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If one were to ask business managers to identify the most common problems they have in the workplace, most would likely say people problems—for example, employees’ lack of motivation, difficulty hiring the right staff, and trouble preventing turnover. Remedies for such problems include managers searching for motivated employees who have good attitudes and who are conscientious. Managers expend much energy finding and keeping employees with these coveted characteristics, and human resource specialists, psychologists, and consultants spend considerable time developing procedures to select such people.
In popular business books, one tends to find that most researchers focus on the personality traits and characteristics of desirable and undesirable employees—for example, whether employees have a good attitude or whether they are motivated—but pay very little attention to why certain employees have a bad attitude or why others are not motivated. Typical approaches to addressing people problems in the workplace tend to describe good and poor performers but fail to focus on changing what people at work actually do. Unfortunately, if organizational results are not where they should be, the primary culprit is, in fact, what people are—or, maybe more important, are not— doing. As Daniels and Daniels (2004) stated, “Every result is produced by someone doing something. If you want to improve results, you must first get employees to change what they are doing” (p. 27). Thus, if a manager can understand how to change behavior and maintain that change, she will have a significant advantage over her competitors. Organizational behavior management (OBM) is a “systematic, data-oriented approach to managing behavior in the workplace” (A. C. Daniels, 1989, p. 4) and can provide such an advantage.
Because of the competitive nature of business in today’s global and fast-paced economy, there is a great deal of pressure on organizations to (a) change rapidly in order to keep pace and (b) employ methods that their competitors are using. This pressure to change rapidly can result in organizations implementing popular interventions without empirical support for efficacy. Consequently, organizations may spend an inordinate amount of money on ineffective systems (e.g., management by wandering around). In a popular 1980s book, Peters and Waterman (1982) identified 43 organizations they considered to be excellent for a number of reasons. Bailey and Austin (1996), however, subsequently reanalyzed these organizations and reported that only 14 of the original 43 organizations would have still been considered excellent just 2 years later. Why such attrition? Most likely, these organizations were implementing unsupported fads that did not sustain long-term, real performance change. OBM, in contrast, approaches workplace people problems by relying on science-based methods and behavior-analytic principles.
Behavior analysis is the scientific study of behavior, and applied behavior analysis (ABA) is the application of behavioral principles in an attempt to solve problems of social relevance (Baer, Wolf, & Risley, 1968). Although behavioral researchers have studied basic principles of learning for well over a century, they began applying these principles in the workplace only in the 1960s. To date, these applications have resulted in great successes in the areas of customer service, distribution, engineering, information management, manufacturing, research and development, safety, and sales, among others.
Applying Behavioral Principles To The Workplace
OBM psychologists approach workplace change by assuming that all human behavior is caused by the interaction of innate characteristics, experiential history, and current environmental conditions. They also understand that problems at work are due in large part to deficiencies in certain human behaviors (e.g., lack of selling). Finally, they view behavior as a subject that can be understood scientifically. Thus, in order to fix workplace problems, OBM psychologists draw on what they know about human behavior using the principles of behavior analysis.
A primary concept in behavior analysis is the three-term contingency (A: B ◊ C). In this model, antecedents (A) set the occasion for behavior (B), which is then followed by one or more consequences (C). In the workplace, antecedents may include a phone ringing, a training manual, and a sign that says “We Put Safety First!” Corresponding behaviors may include answering a customer’s call, correctly addressing an accident in the workplace, and wearing safety goggles, respectively. Finally, consequences may include any stimuli that follow workplace behaviors, including customer feedback, receiving praise, or being reprimanded by a supervisor. Consequences have much more power over behavior than do antecedents, which is important to know when it comes time to introduce workplace interventions.
Behavior analysts typically separate consequences into two categories: those that increase behavior and those that decrease behavior. Consequences that increase behavior include (a) positive reinforcers, which are those reinforcers that an individual receives by engaging in a specific behavior (e.g., praise, money, attention), or (b) negative reinforcers, which are those reinforcers that an individual avoids by engaging in a specific behavior (e.g., a reprimand, getting fired). Consequences that decrease behavior include punishers or penalties (or positive and negative punishers, respectively). Punishment occurs when a behavior decreases following the consequence it produces. For example, an employee might stop making inappropriate jokes at work after receiving a formal reprimand from his supervisor. Penalty occurs when an employee loses something of value, resulting in a decrease in behavior. A cross-country truck driver might be fined for speeding, resulting in the driver’s speeding less. In addition, reinforcers and punishers can be either social (e.g., praise, attention) or tangible (e.g., a plaque, a raise). A basic understanding of these four consequences can result in major gains for an organization and its employees.
Most organizational interventions rely primarily on antecedents in their attempt to change behavior. Managers will train, explain, send memos, construct new manuals, put up signs, beg, plead, and yell to change an employee’s behavior. For example, picture an auto dealership in the process of starting a new sales campaign. The new cars are in, and the company is pushing for big sales on a particular new model. To improve sales, salespersons may be urged to attend training seminars to learn about the new car, and managers may send out memos and prompt and encourage their employees to focus on selling the new model. The sales room is decorated with banners and balloons, and signs in each office remind sales personnel of the importance of this month’s campaign. Unfortunately, after a few weeks, the new model has not sold as well as the company would like. Why?
These interventions—the training, the memos, the banners—are antecedents, and, as already mentioned, antecedents alone do not change behavior. Rather, a common mantra in behavior analysis is “Behavior is a function of its consequences.” Thus, one can predict and change behavior best by focusing on what happens after a behavior has occurred. To the extent that antecedents are effective in the workplace, it is due to the fact that they have been reliably associated with consequences in the past. For example, telling a salesperson that he should focus on selling the new model car (an antecedent) will be effective only if selling that car results in an important consequence (e.g., a bonus, recognition). Most organizational changes, however, involve elaborate antecedents, without considering consequences. Research shows that initiatives aimed at making desired behaviors reinforcing are more successful than initiatives that rely only on antecedents (e.g., Bailey & Austin, 1996). As such, an OBM consultant might ask people at the car dealership, “What was the salesperson’s commission on the new model?” By asking this type of question, the consultant focuses on the consequences of the salesperson’s behavior.
When OBM psychologists address a problem at work, it is common to analyze an individual’s performance problem using one of several behavioral tools. One commonly used tool is the ABC analysis, which systematically examines the antecedents and consequences of both desired and undesired behaviors. Consider again an auto dealership in which new cars are not selling. Desired behaviors for salespersons may include discussing the new model car with customers and encouraging them to buy that car. These behaviors have many antecedents, including banners around the store and memos posted near the sales floor. But what happens to the employee when he engages in those behaviors? Do customers show disinterest in the new model? Is the commission for the new car less than what it would be for others cars? If so, it would not be surprising if the salesperson were not trying to sell more of the new model cars. Consider also undesired behaviors, which might include discussing sales of other model vehicles with customers. If customers are receptive to the prospect of buying a different car, the salesperson is more likely to engage in this behavior.
In sum, the ABC model of behavior change can be extremely helpful in producing and managing organizational change (A. C. Daniels, 1993). Managers can share this framework with employees and the basic ideas can be implemented at all organizational levels. With even a basic understanding of behavioral principles, employees can analyze situations on their own and make significant changes to their work behavior.
Features And Methods Of OBM Interventions
Bailey and Austin (1996) identified the distinguishing features of OBM interventions that separate them from more traditional workplace initiatives. Specifically, OBM differs from other approaches in its focus on measurement of performance, empirical interventions, and continual evaluation of intervention effectiveness.
Foremost in OBM interventions is the frequent, specific, and continuous measurement of performance. In order to measure correctly, OBM consultants pinpoint the desired results as well as the behaviors that will produce those results. Managers commonly want their employees to be “team players” and have “good attitudes.” However, these fuzzy descriptions are not helpful when it comes to changing behavior simply because one cannot measure them accurately. Therefore, one needs to pinpoint, or describe those behaviors in precise and measurable terms. A person who has a “good attitude” might volunteer for assignments, arrive to work early, and say positive things about the organization. Without these specific descriptions of a “good attitude,” one would not be able to measure the relevant corresponding behaviors.
In business, leaders are interested in both results and the behaviors that produce those results. In essence, results are the destination, and behaviors are the directions to that destination. Thus, it is important to pinpoint results first. By doing so, one can avoid punishing or reinforcing undesired behaviors that add no value to the organization. Unfortunately, many managers occupy themselves with the nuisance behaviors of their employees—behaviors that are not problematic in and of themselves. For example, consider an employee at an electronics store who is gossiping with his coworkers in order to avoid talking to customers. If a manager punishes the employee’s talking with coworkers (the nuisance behavior), the employee would probably find another way to avoid work. If, however, the manager reinforces the employee’s behavior of approaching customers, sales would likely improve. Thus, by pinpointing the results first (i.e., the employee’s sales performance), one can disregard nuisance behaviors and focus instead on changing behavior (i.e., approaching customers) that impacts results.
By pinpointing results and behaviors, one can also avoid labeling employees. Labeling an employee—for example, an employee who has “a bad attitude”—does not give her any information about what it is we would like her to change. Telling an employee she has a bad attitude might also result in her getting defensive and resisting change even more. Instead, it would be more effective to focus on the pinpointed observable behavior and avoid making false attributions.
Once a manager has pinpointed a behavior, she can then move on to measuring it. A. C. Daniels and J. E. Daniels (2004) discussed some important reasons for measuring behavior at work. First, progress requires measurement. Second, in order to give feedback to an employee, one needs to know what that employee is doing and how that employee has improved. Third, managers are more credible when they measure performance. Finally, measurement helps reduce emotionality and solve problems. To illustrate these ideas, consider a situation where an employee is not making enough sales calls. Without measurement, a manager would have no way of knowing when or if that employee had improved in this domain. It would also be impossible for a manager to give him any meaningful feedback if he or she were unaware of how the employee was performing. Telling an employee he is lazy and needs to make more sales calls is probably not going to be very effective, and will likely result in hostility. However, by showing him how many calls he is currently making and how many calls he needs to make in order to meet the departmental goal, he may see management as more credible and may be less likely to get defensive at the feedback.
Properly measuring behavior in the workplace is a complex issue; it is, however, vital for change. It is important to note, though, that both employees and employers often resist measurement for several reasons, including fear of punishment if they aren’t performing up to par, the belief that measurement takes too much time, and the belief that one cannot measure certain behaviors. Komaki (1986), however, noted that the most important factor in determining the effectiveness of leaders is how frequently they measured the performance of their employees. Therefore, although employees might have an initially negative reaction to measurement, it is imperative that leaders implement precise measures in the workplace in order to help their employees excel. Effective measurement is the principal component in any effective workplace intervention.
A second distinguishing characteristic of OBM is the reliance on research-based interventions, the most common of which include goal-setting and performance feedback (Johnson, Mawhinney, & Redmon, 2001). Whereas goal-setting refers to “defining a specified, or preset, level of performance to be obtained” (A. C. Daniels & J. E. Daniels, 2004, p. 241), feedback is “information about performance that allows a person to change his/her behavior” (p. 171). Thus, in behavior-analytic terms, goals function as antecedents, and reaching those goals functions as a reinforcing consequence. Moreover, feedback combined with reinforcement is frequently an effective way to change behavior. However, a common misconception is that goals or feedback alone can change performance. In reality, without the added benefit of reinforcement, changing behavior via feedback is unlikely. For example, consider the following question: How many times have you told yourself that you want to lose weight (i.e., goal-setting)? Does weighing yourself (i.e., feedback) produce weight loss? Probably not. People often set goals and receive information about their performance, only to dismiss it when it is not what they had hoped. In contrast, when someone steps on the scale and has lost weight (a reinforcing consequence), his behavior that led to the weight loss is more likely to change. In short, when goal setting and feedback are implemented properly with appropriate reinforcement contingencies, they can produce meaningful behavior change.
Research shows that goals should be specific, focused on behavior (not just results), positive, challenging but attainable, measured frequently, and include both short-term (or subgoals) and long-term objectives (A. C. Daniels, 1989). For example, if a student were trying to improve her GPA, a poor goal would be to “get better grades.” A more effective goal would be to increase her time spent studying by 1 hour each week until the end of the term— a goal that is specific, positive, challenging but likely attainable, can be measured frequently, and will likely have both short- and long-term benefits. Nonetheless, the goal will not be effective unless it is followed by contingent reinforcement. One benefit of good goal setting is that it results in opportunities to reinforce desired behavior. For the student trying to improve her grades, she will have many opportunities (i.e., on a weekly basis) to celebrate (i.e., reinforce) successfully reaching her goal.
Adding feedback to the equation tends to boost performance as well. In fact, it is almost impossible to discuss the characteristics of effective goal setting without emphasizing feedback. After all, how would an employee know that he met his sales goal without information about his performance? Some characteristics of good feedback include that it (a) is specific, (b) focuses on behavior that is under the performer’s control, (c) is individualized, (d) is immediate, (e) focuses on improvement, and (f) is used as an antecedent that signals pending reinforcement (A. C. Daniels & J. E. Daniels, 2004). Feedback is also most effective when it is provided in both written and visual (i.e., graphic) forms. Many successful OBM interventions in different organizational settings and focusing on different problems have included goal setting and feedback (see Alvero, Bucklin, & Austin, 2001).
Without some sort of framework to organize goals and give all employees a common vantage point, though, evaluating progress toward goals will be impossible. A performance matrix is a goal alignment and point system that can be used to analyze and organize goals in the workplace (Gilbert, 1996). In essence, a performance matrix provides a method for establishing goals, measuring performance, and providing feedback to employees on their progress toward goals (A. C. Daniels, 1993).
To construct a performance matrix, one first defines five to seven dimensions of job performance to measure, such as sales performance and communication. The next step is to determine an accurate and reliable way of measuring employees’ performance on these different dimensions. After constructing an accurate measure of how employees are currently performing, the different goals are weighted in terms of their importance. Then one constructs plans specifying what reinforcement individuals or work groups will receive when they meet those goals. In sum, using a performance matrix helps capitalize on the most effective characteristics of goal setting and feedback.
Continual Evaluation of Intervention
The final defining characteristic of OBM is the use of continual evaluation after an intervention is introduced. In order to determine if OBM interventions improve workplace performance, a manager must evaluate the intervention. However, the standard method of evaluation, one in which individuals are randomly assigned to either a control group or an experimental group, is often not feasible in applied settings for numerous reasons. Conversely, in OBM interventions, in which there is interest in changing the behavior of individuals and not evaluating the average performance of a group, one typically uses single-subject research designs in which individual employees, managers, or supervisors serve their own control, or baseline (see, e.g., Johnston & Pennypacker, 1993).
There are several single-subject research designs that help OBM consultants measure and evaluate their interventions. The most common one is the A-B design, in which one measures performance during a baseline, or nonintervention, phase (A) and then implements the intervention (B) and measures its effects, if any, on the behavior of interest. This method provides some information regarding whether performance improved following the intervention.
However, one cannot be certain that the intervention caused this improved performance. An improvement could instead be due to other extraneous factors that occurred at the same time as the intervention. Therefore, consultants might instead implement an A-B-A or an A-B-A-B design. The power of these designs lies in the reversal to baseline conditions (A) and, in the case of the A-B-A-B design, an additional implementation of the intervention (B). If performance improves during both intervention phases relative to behavior during baseline phases, one can be more assured that the intervention was responsible for this improvement.
Although A-B-A and A-B-A-B designs are significant improvements over basic A-B designs, there are practical issues when using these designs in business settings. For example, if one finds that performance increases following an introduction of feedback during the intervention phase (B), many managers would be reluctant to remove this seemingly successful intervention and risk a return to baseline performance levels, especially if the change resulted in losing large sums of money. There might also be ethical issues regarding the removal of an intervention that benefits employees.
In these cases, one may use an alternative intervention: a multiple-baseline design. A multiple-baseline design involves the “sequential, time-staggered introduction of the intervention across different [situations]” (Bailey & Austin, 1996, p. 186). Essentially, a multiple-baseline design entails several A-B designs repeated across situations, work groups, or individuals. For example, say that one is interested in evaluating the effectiveness of a reinforcement intervention at a retail clothing store. In a multiple-baseline design across work shifts, one would start baseline measures for the first, second, and third shifts at the same time (A). Then, one would implement the reinforcement intervention during Shift 1 (B) but continue measuring behavior under baseline for the remaining two shifts (A). After behavior under the intervention (B) had stabilized during Shift 1, one would then implement the intervention during Shift 2 (B), while continuing with the intervention during Shift 1 (B) and the baseline during Shift 3 (A). Finally, once behavior had stabilized during Shift 2, one would implement the reinforcement contingencies across the third shift, such that now all three shifts are under the same reinforcement contingencies at the same time. Again, if behavior changed only with the contingent introduction of the intervention, one could be relatively certain that the intervention, and not some other variable, was responsible for the observed change.
Regardless of the specific single-subject design that one decides to use, it is vital that measurement continue for the duration of the change. It is common in organizations to measure behavior for a brief period following an intervention, only to remove measurement once the intervention produces initial changes. This strategy would be a mistake. If one ceases measurement right after performance changes, one would not be able to deliver additional consequences contingent on performance, because one would not know whether performance is long lasting or only short lived. Moreover, if one doesn’t continue to reinforce desired behavior, performance is likely to deteriorate due to the extinction of reinforcing consequences. Goal setting and feedback will also not be possible, because in order to do each well, one needs accurate measures of performance. Finally, one will not be able to determine if changes in the organizational environment have resulted in a change in the effectiveness of the intervention. In short, continual measurement is vital for continual progress.
Diagnosing Performance Problems In The Workplace
In general, applications of OBM involve the evaluation of performance problems caused by myriad factors. Luckily, as outlined above, there are some key features of behavior-analytic interventions that allow for the analysis of diverse performance problems in the workplace. The diagnosis of these problems is vital to constructing effective interventions, and there are common methods for understanding most performance problems, regardless of how complex they might be. The literature on behavioral approaches to diagnosing performance problems includes several different models and algorithms from which to choose (or, more likely, to use in tandem), depending on the level of analysis (e.g., one individual, a team, or the entire organization) and how specific the analysis needs to be.
Diagnostic models typically involve a series of steps that ultimately get to the root cause of a problem. Although there are several common models for analyzing performance in the workplace, including Brethower’s (1982) total performance system (TPS) model and Rummler and Brache’s (1995) process mapping system, arguably the most well-known is A. C. Daniels’s ABC analysis model, which capitalizes on changing components of the three-term contingency. When a problem behavior is occurring or a desired behavior is not occurring, A. C. Daniels (1989) suggested that managers or consultants can use the following steps to alleviate the problem:
- Describe the undesired behavior and who is currently doing it (the problem).
- Describe what this person should be doing instead (correct or desired behavior).
- Determine the severity of the problem.
- Complete an ABC analysis for the problem behavior.
- Write down the person’s name along with the problem behavior.
- List all possible antecedents and consequences for the problem behavior.
- Cross out any consequences that don’t seem relevant.
- Indicate whether each remaining consequence is positive or negative, immediate or delayed (i.e., in the future), and certain or uncertain (see below).
- Complete an ABC analysis for the desired behavior.
- Identify the desired behavior that you wish to replace the problem behavior.
- Complete Step 4 above.
- The Diagnosis: Summarize the antecedents and consequences that are most likely functioning to maintain the undesired behavior and try to remove them.
- The Solution: Introduce antecedents and positive and immediate consequences for the desired behavior.
Because behavior is a product of its consequences, the most important steps are 4 and 5, where one analyzes the consequences of the desired and undesired behaviors. Specifically, one classifies consequences along three dimensions, determining whether the consequences (a) are positive (good) or negative (bad) for the performer (P/N), (b) occur immediately following the behavior or sometime in the future (I/F), and (c) are certain to occur or uncertain (C/U).
The results of ABC analyses often reveal common patterns (A. C. Daniels, 1989). First, the undesired or problem behavior typically has more antecedents than the desired behavior. In other words, many more events in the current environment often prompt one to engage in undesired behaviors. Second, although desired and undesired behaviors often have equal numbers of positive and negative consequences, one typically finds that the negative consequences associated with the undesired behavior often occur in the future and are relatively uncertain, whereas the positive consequences associated with that behavior are immediate and certain (PICs). In contrast, one often finds the opposite pattern with desired behaviors: The positive consequences are in the future and uncertain, whereas the negative consequences are immediate and certain (NICs). Hence, with this pattern of consequences, undesired behaviors are more likely than desired behaviors to occur. Finally, A. C. Daniels offered several tactics for addressing problem behavior: (a) adding PICs for the desired behavior; (b) adding antecedents for the desired behavior and removing antecedents for the undesired behavior; (c) removing NICs for the desired behavior and PICs for the undesired behavior; and, if absolutely necessary, (d) adding NICs for the undesired behavior.
To illustrate this concept, consider the problem behavior of smoking. Common antecedents for this behavior include a craving for nicotine, the presence of friends who are smoking, and eating a meal. Antecedents for the desired behavior—in this case, not smoking (e.g., a warning label on a pack of cigarettes)—tend not to be very salient. In addition, negative consequences associated with smoking, such as disease or death, are uncertain and typically do not occur until sometime in the distant future. However, one primary positive consequence of smoking, reduction in nicotine craving, is both immediate and certain. When evaluating the consequences for the desired behavior, abstaining from smoking, one finds the opposite pattern: The negative consequences—in particular, a nicotine craving—are both immediate and certain; the positive consequences, such as improved health, are delayed and uncertain. After examining a behavior in this way, one can see why someone might have a difficult time quitting smoking.
By using A. C. Daniels’s (1989) ABC analysis model, one can understand better why people engage in certain behaviors and avoid others. A. C. Daniels and J. E. Daniels (2004) stated that “In over 35 years of doing [this type of] analysis, no workplace problem has ever arisen for which the general finding of the [ABC analysis] has not held true” (p. 45). Nonetheless, because Daniels’s ABC model is representative of an individual level analysis, it is not particularly accommodating when one is interested in widespread organizational change. To address such large-scale changes, OBM consultants turn to a behavioral systems analysis (BSA).
There are many times when the changes one needs to make in the workplace go beyond a single individual’s problem behavior. For example, suppose an airline has a problem with lost luggage. This problem is not specific to one baggage handler but probably involves many different people. How can one understand what is causing this lost luggage problem? More important, how can one fix it? OBM can address these types of large-scale problems by using BSA, which combines behavior analysis with systems analysis, a type of analysis that examines how complex systems, such as an airline, function, as well as how the different parts of those systems work together to achieve an outcome. In an organization, there are many different people, departments, and units that may be interacting to achieve a common goal. For example, with our problem airline we have baggage handlers, grounds crew, and security personnel, all involved in the transport of luggage. With systems analysis, one can investigate how these different groups and processes are interacting, as well as how to improve their interactions.
As stated above, behavior analysis examines antecedents and consequences of behavior in order to explain why people do what they do. With BSA, one takes that perspective and applies it at the organizational level, where there may be many different but interrelated contingencies operating. For example, although one might encourage baggage handlers to load luggage onto planes promptly, security personnel are likely focused on thoroughly examining the contents of luggage, which may take more time.
BSA can help one understand how these contingencies differ across groups, how one can better align these contingencies, and how to apply behavioral methods to meet organizational objectives. Because a complete discussion of BSA is beyond the focus of this research-paper, I will not discuss it further (but see Malott, 2003).
Unlike models that include a series of steps, diagnostic algorithms involve a series of questions (typically yes-no questions) that one answers in order to understand a performance problem. Two common diagnostic algorithms are Mager and Pipe’s (1997) performance analysis diagram and Austin’s (2000) performance diagnosis checklist.
Mager and Pipe’s (1997) performance analysis flow diagram is helpful in analyzing the behavior of an individual employee. Let’s take the previous example of an electronics store employee who is talking with her coworkers instead of approaching customers. First, one needs to describe the discrepancy between what the employee is currently doing and what she should be doing. Second, one asks whether this is a problem that is worth solving. If such a change does not meaningfully impact results, then it may not be a significant problem. If it is, however, one then moves on to the next set of questions to see if quick fixes can address the problem. These include asking whether the employee knows what her manager expects her to do and if she receives feedback on her performance. If these answers are in the affirmative, one moves to the next series of questions, which focuses on analyzing the consequences of her behavior. Is approaching customers punishing? Is talking with coworkers rewarding? Next, one determines whether this problem is due to a skill deficiency (she does not know how to interact with customers) or a motivational issue (she does not want to interact with customers). Finally, the algorithm examines the solutions available to solve the problem and which solution(s) are feasible and cost effective.
Austin (2000) developed a different algorithm called the performance diagnostic checklist. This checklist focuses on questions in four different areas: (a) antecedents and information, (b) equipment and processes, (c) knowledge and skills, and (d) consequences. The first area, antecedents and information, addresses whether the electronics employee knows what is expected of her. Perhaps she was never told that she should approach customers as they enter the store. The second set of questions, equipment and processes, focuses on whether she has the necessary materials to approach customers and if there are any obstacles preventing her from doing so. For example, does she have access to a training manual that details the best way to approach customers? The third area, knowledge and skills, examines the knowledge and capabilities of the employee. For example, does she know enough about televisions to answer the customer’s questions? Finally, the most extensive section, consequences, analyzes the consequences affecting the employee’s behavior. For example, what happens to the employee when she approaches the customers?
By using one or a combination of these tools, a consultant is able to identify where performance or system problems might be. Consequently, the consultant can concentrate his efforts on the element of the system that is faulty. Furthermore, these tools provide guidelines for addressing problems at different levels of the system.
Applications Of OBM
One of the most prolific areas of OBM applications is in the field of behavioral safety. Consultants have implemented effective behavior-based safety interventions in a wide range of employment settings including construction, transportation, mining, food manufacturing and farming, police units, hospitals and in-home care, and office settings. In fact, it is not uncommon for these interventions to result in a 30 to 50 percent improvement in safety outcomes (Sulzer-Azaroff & Austin, 2000). More important, these interventions regularly reduce or eliminate serious injury and even death.
An important outcome of these applications has been an understanding of employees’ reasons for behaving unsafely (Sulzer-Azaroff, McCann, & Harris, 2001). Many organizations have focused for years on the reduction of injuries in the workplace, bringing together different specialists in engineering, workplace design, public health, occupational medicine, and computer science to develop safer work spaces for employees. Nonetheless, injuries have persisted because of human behavior problems in the workplace, where individuals engage in risky behavior even when they “know better.”
One can readily understand these seemingly illogical behaviors by analyzing the contingencies that are in place for behaving safely. If there are reinforcing contingencies for behaving unsafely, or punishing contingencies for behaving safely, then unsafe or risky behavior is likely to persist. Unfortunately, such contingencies tend to be common in the workplace (see, e.g., Geller, 1996). For example, one of the most important markers for airline performance is the percentage of planes that leave and arrive on time. In order for planes to remain on time, airline workers must conduct a safety check of the plane, clean the cabin area, load luggage, fuel the plane, and conduct any necessary maintenance. To complete each of these behaviors safely takes more time than behaving unsafely (e.g., loading luggage without appropriate lifting techniques, conducting a safety check quickly). Thus, it is likely that there are reinforcing contingencies for behaving unsafely (e.g., the plane is more likely to be on time) and punishing contingencies for behaving safely (e.g., social consequences for not moving quickly enough). In short, getting an employee to behave safely in the workplace often requires management of reinforcement contingencies.
The nature of behavioral safety interventions is similar to that of any other OBM intervention in the workplace. Consultants must analyze the culture of the organization; pinpoint the desired behaviors; set priorities (i.e., construct a performance matrix); select and implement measures; intervene using training, goal-setting, feedback, and reinforcement; evaluate and refine the intervention; and put contingencies in place that support lasting change (Sulzer-Azaroff et al., 2001). In all, these basic behavioral interventions in the workplace have been extremely successful and continue to be an important OBM venture.
Training and Development
Because a behavioral approach to organizational change places more emphasis on consequences than on antecedents, there is a misconception that training is not a focus of OBM interventions (Perlow, 2001). Although contingencies are vital for performance change, a significant portion of designing effective and efficient training interventions involves arranging the appropriate contingencies for participating in and valuing training programs.
OBM research in the area of training and development has evaluated the effectiveness of training in terms of its impact on behavior change, the relative effectiveness of different training programs, and the organizational context that best supports training effectiveness (Perlow, 2001). In terms of training context, Perlow maintained that focusing on specific behaviors is vital for effectiveness. In addition, before implementing a training program, one must determine whether the problem is a skill issue or a motivational issue. If the problem is due to motivation, then training skills will be ineffective (Mager & Pipe, 1997). In addition, the organization needs to support training initiatives; if employees believe that the organization is supportive of training, then training will be more effective.
OBM interventions often involve a reevaluation of employees’ pay structure. As Abernathy (1996) outlined in The Sin of Wages, the conventional pay structure in business often has misaligned reinforcement systems where employees receive payment based on time rather than on performance. In other words, employees’ pay is frequently not contingent on their behavior. Behavioral approaches to payment systems involve providing a more direct connection between an individual’s performance and his or her pay (Abernathy, 1990).
Abernathy’s (1996) suggestions included moving from a traditional to an incentive-based pay system that is tied to individual performance. Research on incentive-based pay systems shows that they increase productivity in the workplace (e.g., Bucklin & Dickinson, 2001). Of these effective pay schemes, there are several elements critical to performance improvement (see Smoot & Duncan, 1997). First, there must be objective measures of individual employees’ performance instead of a reliance on typical supervisor evaluations. If worker pay is tied to a subjective and mistrusted evaluation system, there will be significant problems not only with intervention integrity but also with employee acceptance and morale.
Another key feature is the timely and frequent availability of consequences. Employees should frequently see data on their performance or, if possible, track their own performance. This way, employees receive specific and immediate feedback, and the data come to function as conditional reinforcers (i.e., reinforcers that predict other reinforcers). Unfortunately, traditional payment systems rarely implement this type of feedback.
A third crucial characteristic of these payment systems is the close relation between employee performance and pay—what individuals do in the workplace directly impacts their pay. Finally, effective pay for performance systems should be based on targets available to all employees (i.e., benchmarks) rather than on relative standards, where only a few employees receive bonuses. For example, a pay system for real estate agents in which every agent who achieves a certain level of sales receives an exotic trip would be an example of a benchmark system. In contrast, a relative system would give the top 10 agents the exotic trip, regardless of how much they sold. OBM interventions focus on using benchmark pay systems, helping to avoid competition in the workplace, which can be damaging to performance.
If one examines these crucial characteristics of effective pay systems, it is obvious that they involve precise measurement of employees’ performance, appropriate goal setting, continuous feedback, and positive reinforcement. Each of these characteristics is typical of any OBM intervention for performance change.
Research in the area of leadership has traditionally used questionnaire or survey methods to understand leader effectiveness and performance. Typically, subordinates complete surveys regarding how often their leaders engage in general (not pinpointed) behaviors. Because these methods provide only indirect measures of what leaders do in the workplace, they are likely inaccurate and do not give researchers a full picture of leadership behavior (Komaki & Minnich, 2002). In addition, most research on leadership involves measurement at one point in time. However, it does not collect longitudinal data to determine how leader behavior or ratings change across time and circumstance. Finally, a reliance on average ratings of leader effectiveness can often miss important information about the variability of individual behaviors. By addressing leadership from a behavioral perspective, one can address these concerns, at least to some extent.
One significant OBM contribution to the area of leadership is the Operant Supervisory Taxonomy and Index (OSTI; Komaki, Zlotnick, & Jensen, 1986). The OSTI focuses on communication with followers, or “influencing workers to accomplish a desired goal” (Komaki et al., 1986, p. 260). To measure leadership performance, observers watch the leader behave on the job and record how often she engages in different behaviors. Specifically, the OSTI includes three components directly linked to behavior analysis: (a) antecedents, which provide information to subordinates about their performance; (b) monitoring of subordinates’ behavior; and (c) providing appropriate consequences. Observers can reliably use the OSTI to distinguish among managers. Moreover, because the instrument is reliable, different observers tend to get the same score when observing the same leader. In contrast, typical surveys of leadership ability are subjective and vary greatly depending on who is doing the measuring. In addition, typical evaluations of leadership find little variability across leaders, resulting in an inability to look at relations among leadership and other variables, or make predictions based on leadership scores.
Linking And Differentiating Industrial/Organizational Psychology And OBM
When students encounter information from OBM and industrial/organizational (I/O) psychology, understanding both literatures and the advantages and disadvantages of each can become confusing. In fact, although the two areas influenced each other to some extent, OBM and I/O lack “cross-fertilization” (Bucklin, Alvero, Dickinson, Austin, & Jackson, 2000, p. 37). Bucklin et al. presented a detailed review comparing these two fields, addressing the history, theory and concepts, common journals, methods, and topics of study for each. They also discussed the strengths and weaknesses of each as well as the benefits of learning from each other. Although OBM and I/O psychology have different roots and different histories, they ultimately have the same goals: to improve performance in the workplace and to encourage a healthy and satisfied workforce.
A primary difference between these two fields is that, from the outset, I/O psychology researchers did not have a single unifying theory on which to base their concepts and methods. Instead, I/O has included dozens of theoretical frameworks (e.g., individual differences, cognitive psychology) that address the problems at hand. I/O’s history contrasts starkly with OBM’s history in this way. OBM has its roots in behavioral “theory” (although see Hopkins, 1999, for a discussion of why some view this approach as “atheoretical”) and continues to be based on that perspective. Whereas much of the research in I/O psychology is theory-driven, OBM’s purpose is not to test theory explicitly but to describe, predict, and change behavior through direct application of behavioral principles.
Bucklin et al. (2000) also discussed the methods and types of problems addressed by the two areas. Most studies published in OBM journals are experimental in nature and predominantly conducted in field settings. In contrast, I/O research relies more on correlational and lab-based studies. In terms of content, OBM research focuses on productivity and quality of performance; I/O research, conversely, covers much more ground, including selection and placement, performance appraisal, and attitudes (Bucklin et al., 2000). Bucklin et al. also noted that although OBM has addressed these topics to some extent, the field would benefit from further examination of these more traditional I/O topics. Finally, the majority of work in OBM has had an applied rather than a theoretical focus, which has been more reminiscent of I/O studies.
Finally, Bucklin et al. (2000) discussed strengths and weaknesses of the two areas, along with recommendations for each. According to Bucklin et al., the primary strength of OBM is its focus on applied problems in the workplace; I/O research brings complex organizational topics to the table, along with social validity data that have not been a focus of OBM. By capitalizing on the strengths of each, significant advances are possible, and professionals from each area should be encouraged to collaborate and communicate.
Although relatively young, OBM has nevertheless delivered impressive results. Because OBM is rooted in behavior analysis, it tends to be parsimonious and practical, focusing its efforts on manipulating salient antecedents and consequences. OBM consultants have used this approach to make impressive strides in the areas of behavioral safety, leadership, behavioral systems analysis, incentive systems, and training. Nevertheless, there is much work to be done. For instance, OBM has only recently begun making contributions in the areas of selection and placement, two areas that have heretofore had their roots in traditional I/O psychology. Moreover, even though OBM and I/O have not had historically strong ties, research combining the strengths of these two approaches, as well as increased conversation between the two camps, is vital for achieving even more impressive organizational outcomes and employee satisfaction. In addition, OBM needs to focus further its efforts on validating applications in different workplaces and communities in order to evaluate the generality of its interventions. Because OBM rests on a solid scientific foundation and because there are numerous opportunities to apply behavioral principles in the workplace, OBM has the ability to make an impact in the world of organizational change.
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