Bank, The European Patent Office) have relied heavily on application models presented by Stolovitch and Keeps (1992). One very large multi-national high technology company has informally reported benefits of "12 to 50 times cost" when using the approach (J. Fuller, Personal Communication). Stolovitch and Keeps (1992) have estimated that the benefit of training interventions in modern organizations is approximately twelve percent of their cost. One of the reasons for this very low return on training investments is the fact that many training programs are inappropriately designed and applied to motivation or organizational problems (rather than to knowledge problems). Stolovitch and Keeps (1992) recommend a "human performance technology" approach to reduce the waste and increase the impact of training and motivational solutions to organizational problems. Their approach begins with a list of specific and measurable business or organizational goals. A current measure of goal achievement is analyzed (e.g. "Which of these goals is achieved, which remain to be achieved?"). Analysis of three possible causes for failure to achieve a business goal is conducted. The Human Performance system draws heavily on Gilbert (1996), Rummler and Brache (1996) and Harless (1995) who suggest that all performance requires three primary factors: 1)Knowledge which can be presented in training or "hired" with new employees; 2) Organizational policy and procedures which must be analyzed to see if they support or are barriers to business goals; and 3) the motivation of employees to pursue work goals with appropriate mental effort. Most organizational goals require the systemic and systematic support of all three factors. A number of organizations interested in applying motivational models have generally adopted the "human performance technology" approach with considerable success. Yet the availability of current, cognitively-based models of work motivation is limited. Origins of the CANE Motivation Model: Research Indexes What performance problems do motivation strategies attempt to solve? The model that is presented here derives, in part, from an analysis of motivation research by Pintrich & Schunk (1996); from the Motivational Systems Theory (MST) proposed by Martin Ford (1992) and from recent work on cognitive effort by Bandura (1997) and Salomon (1984) among others. Pintrich and Schunk (1996) have suggested that our diverse body of motivation research tends to focus on a number of "indexes" or outcomes. These indexes are the problems that motivation researchers are attempting to understand and solve. Examples of these outcomes are goal choice (the passive and active selection of work goals), commitment (persistence at a work goal over time in the face of distractions), mental effort (employing conscious, non-automatic cognitive strategies to facilitate goal achievement) and performance (measures of goal or task success). All of these indexes have, at one time or another, been used to define motivation at work and to define the variables examined in motivation research. Since goal commitment and mental effort seem to be the key motivational issues in most work settings, the theoretical model chosen for this discussion is called the CANE (Commitment And Necessary Effort) model. Stages in Motivated Behavior: In the CANE model, two stages of motivation are proposed. In the first stage of the process we decide to actively pursue a goal. In the second stage, we determine the amount of necessary effort required to achieve the goal we have chosen. Since the effort we invest is largely determined by our perceptions of our goal self efficacy, and since we cannot fully assess our self efficacy until we are actively pursuing a goal, effort decisions are hypothesized to follow goal commitment. These two stages of motivation appear to be influenced by different processes. Once a work goal has been chosen, and a level of mindful effort required to achieve the goal has been determined, it is likely that both commitment and effort are constantly reexamined as our perception of the conditions surrounding the goal evolve. The discussion turns next to a description of the factors that influence goal commitment in stage one and effort in stage two. Specific interventions that might be expected to increase commitment and effort will be suggested. Commitment to Work Goals While many researchers have examined the variables that influence task commitment (e.g. Bandura, 1997; Dweck & Leggett, 1988; Erez & Earley, 1993; Keller, 1987; Locke, 1990; Locke & Latham, 1990; Schwarzer, 1993) Martin Ford’s (1992) Motivational Systems Theory provides the most comprehensive and coherent view of the factors that influence task commitment and persistence at work. His theory seems to include the major findings of other researchers in this area. As a result of his analysis of 32 motivational theories and related research Ford (1992) indicates that there are three variables which, if taken together, appear to predict the strength of our commitment to a work goal. The three variables influencing work goal commitment are: 1) goal value (as we strengthen our belief that achievement of a work goal will increase our personal control or effectiveness, our commitment to the goal is hypothesized to increase); 2) emotions (positive emotions facilitate and negative emotions discourage goal commitment); and 3) personal agency (beliefs concerning the extent to which our ability and contextual factors will facilitate goal achievement - as our expected chances for success increase, goal commitment is also hypothesized to increase). The hypothesized relationship between the three variables is multiplicative. This implies that if the value of any one of the variables is zero or negative, goal commitment will not occur. Table 1 illustrates this part of the CANE model. Table 1
Values and Goal Commitment Many motivation researchers (e.g. Freedman and Lackey, 1991; Heckhausen and Schultz, 1995; Shapiro, Schwarz and Austin, 1996) share the implicit and explicit belief that the ability to gain and maintain a sense of personal and organizational control or effectiveness is the essential goal of all motivated behavior. These "expectancy-control" researchers assume that commitment behavior is rational (although not always logical or effective) in that persistence at a goal over time is based on an explicit or implicit analysis of the "control potential" value of a work goal. Studies in this area are based on the often implicit assumption that people act as if they can achieve control to the extent that they can accurately predict which of the many alternative commitments they face will enhance their success. While different individuals and cultures might adopt radically different preferred methods to achieve control, the value for control is thought to be one of the most dominant and crucial human "universals" (Brown, 1991). A few multi-national organizations currently use a motivational strategy based on control theory. The strategy is often called "employee empowerment". Table 2 presents a list of empowerment techniques found in Martin (1995) and contrasts them with older approaches to motivation. Essentially, "empowerment" consists of giving employees more control over how they do their job (but not control over what job they will do). Table
2 Employee Empowerment Changes in American Organizations*
*Excerpted by permission of the publisher from The Great Transition © James Martin. Published by AMACOM, A division of American Management Association, http:www.amanet.org. All rights reserved
Research and evaluation studies on empowerment strategies produce mixed results. In a review of the methodology of quality of work and empowerment research ( for example on quality circles), Golembiewski and Sun (1990) and Newman, Edwards and Raju (1989) found evidence for a "positive-finding bias". Their reviews of many empowerment experiments indicate that the most positive results come from the most defective evaluation designs. This suggests that the evaluators have a "bias" towards evaluation conditions that emphasize positive results but tend to ignore or downplay negative outcomes. Yet a review by Barrick and Alexander (1987) focused only on quality circles, did not find any bias and suggested that these interventions can be very powerful in some settings. More recent work by Roberts and Robertson (1992) has identified the type of evaluation errors made in empowerment (and other) organizational change studies. Their review suggests, among other things, that empowerment strategies may succeed very well with some groups and not with others. Some of these failures seem to bee due to cultural differences in the form of learned preferences for different control strategies. For example, in some cultures, gaining more control over how work is performed is viewed positively and leads to increased commitment to work goals. In these settings, being allowed to decide how work goals are achieved (rather than being told how to work by managers) apparently gives workers a sense of greater potential effectiveness and enhances their commitment to work. In other organizational and national cultures, allowing workers to circumvent their managers and decide how to do a job reduces their perception of control. In these settings, people apparently believe that effectiveness or control is best achieved by following the directions of competent, effective managers (Bandura, 1997). The increasingly diverse and multi-cultural background of workers in most modern multi-national organizations suggests that motivation specialists must exercise caution in generalizing the results of control theory interventions to different or mixed cultural settings. It appears likely that we must ask more basic questions about how we determine people’s control beliefs. What is valued as a control strategy by some workers (for example, workers whose cultural background leads them to exercise more direct and personal influence over how a job is performed from day to day) may be seen as reducing or eliminating control by others (for example, workers whose cultural background presses them to achieve control through complying with powerful managers who are perceive to make effective decisions). After determining a universal model for goal commitment, we must then "translate" the model to fit the actual values that we find in specific organizational cultures. Value Types, Measurement and Interventions There are diverse ways to measure values. Rokeach (1979, 1997) has developed standard measures of social values. Some of the best and most recent research on motivational values has been conducted by Eccles and Wigfield (1995) who have found compelling evidence for the impact of three different types of control values in educational settings: utility, interest, and importance. The first type of value, utility, is defined as the "usefulness of the task for individuals in terms of their future goals, including career goals...[and] is related more to the ends in the means-ends analysis of a task" (Pintrich & Schunk, 1996, p. 295). This implies that utility value is placed on goal outcomes or ends, but not on the means or process used to achieve the outcome. Utility value is the one used to justify a less desirable experience that is endured in order to achieve a more desirable end or result. The second type of value, interest, is defined as the enjoyment or intrinsic curiosity people experience when performing tasks that have subjective interest. The third type, importance, or attainment value, represents the significance to a person of doing well on a task because success confirms their own beliefs about their skill levels. All three of these types of values contribute to our estimate of the control potential of commitments. Eccles and Wigfield (1995) have subjected the tests of these value types to confirmatory factor analysis and have used them successfully to predict goal commitment in a number of studies. Choosing or Assigning Goals An issue related to control values and employee empowerment concerns the methods of assigning goals or allowing employees to choose goals at work. Since most organizations cannot permit employees to select their own work goals, will assigned or forced work goals reduce commitment? Locke and Latham’s (1990) studies have provided evidence that employees do not have to participate in work goal setting in order to make a strong commitment to assigned work goals. In cases where participatory goal setting is not possible, they find that value for the goal is enhanced if people perceive the goal to be: 1) assigned by a legitimate, trusted authority with an "inspiring vision" that reflects a "convincing rationale" for the goal, and who; 2) provides expectation of outstanding performance and who gives: 3) "ownership" to individuals and teams for specific tasks; 4) expresses confidence in individual and team capabilities while; 5) providing feedback on progress that includes recognition for success and supportive but corrective suggestions for mistakes. Emotion and Goal Commitment In addition to values, the current emotional state of an individual or group influences task commitment. The general hypothesis resulting from research on emotion and commitment suggests that as mood becomes more positive, commitment becomes more likely, frequent and stronger in the face of distractions and vice versa (Ford, 1992; Bower, 1995; Boekaerts, 1993. Negative moods are characterized as sadness, fear, depression and anger (Ford, 1992). These negative mood states inhibit commitment (Bower, 1995). Positive moods are characterized by happiness, joy, contentment and optimism. Positive emotions have been found to foster commitment (Ford, 1992; Bower, 1995). In research, mood states are indicated by people’s memory for information congruent with their self-reported mood state; ratings of the enjoyableness of mood congruent information or commitments; affiliation preferences for people with similar mood states; social comparisons with mood-congruent people at work; and a focus on the positive or negative aspects of goals as moods change (Bower, 1995). Expectancy-control theorists suggest that negative mood states lead to lowered expectations that success or control will be achieved by a work goal and negative moods focus people on past errors and failures (Boekaerts, 1993; Bower, 1995). In fact, there are suggestions (for example, Shapiro et. al, 1996; Weiner, 1986) that one of the origins of negative emotions is the perception that we are denied adequate control in specific situations. For example, Weiner, (1986) suggests that depression sometimes results from the self perception that we are lacking in critical skills or ability to achieve a necessary goal, and that anger is the emotional product of the cognitive belief that some external agent has threatened us and that agent is not under our immediate control. Izard (1993) has presented evidence of four separate mechanisms that generate the same emotion in any individual. Only one of those systems is cognitive and under the control of the individual. Other, non-cognitive emotion activation systems include habitual or automated emotional reactions to events (Anderson, 1990,1993) plus neural, biochemical and hormonal processes (Izard, 1993). This research suggests that the origins of emotions are not always under our direct control. Yet Bower (1995) makes the point that emotions can be influenced by environmental and cognitive events even when their origins are biological or neurological. Emotion Measurement and Intervention: Bower (1995) describes a number of techniques for assessing emotional state and levels in research that could be adopted in organizational settings including: affiliation preferences (people tend to affiliate at work with people who share the same emotional state); recall of information related to our mood state (people tend to remember more information congruent with their current mood state); the time spent looking, listening and reading information related to our mood state (more time is spent attending to mood congruent information). The assessment of changing moods may be possible by noticing when people compare themselves with people who’s mood is more positive (if they are moving toward a negative mood) or with people who’s mood is negative (if they are shifting to more positive moods). This "reversed" social comparison process seems to accentuate the direction in which mood is moving by increasing the differences between ourselves and others whose mood is different. Boekaerts (1993) suggests the use of pencil and paper measures of "stress" to evaluate negative emotion related performance problems. Interventions that have been found to change negative mood states have included listening to music that is perceived to be positive; writing or telling about a positive mood-related experience; watching a movie or listening to stories that emphasize positive mood states (Bower, 1995); and emotion control training through "environmental control strategies" including the choice of work space and "positive self talk" (Corno and Kanfer, 1993). There are also indications that trusted enthusiastic, positive, energetic managers and work "models" encourage positive emotions in others and support work goal commitment (Bandura, 1997). Personal Agency and Goal Commitment In addition to values and mood, the final factor found to influence active commitment is our personal agency beliefs. Personal agency consists of two concerns: Ford (1992) provides evidence that we engage in an analysis of whether we have the knowledge required to achieve the goal ("Can I do it?), and second, we consider the barriers to our performance in the goal setting ("Will I be permitted to do it?"). The more that we believe we might be able and permitted to achieve a goal, the more likely we are to choose and commit ourselves to the goal The "can I do it?" question engages our memory about our ability and prior experience with similar goals. This review seems often to be implemented at a shallow level during this stage in a motivational process (Ford, 1992). The mechanism for the analysis of our capabilities may be similar to our "meta memory" or "feeling of knowing" experiences (Nelson, 1988) found in the familiar experience of "knowing that I know a fact (for example, a person’s name) without at the moment being able to remember the fact that I want to recall". Personal agency analysis at the commitment stage is similar to memory analysis in that we guess whether we have the general capability to achieve a goal without deeply analyzing our task-specific self efficacy for the goal we are considering. While it seems that many people may slightly overestimate their ability to achieve a goal (Nelson, 1988), the errors that are made in personal agency judgements tend to occur when we confuse our familiarity with the goal statement with our ability to achieve the goal (Reder & Ritter, 1992). Interventions Fostering Personal Agency and Commitment Personal agency involves both our memory of past performance on tasks similar to those we are considering and beliefs about the support available in the environment where the goal is pursued. Most measures of personal agency are based on self report (Ford, 1992). Agency measures that are more specific to a goal or class of goals are more robust than measures that are more global (Ford, 1992; Bong, 1997). Locke (1990) and Locke and Latham (1990) suggest that goal commitment increases, and temporary failure or negative feedback is handled much more successfully, when we believe that: 1) the goal is possible to achieve within the time and resources available; 2) we have the knowledge to achieve the goal; 3) more specific, explicit and difficult goals are chosen; 4) newly learned skills are directly relevant to goal achievement; and, 5) training is available. Kanfer et al (1994) have evidence that in some contexts (for example, time-limited, massed practice conditions) there are interactive effects of goal conditions and goal commitment. In their study, challenging goals may have been perceived as "impossible" when time was limited in massed practice conditions. Bandura (1997) recommends three types of agency interventions: First, organizations should provide mastery-oriented training experiences where increasingly challenging tasks representing organizational goals are accomplished. This intervention requires that we have a sense of the skill levels of employees necessary to achieve organizational goals. In addition, Bandura recommends that co-workers and managers focus positive feedback about task success on ability and effort. For example, when people succeed at work goals, the best feedback suggests that the person invested "good effort" and that they "have an ability for this kind of task". When performance difficulties occur, mistakes and failures should be attributed to a lack of effort. For example, to suggest that "You need to break this task into smaller chunks and focus your effort on completing each chunk". Another way of fostering personal agency at work is to expose people to "coping models" (Bandura, 1997) who are perceived to be from similar backgrounds and who have selected difficult goals and are succeeding only gradually and with difficulty. This approach is most important for people who may have different cultural origins than the majority of the employees in an organization. Finally, Bandura stresses the need to discourage people from using self-handicapping biases as they appraise their own capabilities. He recommends an approach described by Goldfried and Robbins (1982) where people learn to modify their standards of self evaluation and personal appraisals of their efficacy. Context Barriers and Goal Commitment Context beliefs are another key aspect of personal agency. Commitments are also based on beliefs about the contextual barriers in the environment where the goal is pursued. People who believe that personal prejudice, policy or procedural barriers or environmental noise or hazards exist in the performance environment are reluctant to make a commitment to work goals. Ford (1992) suggests three interventions that help foster a positive view of the context were a task is to be performed: First, the context must be perceived as supportive of the goal in its physical layout such as appropriate tools, materials, distracting noise, heat, and space. Second, the environment must offer a supportive social climate that includes fairness, trust, encouragement and social support. Included in this social element of context is the chance for "ownership" of the results of goal attainment. Third, the context must offer a high probability of policy and procedural support (and a lack of organizational or managerial barriers) to goal achievement. Mental Effort in Work Motivation Commitment to a work goal is evidenced by persistence at a task over time. However, commitment does not guarantee successful performance. Committed people sometimes fail when goal achievement requires novel problem solving or learning and those pursuing he goal believe it to only require routine, automated skills (Clark and Estes, 1996; Gagne and Medsker, 1996). Cognitive effort is one of the least explored areas in motivation research. It has been poorly understood, partly because of confusions between physical and mental effort and partly because effort is often confounded with commitment in motivation studies. In the past, work effort has most often been defined as physical exertion. Yet in the present climate of most organizations, "knowledge work" is more important to success than manual or physical work. Successful achievement of many, but not all knowledge goals requires that we invest mental effort. In general, mental effort has been found to be directly related to our perceptions of self efficacy to achieve a work goal (Bandura 1997; Graham and Weiner, 1966; Pintrich & Schunk, 1996). Most reviews of self efficacy research suggests that as efficacy for a specific task or goal increases, effort will also increase (Bandura, 1997; Bong, 1998). Commitment and Mental Effort Confusion in Research Few researchers have separated cognitive effort from goal commitment. While self report measures of mental effort are very common (Cennamo, 1989; Paas, 1993; Paas and Van Merrienboer, 1993; Pintrich & Schunk, 1996) most are used in research studies as an indicator of goal commitment. An implicit implication in this research is that commitment and active pursuit of a work goal is always accompanied by mental effort. Most often, self report questionnaires ask people to indicate "how much effort you invested"or "how hard you worked". The logic of this approach to defining commitment is that simply "spending time" on a task is not an adequate indicator of persistence. In fact, many motivation researchers may not consider the effects of different knowledge requirements on task performance. It is presumed that if we perceive a task as very difficult, that perception reflects an analysis of our own task-relevant skills. The usual solution to such perceptions is to attempt to increase self efficacy or self regulation perceptions and therefore reduce the perception that tasks are difficult. The problem with this strategy, is that when people lack knowledge and skills, no increase in their self efficacy alone, apart from a concurrent increase in knowledge and skills, will increase performance. In fact, there is evidence that the reverse can happen. For example, Highley (1994) reported a study with adults where an attempt to increase self efficacy perceptions of college study tasks initially rated as very difficult, resulted in an initial increase in efficacy and self-reported mental effort. Later however, when the most "at risk" students realized that they did not have the skills to achieve at the difficult tasks, their self efficacy decreased significantly below their initial levels. There is additional evidence to support the suggestion that commitment and effort outcomes should be separated in research. Commitment is influenced by values, emotion and personal agency. Effort is primarily influenced by specific and detailed self efficacy assessments of the knowledge required to achieve tasks. The Highley (1994) subjects persisted at their study tasks and invested effort until they learned that they did not have the knowledge required to succeed at the tasks. At that point, they retreated from the task and used the experience to reduce their self efficacy judgements about study. Pintrich and Schunk (1996) have described the mixed results of attempts to examine the impact of values and self efficacy beliefs on persistence, effort and goal achievement. They summarize their view of the results of a series of studies by Eccles & Wigfield thus: "The main effects of these studies is fairly easy to summarize. Expectancy beliefs, including self concepts, ability perceptions and expectancy for success, seem to predict actual achievement ... Values are positively correlated with actual achievement, but when both expectancy beliefs and values are used to predict achievement, expectancy beliefs are significant predictors but values are not significant predictors... It appears that achievement values may be more important for commitment behaviors [but once commitments are made] ... values are not as important for actual performance as are expectancy beliefs." (Pintrich and Schunk, 1996, p. 296). The claim being made here is that efficacy judgements influence the type of knowledge we employ, and therefore our effort and, with appropriate knowledge, our performance. Values do not motivate performance directly because they influence our commitment and persistence at a task but not our mental effort. Self-Efficacy Based Perceptions of Goal Novelty Once we are committed to a goal, we must make a plan to achieve the goal. A key element of all goal-directed planning is our personal assessment of the necessary skills and knowledge required to achieve a goal. A key aspect of self efficacy assessment is our perception of how novel and difficult the goal is to achieve. The ongoing result of this analysis is hypothesized to determine how much effort we invest in the goal. A key element in the assessment of goal or task novelty is our memory of our prior experience with the task or our assessment of the challenge the goal has posed to people who we judge to be "similar" (Bandura, 1997; Salomon, 1984). The more familiar the goal and the more knowledge and skill we believe we have gained in the pursuit of similar goals, the less effort we are inclined to invest. The rationale for this relationship can be understood by reference to theories of knowledge types (e.g. Anderson, 1983, 1993; Gagne et al, 1993). Automated expertise, developed over many hundreds of hours of practice, requires no cognitive effort to express. Only conscious, non-automatic, declarative knowledge requires cognitive effort (Anderson, 1983, 1993; Brown and Langer, 1990; Salomon, 1984). The more novel and difficult we perceive the goal to be, the more challenge we expect in the task. Salomon (1984) for example, presented evidence that our perceptions of the difficulty of learning from various media greatly influenced perceptions about the amount of conscious, non-automatic mental elaborations required to learn or solve problems. He found that people who believed that a medium (e.g. print) was very difficult, worked significantly harder to learn a task from that medium than they invested in the same learning task presented on another medium (video) they had judged to be much easier. He also found that people who believed that the learning task was impossible expended no effort. It is likely that when tasks are perceived as impossible, personal agency and value issues lead to goal avoidance. This implies that effort diminishes at either exceptionally low or high self efficacy levels and that the relationship between self efficacy and effort follows the shape of an inverted "U".
Figure 1 Over Confidence, Under Confidence and Effort Bandura (1987; 1997) and Salomon (1984) have described research that is useful in explaining different levels of effort. Many researchers in this area (e.g. Pintrich & Schunk, 1996) have suggested that efficacy and mental effort are linearly related. In general, there is a belief that as efficacy increases, effort will also increase (Bandura, 1997). Yet Salomon (1984) and Paas (1992) and Paas and Van Merrienboer (1993) find conditions where the relationship is negative. These researchers have suggested that under all task conditions, self efficacy beliefs may be curvilinearily related to effort in an "inverted U" fashion (See Figure 2 for a graphic depiction of this relationship). As efficacy beliefs become more positive, effort increases until we become overconfident. At that point, effort may decrease because workers tend to use "automated" and "unconscious" knowledge (Anderson, 1993) when more conscious and non-automated cognitive strategies may be needed for success. This "cross over point" between self efficacy increase and effort reduction occurs when over confidence leads us to attack a work goal with routine, automated skill. This is only a problem when the knowledge required for the task has been misjudged. A different reason for a decrease in effort occurs when under confident people believe that a goal is impossible to achieve because they are too novel and not enough prior knowledge is available to suggest a strategy. Sweller (Sweller, 1988; 1994) has considerable evidence that when the "cognitive load" of a task exceeds the capacity of working memory, effort ceases. He has suggested a specific method of quantifying the expected intrinsic cognitive load of problem solving task (Sweller, 1994). Paas and Van Merrienboer (1993) have provided evidence that excessive cognitive load reduces both mental effort and performance scores. When we believe that we cannot succeed, our control values are violated, stress occurs and we tend to look for ways to withdraw from the task (Boekaerts, 1991; Shapiro et al, 1996). Clark (1999) has presented evidence for both over confident and under confident mental effort "defaults". He argues that at both very high and very low levels of confidence and self efficacy, mental effort is halted. The amount of confidence required for both the over- and under-confident defaults may vary for different individuals and groups. Once we begin active pursuit of a goal, we experience the success or failure of the strategies we actually employ. When we misjudge our capabilities to achieve any part of a work goal, we may make one of two mistakes. If we assume that a task is novel when it is familiar, we will attempt to invent a new approach when our existing strategies would suffice. We often characterize people who make this mistake as "under confident". This efficacy error increases the person’s perception of the demands of the task and often delays its achievement. These delays cause significant problems in work settings were the time to achieve goals is severely constrained. If we view a task as familiar when it is not, we will tend to use existing strategies when a new approach is required. People who make this mistake are often called "over confident" (Clark, 1991, 1992). Weiner’s (1983) attribution theory would suggest that any unexpected and negative failure event would provoke an attempt to explain why failure occurred. His theory could be interpreted to suggest that people who make over confident mistakes may be difficult to correct since they generally can be expected to avoid taking responsibility for their use of inappropriate knowledge. They may project the blame for their mistakes to the organization, co workers or the task for the errors. The research of Baumeister, Boden and Smart (1996) provides added evidence that over confident people will reject responsibility for the mistakes they make. Baumeister et al (1966) demonstrate that under some conditions, resentment, anger and even violent behavior at work can be explained by an effort to protect inflated and unstable beliefs in self efficacy. They suggest that directing anger outwards is one way people avoid a downward revision of their inflated self efficacy. Either highly inflated or depressed efficacy errors can, and often do, result in performance problems and/or inefficiency at work. Measurement and Interventions for Self Efficacy and Mental Effort The measurement of self-efficacy is well established (Bandura, 1997). Most of the measurement approaches involve self-report although Bandura (1997) describes strong reliability and validity data for these instruments. The measurement of mental effort has followed a similar path. Most measurement instruments request self reports of the amount of mental effort and "non automatic" knowledge used to solve problems, learn or transfer knowledge to new tasks. Paas (1993) describes one such measure which involves asking subjects to report the amount of mental effort they invest in each problem-solving task. His measure uses a 9 point effort scale that varies from "very very high mental effort" to "very very low mental effort". Paas (1992; 1993) has also successfully used heart rate as a correlate of self report. In general, self report measures of mental effort report alpha levels of from .74 to .93 (see Cennamo, 1989). Some mental effort measures ask experimental subjects to estimate the time it took to complete tasks (for example, Dweck, 1989; Corno & Kanfer, 1993). Here it is assumed that lower levels of mental effort free up more working memory so that time can be more accurately estimated. Another measurement strategy involves the use of dual-task measures (e.g. rhythmic finger taping while solving problems, Cennamo, 1989). Interventions for mental effort problems One indicator of a mental effort problem occurs when people are making mistakes on a task they are actively pursuing (if people are avoiding a task, they have a commitment problem). Once actively involved in a task, excessive efficacy (over confidence) problems show up as mistakes or missed deadlines due to inappropriate approaches to a work goal. When over confident mistakes are analyzed, there are indicators that inappropriate and routine knowledge is being applied inappropriately. People using routine knowledge when new approaches need to be developed may have the ability to generate new approaches. They may not realize that a novel solution is required to achieve the goal. Dweck and Leggett (1988) suggest that it is necessary first to demonstrate to over confident people that their approach to the problem is not working and is responsible for the errors being recorded. The best way to approach people who are making efficacy mistakes is to focus feedback on the way that the task is being pursued, and not on the person’s ability (Locke and Latham, 1990). Under confident people need to know that the task can be made more manageable, for example by separating a larger task into smaller, more specific and tractable tasks. Locke and Latham also see value in providing training on the task if it is determined that people with efficacy problems do not have the knowledge required to achieve work goals. If they do have the knowledge and their confidence is causing mistakes, they need help in applying the correct knowledge to the task. Over confident people are more difficult to support. Dweck & Leggett (1986) suggest that in order to make progress, we must convince the overconfident person that the strategy being used to achieve the goal is not working before we can expect that the person will substitute a more adequate strategy. If we only focus on the poor task results, people who are acting overconfident will project responsibility for the problem (since they are confident that their approach is correct) and refuse to adjust their approach. Conclusions and Summary Organizations are advised to consider implementing cognitive motivation theory at work using the human performance approach of Gilbert (1996) and Stolovitch and Keeps (1992) and a research-based theory such as the CANE model presented above. Motivation is defined as processes that initiate and sustain goal-directed work activity (Pintrich & Schunk, 1996), and where necessary mental effort is invested in the achievement of quality work goals (Salomon, 1984). The CANE (Commitment and Necessary Effort) model is suggested as an initial approach to diagnosing motivational opportunities and solving motivation problems. While the cognitive research on work motivation is incomplete and some of the research was conducted in academic settings, organizations who have used the approach have experienced considerable success. Clark (1998) provides examples of some of these applications. The CANE approach indicates that two types of motivation problems exist, failure to accept and actively pursue work goals; and failure to invest adequate effort to achieve goals once active pursuit of the goal is underway. A two-stage theory was described where goal commitment occurs first and then the pursuit of a goal leads to decisions about the quality and quantity of effort invested. In the first stage, active commitment to goals is predicted by a multiplicative relationship between three factors: Personal agency, emotion and control values. Personal agency is defined as general self efficacy, a meta-assessment of one’s ability to achieve a class or domain of work goals ("Can I do it?"), on the one hand, and our estimates of the barriers that surround the class of work goal ("Will I be permitted to do it?"), on the other hand. In addition, our emotional reaction to the goal must be neutral or positive. Finally, we must believe that achieving the goal will lead to control benefits (e.g. make us significantly more effective than competing goals). Three types of values were hypothesized to influence work goal commitment: a) utility ("I may not enjoy the pursuit of this goal, but I do desire the benefit of achieving the goal"); b) Interest ("I am curious about this goal, it has intrinsic value"); and c) Importance ("Mastering this goal will make me more effective and/or give a good impression to others"). When these three factors are positive, goal pursuit is more likely. When any one of these factors is negative, goal pursuit is less likely. In the CANE model, effort is viewed as a secondary but vital motivational process. When goals are chosen and actively pursued, decisions about the type of knowledge required to achieve the goal predict the level of effort invested. An inverse U relationship is hypothesized between the perceived novelty of a task and the amount of effort people will invest in the pursuit of the task. The more novel the goal is perceived to be, the more effort will be invested until we believe that we might fail. At the point where failure expectations begin, effort is reduced as we "unchoose" the goal to avoid a loss of control. This inverted U relationship suggests that effort problems take two broad forms: over confidence and under confidence. Over confident employees may value work tasks but they make mistakes at them because they have incorrectly diagnosed a new task as familiar and routine when it is novel. Conversely, they may believe that a task is novel when the task is routine. Thus over confident workers either spend unnecessary and inefficient effort to develop new work strategies when existing strategies would suffice, or they use familiar strategies when the goal requires the development of new approaches. Over confident workers complicate work problems because they typically resist taking responsibility for their mistakes. They often believe that they have correctly diagnosed the strategies required to achieve a work goal and therefore it is the manager who is mistaken about the quality of their work. Under confident workers have a different problem. They may have the skills necessary to achieve the goal but do not believe that they can succeed. Because they expect failure, they often experience a decrease in their value for the goal and attempt to withdraw from the task by handing it off to someone else or experiencing anxiety and stress to the point of illness. Interventions are suggested to help enhance both goal commitment and effort. Commitment is supported by increasing self-efficacy and changing perceptions of the barriers that prevent goal achievement. Mood can be enhanced by a variety of modeling, music and story interventions that can be used in the work setting. Value for the work goal can be influenced by credible descriptions of the utility, intrinsic interest and importance of goal pursuit and achievement. Similarly, the level of mental effort necessary to achieve work goals can be influenced by adjusting perceptions of goal novelty and the effectiveness of the strategies people use to achieve goals. A caution is suggested concerning the implementation of the interventions proposed in the CANE model. There is considerable evidence of both individual and cultural differences in patterns of personal agency, emotion, value and novelty perceptions. When adopting cognitive motivation models, as with any other model, we must "translate" interventions for local users. However, if cognitive motivation theories are adopted at work, there is clear evidence in the research and current practice in organizations that the benefit of its use will be significantly higher than the cost of the interventions. Parts of this manuscript are drawn from
an invited address to the faculty of the University of Leuven, Belgium, November
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