Handbook of Principles of Organizational Behavior

Indispensable Knowledge for Evidence-Based Management

Edwin Locke

• Publisher: Wiley
• Year: 2011
• Print ISBN: 9780470740941, 9780470740958
• eBook ISBN: 9780470685334
• Edition: 2
• Topic: Betriebswirtschaft
• Subtopic: Organizational Behavior

There is a strong movement today in management to encourage management practices based on research evidence. In the first volume of this handbook, I asked experts in 39 areas of management to identify a central principle that summarized and integrated the core findings from their specialty area and then to explain this principle and give real business examples of the principle in action. I asked them to write in non-technical terms, e.g., without a lot of statistics, and almost all did so.

The previous handbook proved to be quite popular, so I was asked to edit a second edition. This new edition has been expanded to 33 topics, and there are some new authors for the previously included topics. The new edition also includes: updated case examples, updated references and practical exercises at the end of each chapter. It also includes a preface on evidence-based management. The principles for the first edition were intended to be relatively timeless, so it is no surprise that most of the principles are the same (though some chapter titles include more than one principle).

This book could serve as a textbook in advanced undergraduate and in MBA courses. It could also be of use to practicing managers and not just those in Human Resource departments. Every practicing manager may not want to read the whole book, but I am willing to guarantee that every one will find at least one or more chapters that will be practically useful. In this time of economic crisis, the need for effective management practices is more acute than ever.

Part I

SELECTION

1

Select on Intelligence

FRANK L. SCHMIDT

Other things equal, higher intelligence leads to better job performance on all jobs. Intelligence is the major determinant of job performance, and therefore hiring people based on intelligence leads to marked improvements in job performance - improvements that have high economic value to the firm. This principle is the subject of this chapter.

This principle is very broad: it applies to all types of jobs at all job levels. Until a couple of decades ago, most people believed that general principles of this sort were impossible in personnel selection and other social science areas. It was believed that each organization, work setting, and job was unique and that it was not possible to know which selection methods would work on any job without conducting a validation study on that job in that organization. This belief, called the theory of situational specificity, was based on the fact that different validity studies of the same selection procedure(s) in different jobs in the same organization and/or different organizations appeared to give different results. However, we now know that these “conflicting findings” were mostly due to statistical and measurement artifacts and that some selection procedures have high validity for predicting performance on all jobs (e.g. intelligence) and others do a poor job of predicting performance on any job (e.g. graphology) (Schmidt and Hunter, 1981, 1998). This discovery was made possible by new methods, called meta-analysis or validity generalization methods, that allow researchers to statistically combine results across many studies.

Meta-analysis has also made possible the development of general principles in many other areas beyond personnel selection (Hunter and Schmidt, 2004; Schmidt, 1992). For example, it has been used to calibrate the relationships between job satisfaction and job performance with precision (Judge, Thoresen, Bono, and Patton, 2001) and between organizational commitment and work-related outcomes including job performance (Cooper-Hakim and Viswesvaran, 2005).

WHAT IS INTELLIGENCE?

Intelligence is not the ability to adapt to one’s environment; insects, mosses, and bacteria are well adapted to their environments, but they are not intelligent. There are many ways in which organisms can adapt well to their environments; use of intelligence is only one possible way. Intelligence is the ability to grasp and reason correctly with abstractions (concepts) and solve problems. However, perhaps a more useful definition is that intelligence is the ability to learn. Higher intelligence leads to more rapid learning, and the more complex the material to be learned, the more this is true. Intelligence is often referred to as general mental ability (GMA) and general cognitive ability, and we use all these terms interchangeably in this chapter.

Intelligence is the broadest of all human mental abilities. Narrower abilities include verbal ability, quantitative ability, and spatial ability. These narrower abilities are often referred to as special aptitudes. These special aptitudes do predict job performance (although less well than GMA), but only because special aptitude tests measure general intelligence as well as specific aptitudes (Brown, Le, and Schmidt, 2006; Schmidt, Ones, and Hunter, 1992). It is the GMA component in these specific aptitude tests that predicts job performance. For example, when a test of verbal ability predicts job or training performance, it is the GMA part of that test - not the specifically verbal part - that does the predicting (Brown et al., 2006).

Intelligence predicts many important life outcomes in addition to job performance: performance in school, amount of education obtained, rate of promotion on the job, ultimate job level attained, income, and many other things (Brody, 1992; Herrnstein and Murray, 1994; Gottfredson, 1996; Jensen, 1998). It is even involved in everyday activities such as shopping, driving, and paying bills (Gottfredson, 1996). No other trait - not even conscientiousness - predicts so many important real world outcomes so well. In this sense, intelligence is the most important trait or construct in all of psychology, and the most “successful” trait in applied psychology.

The thousands of studies showing the link between intelligence (GMA) and job performance have been combined into many different meta-analyses. Ree and co-workers have shown this for military jobs (Olea and Ree, 1994; Ree and Earles, 1991, 1992; Ree, Earles, and Teachout, 1994), as have McHenry, Hough, Toquam, Hanson, and Ashworth (1990) in the famous Project A military study. (With a budget of 24 million dollars, Project A is the largest test validity study ever conducted.) Hunter and Hunter (1984) have shown this link for a wide variety of civilian jobs, using the US Employment Service database of studies. Schmidt, Hunter, and Pearlman (1980) have shown it for both civilian and military jobs. Other large meta-analytic studies are described in Hunter and Schmidt (1996), Schmidt (2002), and Schmidt and Hunter (2004). Salgado and his colleagues (Salgado, Anderson, Moscoso, Bertua, and de Fruyt, 2003a, 2003b) demonstrated the link between GMA and job performance across settings in the European countries. The amount of empirical evidence supporting this principle is today so massive that it is hard to find anyone who questions the principle.

There has been an important development since the first edition of this book appeared in 2000: a new and more accurate method for correcting for the biases created by range restriction has been developed and applied (Hunter, Schmidt, and Le, 2006; Schmidt, Oh, and Le, 2006; Schmidt, Shaffer, and Oh, 2008). (Range restriction is the condition in which variability of the predictor (here intelligence) in one’s sample of people (job incumbents) is artificially lower than in the population of people (job applicants) one wants to get estimates for.) Application of this procedure to existing data shows that previous estimates of the validity of GMA - including those in the 2000 version of this chapter - were underestimated by 25% to 30%. In this chapter, I present the updated, more accurate validity estimates. When performance is measured objectively using carefully constructed work sample tests (samples of actual job tasks), the correlation (validity) with intelligence measures is about .84-84% as large as the maximum possible value of 1.00, which represents perfect prediction. When performance is measured using ratings of job performance by supervisors, the correlation with intelligence measures is .66 for medium complexity jobs (over 60% of all jobs). For more complex jobs, this value is larger (e.g. .74 for professional and managerial jobs), and for simpler jobs this value is not as high (e.g. .56 for semi-skilled jobs). Another performance measure that is important is amount learned in job training programs (Hunter et al., 2006). Regardless of job level, intelligence measures predict amount learned in training with validity of about .74 (Schmidt, Shaffer, and Oh, 2008).

WHY DOES INTELLIGENCE PREDICT JOB PERFORMANCE?

It is one thing to have overwhelming empirical evidence showing a principle is true and quite another to explain why the principle is true. Why does GMA predict job performance? The primary reason is that people who are more intelligent learn more job knowledge and learn it faster. The major direct determinant of job performance is not GMA but job knowledge. People who do not know how to do a job cannot perform that job well. Research has shown that considerable job knowledge is required to perform even jobs most college students would think of as “simple jobs,” such as truck driver or machine operator. More complex jobs require even more job knowledge. The simplest model of job performance is this: GMA causes job knowledge, which in turn causes job performance. But this model is a little too simple: there is also a causal path directly from GMA to job performance, independent of job knowledge. That is, even when workers have equal job knowledge, the more intelligent workers have higher job performance. This is because there are problems that come up on the job that are not covered by previous job knowledge, and GMA is used directly on the job to solve these problems. Many studies have tested and supported this causal model (Hunter, 1986; Ree, Earles, and Teachout, 1994; Schmidt, Hunter, and Outerbridge, 1986). This research is reviewed by Schmidt and Hunter (1992), Hunter and Schmidt (1996), and Schmidt and Hunter (2004). It has also been shown that over their careers people gradually move into jobs that are consistent with their level of GMA (Wilk, Desmariais, and Sackett, 1995; Wilk and Sackett, 1996). That is, a process that sorts people on GMA takes place gradually over time in everyday life. People whose GMA exceeds their job level tend to move up to more complex jobs; and people whose GMA is below their job level tend to move down.

There is a broader theory that explains these research results: the traditional psychological theory of human learning (Hunter and Schmidt, 1996; Schmidt and Hunter, 2004). This theory correctly predicted that the effect of GMA would be on the learning of job knowledge. The false theory of situational specificity became widely accepted during the first eight decades of the 20th century in considerable part because personnel psychologists mistakenly ignored the research on human learning.

Many lay people find it hard to believe that GMA is the dominant determinant of job performance. Often they have known people who were very intelligent but who were dismal failures on the job because of “bad behaviors” such as repeated absences from work, carelessness at work, hostility toward the supervisor, unwillingness to work overtime to meet a deadline, or stealing from the company. These are examples of so-called “counterproductive work behaviors” (CWBs). Integrity tests predict CWBs with a validity of about .35 (Ones, Viswesvaran and Schmidt, 1993). People with lower scores on integrity tests show more CWBs. The personality trait of conscientiousness also predicts CWBs (again, negatively). However, a recent large-scale study (N > 800) found that GMA predicted CWBs with a validity of .47; when the more accurate correction for range restriction is applied, this figure becomes .57. So it is possible that the best predictor of CWBs is GMA. People who are more intelligent show fewer CWBs.

There is also a facet of job performance called “contextual performance” (CP). CP is just good citizenship behaviors, while CWB is bad citizenship behaviors as discussed above. CP behaviors include willingness to help train new employees, willingness to work late in an emergency or on a holiday, supporting the community relations and reputation of the company, and many other such behaviors. CP behaviors and CWBs are different from core job performance but are often confused with core job performance by lay observers. CP and CWB behaviors are predicted by measures of the personality traits of conscientiousness and to a lesser extent agreeableness (Dalal, 2005). We do not yet know whether GMA predicts CP behaviors; these studies have yet to be done. Low ability leads to an inability to perform well; low conscientiousness and low agreeableness lead, not primarily to low performance on core job tasks but to lack of CP and/or more displays of organizationally disruptive behaviors (CWBs). These disruptive behaviors are more visible to lay observers (and to many supervisors) than differences between employees in core job performance, probably because they appear so willful. On the other hand, a low ability employee has difficulty learning how to perform the job, but if he/she has a “good attitude,” this employee often seems like less of a problem than one showing CWBs. This makes it difficult for some to clearly see the GMA-performance link in the real world (Hunter and Schmidt, 1996).

Of course, low conscientiousness can lead to less effective performance if it results in reduced effort (see Chapter 2, this volume). For objective measures of job performance, empirical evidence indicates that on typical jobs this effect is limited, probably because most jobs are fairly structured, reducing the scope for individual differences in effort to operate (Hunter, Schmidt, Rauschenberger and Jayne, 2000; Hunter and Schmidt, 1996). However, it is important to remember that when supervisors rate job performance, they incorporate into their ratings both CP behaviors and CWBs, in addition to core job performance (Orr, Sackett, and Mercer, 1989; Rotundo and Sackett, 2002). Hence supervisory ratings reflect a combination of core job performance and citizenship behaviors, both good and bad. In the case of ratings, low conscientiousness and low agreeableness lead to poorer citizenship behaviors, which lead to lower ratings of overall performance. For the typical job, the weight on conscientiousness in predicting objectively measured core job performance is only 20% as large as the weight on GMA. In predicting supervisory ratings of job performance, it is 40% as large (Schmidt, Shaffer, and Oh, 2008).

WHAT IS REQUIRED TO MAKE THIS PRINCIPLE WORK?

There are three conditions that are required to make this principle work. That is, there are three conditions that are required for companies to improve job performance levels by using GMA in hiring and to reap the resulting economic benefits.

Selectivity

First, the company must be able to be selective in who it hires. For example, if the labor market is so tight that all who apply for jobs must be hired, then there can be no selection and hence no gain. The gain in job performance per person hired is greatest with low selection ratios. For example, if one company can afford to hire only the top scoring 10%, while another must hire the top scoring 90% of all applicants, then with other things equal the first company will have a much larger gain in job performance.

There is another way to look at this: companies must provide conditions of employment that are good enough to attract more applicants than they have jobs to fill. It is even better when they can go beyond that and attract not only a lot of applicants, but the higher ability ones that are in that applicant pool. In addition, to realize maximum value from GMA-based selection, employers must be able to retain the high performing employees they hire.

Measuring general mental ability

Second, the company must have some way of measuring GMA. The usual and best procedure is a standardized employment test of general intelligence, such as the Wonderlic Personnel Test. Such tests are readily available at modest cost. Less valid are proxy measures such as grade point average (GPA) or class rank. Such proxy measures are partial measures of intelligence. Also, intelligence can be assessed to some extent during the employment interview (Huffcutt, Roth, and McDaniel, 1996), although this is a much less valid measure of GMA than a standardized written test.

Variability in job performance

Third, the variability in job performance must be greater than zero. That is, if all applicants after being hired would have the same level of job performance anyway, then nothing can be gained by hiring “the best.” This condition is always met. That is, on all jobs studied there have been large differences between different workers in quality and quantity of output. Hunter, Schmidt, and Judiesch (1990) meta-analyzed all available studies and found large differences between employees. In unskilled and semi-skilled jobs, they found workers in the top 1% of performance produced over three times as much output as those in the bottom 1%. In skilled jobs, top workers produced 15 times as much as bottom workers. In professional and managerial jobs, the differences were even larger. These are very large differences, and they are the reason it pays off so handsomely to hire the best workers.

There is another advantage to hiring t...

Table of contents

• Title Page
• Copyright Page
• Contributors and Editor
• Preface
• Introduction
• Part I - SELECTION
• Part II - TRAINING AND PERFORMANCE APPRAISAL
• Part III - TURNOVER AND SATISFACTION
• Part IV - MOTIVATION
• Part V - TEAM DYNAMICS
• Part VI - LEADERSHIP
• Part VII - ORGANIZATIONAL PROCESSES
• Part VIII - ENTREPRENEURSHIP
• Part IX - WORK, FAMILY, TECHNOLOGY, AND CULTURE
• Index