Redesigning Psychology : In Search of the DNA of Behavior 9789460949326, 9789462360532

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Redesigning Psychology

If you don’t know what you don’t know, you don’t know what you know

Redesigning Psychology In Search of the DNA of Behavior

Theo Poiesz

Published and distributed in the UK by: Pavilion Publishing and Media Ltd Rayford House School Road Hove BN3 5HX UK Tel: +44 (0) 1273 434943 Fax: +44 (0)1273 227308 Email: [email protected] Web: www.pavpub.com A catalogue record for this book is available from the British Library. UK ISBN: 978-1-909810-33-4 Published, sold and distributed by Eleven International Publishing P.O. Box 85576 2508 CG The Hague The Netherlands Tel.: +31 70 33 070 33 Fax: +31 70 33 070 30 e-mail: [email protected] www.elevenpub.com Sold and distributed in USA and Canada International Specialized Book Services 920 NE 58th Avenue, Suite 300 Portland, OR 97213-3786, USA Tel: 1-800-944-6190 (toll-free) Fax: +1-503-280-8832 [email protected] www.isbs.com Eleven International Publishing is an imprint of Boom uitgevers Den Haag. ISBN 978-94-6236-053-2 ISBN 978-94-6094-932-6 (E-book) NUR 770 © 2014 Theo Poiesz | Eleven International Publishing This publication is protected by international copyright law. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publisher. Printed in The Netherlands

Preface

The basic idea of this book sprang to life on a beach in Vouliagmeni, Greece, about two decades ago. It suggested an avenue to understanding behavior that was quite different from mainstream psychology. I was not in Vouliagmeni to enjoy its beaches but to attend a conference. Here, a presentation at one of the seminars showed conflicting results on a particular behavior phenomenon. Although conflicting results happen to be an inevitable aspect of academic life, this time they made me realize that I, as a psychology professor, failed to understand the very basics of behavior. It was an embarrassing moment. But then the idea struck. I left the seminar and crossed the street to the adjacent beach, where my suit and tie did not remain unnoticed among the swimming trunks and bikinis. Deeply puzzled I began to elaborate on the idea and started the process the result of which is now in front of you. This book presents a new approach to the understanding of behavior. Obviously, I did not construct the approach on the beach nor did it take me 20 years to write the book. But it did require considerable time and effort before it had matured enough to present it to an international audience. The initial beach idea was adapted, corrected and extended repeatedly. New theoretical arguments and conceptual elements were added on a regular basis so that, gradually, a behavior model – dubbed ‘Triad model’ – emerged. In a sense, developing it felt like building a house of cards. Just as misplacing a single card might cause a complete house of cards to collapse, the detection of a single flawed element could render the whole model worthless. But over the years, with the help of different types of input and feedback (both positive and negative, from both academics and practitioners) the structure of the model became more solid and less sensitive to poking fingers. In the process of model construction I frequently implied academic knowledge and practical insights. This was facilitated by my position at TIAS, the business school of the universities of Tilburg and Eindhoven in the Netherlands. It allowed me to draw, on the one hand, from the academic literature and the insights of fellow professors; on the other hand, I learned from the practical experiences and feedback obtained in interactions with managers

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and professionals in executive teaching and consultancy – as I hope they learned from me. Almost invariably, practitioners welcomed the model. It helped them to acknowledge that behavior is an important catalyst in policy implementation, it showed them why behavior often takes a different course than presumed, and it gave them an instrument to work with. By comparison, when the model was suggested to academics their initial reaction was predominantly negative. It usually did not elicit a fair, balanced judgment and seemed to be rejected instantaneously on purely emotional grounds. For most academics the contrast between the simplicity of the Triad model and the esoteric, sophisticated complexity of their own approaches was apparently more than they could handle. In recent years the tone of the discussion has changed, however. An increasing number of academic psychologists now indicate that they are somehow intrigued by the model’s potential. Representatives of related academic disciplines have also developed an interest, partly because the model is believed to offer a broad explanatory promise and partly because it allows them to bridge interdisciplinary gaps. The behavior model presented in this book has thus emerged from a process to which both academics and practitioners have contributed in the past decade. Their continued input and feedback is essential to advancing the ongoing process of theory building. In principle a theory is never complete and is always open to adaptation, verification and falsification; it constantly needs correction, elaboration, amendment and refinement. The same holds true, of course, for the Triad model. Readers are invited to present arguments and examples that are consistent with or run counter to the theory. A ‘card’ may be spotted that, when pulled out, ruins the entire theoretical structure. Arguments and examples from both academia and practice, whether positive or negative, are welcome. Input and feedback contributed to the construction of the model over the past decade; further criticism will be gladly received in the next. For whom is this book? The book was written for academic psychologists, academic researchers in the broader area of behavioral sciences, professional psychologists, analytically oriented practitioners and students of the behavioral sciences. It is for those who are interested in a general framework for the diverse approaches and insights relating to behavior.

Preface

Academic psychologists who are concerned about the problem of fragmentation of their discipline have expressed an interest in the potential contribution of the approach presented in this book. The approach suggests that the origin of behavior may be much simpler than they generally assume. Academics who strongly adhere to the conventions of mainstream psychology may reject the approach because it deviates too strongly from the dominant contemporary line of thinking. However, the Triad model is not intended to conflict with any approach, but rather to amend the various ways of thinking about behavior. One amendment concerns the methodological implications of the Triad approach. It shows why and how current research may be more restricted or even biased than is generally assumed. Professional psychologists tend to be interested in the Triad model because it structures their thinking, sharpens practical recommendations and fosters communication with clients and academic colleagues. They often express a concern about the widening gap between fundamental and applied psychology and about the resulting image of psychology in society. The model hopes to reduce this gap and contribute to a reassessment of the discipline’s reputation. Analytically oriented practitioners who see behavior more as a puzzle than as a problem are intrigued by the model because it triggers them intellectually and helps them to design policy programs and measures more systematically. This book will certainly disappoint practitioners who hope to find a list of directly applicable ‘tips’. To them it hopes to show that behavior is rather more complex than they assume. For students of psychology the Triad model may serve as a handy tool to summarize and integrate the various and possibly fragmented insights they encounter during their studies. The book suggests that the paradigm in which they are educated may not be so flawless and self-evident as appears at first sight. Finally, representatives of behavior disciplines other than psychology may be surprised to see how the model facilitates cross-domain connections and comparisons. At a general level, there may be a common theoretical ground to which all behavior fields implicitly refer. Theoretical feuds bitterly contested in and between these fields may, in the end, be only artificial and superfluous in nature. The book hopes to contribute in some modest way to the formation of unity or ‘consilience’ as called for by Wilson (1998). In

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his impressive book with the same title, he argues that sciences and scientific approaches should invest in finding interconnections, even if they address very different theoretical and conceptual issues. It is only through such interconnections that a more complete understanding of complex behavior phenomena may evolve. In sum, the book’s ambition is to present readers with a new approach to behavior that they can relate and refer to, irrespective of the nature of their particular interest. In spite of their differences, they share a deep interest in the universal phenomenon of behavior. Reading guide Academic psychologists, professional psychologists, other behavioral scientists and students are recommended to read the full text, although some academics may prefer to disregard the practical examples. The text may be read without these. Practitioners may not be overly interested in the assets and problems of psychology as an academic discipline. They may prefer to read the main texts of Chapters 2 and 3 ‘diagonally’, and consult their summaries as a preamble to Chapter 4, where the foundation of the new approach is presented. Acknowledgements I want to thank many individuals, both in academia and in practice, whether in the Netherlands or elsewhere, for their contributions to the ‘redesigning’ that led to this book. It is impossible to credit everyone concerned, but I do want to mention a few people who were, in some way, particularly helpful: (in alphabetical order) Stefan Bogaerts, Jo Caris, Ruud Drabbe, Ton Kuijlen, Mohamed Nabih, Jeske Nederstigt, Ad Pruyn, Gery van Veldhoven and Patrick Vermeulen. I am grateful to my wife Elly and my children Thijs and Marieke for their valuable support.

Table of Contents

1 1.1 1.2 1.3

Introduction Chapter summary The behavior puzzle The goal of the book

11 11 12 24

2 2.1 2.2

The development of psychological knowledge Chapter summary Psychology and its contributions

35 35 35

3 3.1 3.2 3.3 3.4

Limitations of psychology Chapter summary Problems, limitations, risks and pitfalls Limitations of psychology: summing up Towards unification

49 49 49 72 75

4 4.1 4.2 4.3 4.4 4.5 4.6

Back to basics Chapter summary People Behavior Towards a basic model A three-factor behavior model Conclusion

85 85 85 86 91 97 104

5 5.1 5.2 5.3 5.4

The Triad model Chapter summary Introduction Triad model application conditions Triad model propositions Triad determinants and values Triad value assessments Triad model basics Behavior and behavior aspects Affect, satisfaction and wellbeing Intrinsic and extrinsic aspects Balance and compensation effects Emotions

107 107 107 109 111 112 123 125 142 148 150 157 183

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Individual differences

189

6

Triad model conclusions

191

7

Overall conclusions

203

8

Recommendations

207

Epilogue

211

References

213

About the author

225

Introduction 1.1

Chapter summary

This introductory chapter starts by identifying the object and the objective of the book. It addresses the nature of human behavior as an important and intriguing part of life. Behavior is a ubiquitous phenomenon – it is all around us – and it is critically relevant for a number of reasons. Behavior often serves as an important instrument but it may also prove to be a burden. In our daily routines and decisions we are more or less successful in dealing with our own behavior and that of others, but to what extent do we truly understand it? More specifically: which assumptions about behavior are misguided or downright wrong? What mistakes do we make without knowing it? And what do the answers mean for the quality of academic research and for the effectiveness of policy decisions? Psychology is one of the behavior sciences. It focuses on the explanation, prediction and influence of individual behavior. Psychological insights contribute to the quality of life and to the functioning of groups, organizations and society. To underline its importance as a discipline, this chapter presents a random selection of useful insights produced by psychology. However, it also identifies serious limitations that negatively affect its position, reputation and development. Arguments will be presented for the conclusion that its progress decelerates dramatically due to effectiveness and efficiency problems. This conclusion calls for an alternative approach that contributes in an unconventional way to behavior understanding.

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1.2

The behavior puzzle

Let’s start with a few random examples of behavior: Mrs Johnson has not been feeling too well for a number of days. After diagnosing her, the doctor prescribes antibiotics. These need to be taken three times daily, five days in a row. The doctor tells her to complete the full cure. At the pharmacy, she is informed again that it is important to take all the pills. Finally, the same instruction is given on the leaflet that comes with the pills. After two days, mrs. Johnson’s symptoms have dissipated considerably. She feels so much better that she decides that taking the rest of the pills is a waste. She puts them in her medicine cabinet in the bathroom as a buffer: they may come in handy in the future. Or she may give some to her neighbor who occasionally also has health problems. Because of a thick fog, traffic signs instruct motorists to slow down considerably. However, few do. And if they do, it is either late or too late. The board of a company has developed a new strategy that is communicated to all employees. The overall reaction is positive. However, three months later, the same board cannot help noticing that nothing has changed. The organization functions in the way it always has in the past and everyone is doing his or her work as usual. After watching a TV show with Vladimir Horowitz playing Franz Liszt, Johnny signs up to have piano lessons. After a few months, he quits. The launch of a new product is accompanied by an ambitious and expensive advertising campaign. International celebrities are hired to endorse the product in an exotic, tropical setting. Experts agree that the commercial is stateof-the-art and has been created with great skill. It certainly does boost the sales. However, figures show that the largest impact on sales goes to the competing product – for free. When safety measures are installed in cars, the average speed increases, reducing the predicted safety effect. New software is made available to the employees. It will help them to work much more efficiently. When it is introduced, the employees are informed that they can get used to the new software immediately. After a month the

Introduction

old software will be removed from the system. When this eventually happens, the results are disastrous. Production slows down dramatically, the number of mistakes increases significantly and the emotional atmosphere in the office slumps to a state of severe depression. A manager has an excellent job record. Every few years he is promoted. Then, after three months on the latest assignment, he suddenly fails and is fired. His name happens to be Peter (as in the Peter Principle). The rising fat and sugar consumption in a country results in increasing health risks. The proportion of the population that is overweight or even obese is growing fast. People ‘know’ that particular food habits are dangerous for their health and may even reduce their life span. Yet they continue their habit of eating a lot of sweet and fat food and of moving too little. Harry, the newly appointed chairman of the business club, is to give his first 10-minute presentation at the New Year’s party. He is inexperienced as a speaker and is therefore rather nervous. He carefully writes down the speech and repeats it so often in front of the mirror at home that he knows the text by heart. After he is announced, he climbs on to the stage and is immediately seized by a panic attack. He manages to address the ‘ladies and gentlemen’ in a remarkably high-pitched voice, but after that all body parts vital for giving the talk refuse to cooperate. The likelihood of winning the lottery is negligible. Yet its popularity is high. If the amount of money of the jackpot is raised and the likelihood of winning is reduced even further, more tickets are purchased. People hope that others will support them if they are confronted with deprived circumstances. However, when they are told about other people’s bad luck in distant countries, help is often marginally provided, if at all. Johnny’s only extracurricular activity is to play video games. His parents encourage him to become active in sports. The only sports that interest him are the ones that he can watch on a screen – while seated. When his parents tell him that true sports will be out of the question for a lengthy period for financial reasons, he develops an interest. People’s behavior is often consistent with our expectations. Too frequently, however, people surprise us by acting in ways that are disadvantageous to

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themselves, to others or to society as a whole. When we observe the behaviors of others, the words ‘logic’ and ‘rationality’ are often not the first that come to mind. We may even astonish ourselves by behaving in ways that seem inconsistent with who we are or who we hope to be. There are numerous ways to influence behavior, but everyday experience shows that many attempts are unsuccessful. It is important to understand why this is so. Only an adequate understanding of behavior will provide the basis for effective precautionary and corrective measures. Human behavior is a fascinating and at times even perplexing phenomenon. It is omnipresent, continuous, varied, variable, volatile, and versatile. And it is critically important for a broad range of reasons. Thanks to their behavior, people survive, cope and grow. It allows them to deal with their physical, professional and social environments and it supports them in their attempts to control and improve their living conditions. Through their behavior, as expressed in an endless variety of acts and activities, people create opportunities and often produce impressive results. They plan, design, construct, produce, innovate and implement. And they succeed or fail in the process. Along the way they interact, communicate, educate, teach, learn, cooperate, compete, lead, follow and help. From time to time they fight and make war, usually only to make peace later. Behavior is functional for reaching diverse goals. Because of behavior, human life is possible. People exist because of it; behavior gives life meaning. Behavior is life, creates life, improves life, threatens life and, in dramatic cases, stops life. Without behavior society would come to a screeching halt. By means of their behavior people contribute to the wellbeing of themselves and others. However, as we know too well, behavior may also lead to unfavorable outcomes. Behavior may be constructive and destructive. It can ruin, delay and impede positive results and thus can cause irritation, fear, sadness, disappointment, regret and concern. In the ideal case, behavior is readily available, effective and efficient. Effective behavior means that the intended result is reached; efficient behavior requires a reasonable amount of scarce resources like time, money, attention and effort to realize the intended effect. Everyday life presents a multitude of examples in which behavior plays an important role but does so ineffectively or inefficiently. For this reason, people try to steer, influence or change behavior in many ways. They may force, forbid, instruct, manipulate, inform, deceive, persuade, reward, punish, facilitate, support, encourage, request, beg or threaten.

Introduction

People try to influence other people’s behaviors in many different ways, but may be unsuccessful in doing so. Attempts to change behavior often end in failure. This is a blessing when it curbs the influence of destructive, criminal or fraudulent activities. However, if positive goals and intentions are at stake, unsuccessful attempts to change behavior may cause a threefold problem: the goal is not reached, scarce resources are wasted and the opportunity to engage in alternative measures is foregone. Therefore, if vital interests and serious problems are at stake, we can reasonably expect the utmost care to be taken in designing and executing behavior change programs. But, surprisingly, trial and error approaches tend to prevail. People often draw from a haphazard collection of ad hoc behavior strategies and tactics. And if these prove unsuccessful, they are unlikely to learn from the experience, attributing their failure to the irrationality of the target audience or simply to bad luck. In future situations they tend to rely overly on measures that have worked in the past, even if these related to different goals, audiences and circumstances. The problem is that, for several reasons, understanding behavior is – or seems to be – hopelessly complex. No two persons behave exactly alike. Different people may behave differently because of the same motive. Or they may show the same behavior for different reasons. What is more: the same individual may behave differently under different circumstances and at different points in time. These observations lead to the conclusion that behavior is both consistent and chaotic, static and dynamic, placid and volatile, active and passive, and deliberate and impulsive. Behavior may take a seemingly limitless number of directions, rendering it a slippery, ungraspable phenomenon to understand, predict and influence. Because of the risks involved, it would be reasonable to expect decision makers to approach behavior in a very careful and systematic fashion. However, they tend to prefer intuitive and implicit approaches that may be referred to as ‘escape tactics’: Approach 1: Aggregation If we see hundreds of trees, we refer to a forest. If we notice hundreds of individual persons at the same time, we call that a crowd (the effect is referred to as ‘chunking’, see e.g. Cohen and Glicksohn; 2011; Gobet et al., 2001). In general, if it is impossible to understand a particular behavior of a particular person in a particular situation at a particular moment in time,

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we may aggregate over behaviors, over persons, over situations and/or over time. ‘Nobody can be trusted’ is an example of a rule of thumb at a high level of aggregation or generalization. When the level of aggregation is raised, deviations from the average behavior are progressively ignored. Variance (a summary indicator of inter-individual differences) is collapsed so that the different individual behaviors in a group are equated with the average behavior. While aggregation is comfortable, it is not without risk. As aggregation and behavior understanding are inversely related, the clarity and straightforwardness suggested by aggregation may be purely artificial. A crude average leads to a crude level of understanding. Aggregate observations tend to correspond to what is considered common sense. For example: ‘People buy more umbrellas when rain is forecast’. While not incorrect as such, from a policy standpoint the prediction has little news value. It seems more interesting to understand why, when rain is expected, some people buy umbrellas and some do not. The quality of policy decisions is better served by understanding why people behave differently than by understanding why they behave similarly. So aggregation does not present the ideal approach. Approach 2: Reliance on past experiences Although the present and the future may be quite different from the past, rendering mere extrapolations quite risky, our past experiences may be the only basis for behavior understanding when additional information is absent. They provide the second indirect approach to behavior understanding. This approach appears in fact to be the most prevalent – not only among practitioners (non-psychologists) but possibly among psychologists as well. In general, people expect other people to behave consistently. In fact, we appreciate it when they do so (Cialdini, 1993). We assume that people’s needs and preferences are quite consistent, that their competencies do not vary significantly over time and also that their circumstances are fairly stable. So the best bet is to expect the same behavior to reoccur under similar conditions. This may explain why policymakers rely sometimes overly on measures that have worked in the past. While history allows us to accumulate behavioral insights, these insights often prove to be misleading. People tend to change over time and may be very sensitive to small variations in their situations. The problem with past experiences as a basis for behavior understanding is that there is no such thing as a formal or systematic inventory of experien-

Introduction

ces. People just simply accumulate experiences in their memories where, in the course of time, they are likely to be transformed. Experiences may be simply forgotten, integrated with existing memories, washed out by new information or modified to serve psychological needs. For example, the storage of experiences and retrieval of memories is a process that can be seriously distorted by personal preferences and biases (for a review of selectivity in perception, information processing and memory see e.g. Pronin, 2007). In any case, memories of past experiences may fade, change or polarize, rendering them highly unreliable and unstable. So experiences also provide a shaky basis for behavior understanding. Approach 3: Introspection Introspection (see e.g. Wilson and Dunn, 2004) is a systematic, internally focused search for the explanation of behavior. Introspection involves digging deeper than the obvious first impression with one critical question: ‘Why do I do what I do?’ The problem with introspection is that it cannot be falsified. The introspectionist is ‘always right’. Sometimes the outcome of introspection is used to explain or predict the behavior of others. Then the person who performs introspection assumes that s/he may serve as a substitute for the target person. This may work, to some extent, for twins, but to the extent that the psychological ‘distance’ to the other person increases (or the similarity between the persons decreases), introspection seems less justified as an information source for behavior understanding. The subjectivity that is inherent in introspection renders the approach extremely risky if the target person is not known in depth. On the other hand, very good knowledge of the person seems to compromise independent and unbiased judgments. Because it is easy, quick and self-serving, introspection is a popular approach. Usually we do not label it as such. We often wonder what we would do if we were the other person in his/her circumstances. In doing so we project ourselves onto that other person. Policymakers often use introspection to estimate the future success of a measure by addressing the question: ‘How would I react if I were the target person?’ Whatever the answer is, its validity is reduced by the simple fact that the policymaker and that target person are two different persons. This rules out introspection as a viable approach to behavior understanding.

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Approach 4: Assumption of rationality The rational approach is based upon the assumption that people tend to do what is in their best interest. The general idea is that they tend to maximize positive outcomes and minimize costs. In the rational approach people oversee their behavior options and are capable of comparing these options. If mistakes are made, for example by selecting a suboptimal option, they are assumed to make corrections to prevent the error from reoccurring. In this way rational behavior is assumed to be a self-optimizing phenomenon. The rational approach to behavior understanding is quite attractive for the user as it helps to make quite consistent predictions. What is more, if the behavior options and their outcomes are known, the likelihood of some future behavior may simply be ‘calculated’. The rational model has long been the central model of economics. But elaborate research in economic psychology and behavioral economics has made clear that this model has serious limitations as well (see, for example, Kahneman, 2003; Kahneman and Tversky, 1979). The problem is that people may not oversee their options, they may not be able to compare them, they may not be interested in the best alternative, they may even select a behavior option that does not maximize outcomes or minimize costs (think, for example of charity donations and volunteer work), or they may engage in behaviors that hold high risks – do something just for ‘the kick’ of it). These everyday examples show that the rational approach cannot fully account for behavior and should be supplemented with – or even replaced by – other approaches more reminiscent of reality. So there are quite a few comfortable escape routes that policymakers may adopt in order to avoid the hassle of careful decision making on the behavior of target persons. It should be noted that the risks associated with these approaches do not seem to dampen the policymakers’ self-confidence. They tend to be overly optimistic when it comes to behavior explanations and predictions. Kahneman (2011) reviews evidence showing that not only fast, intuitive thinking may be subject to biases – deviations from what may be expected on purely logical grounds, but slow and analytical thinking as well. So neither intuitive nor elaborate decision making can guarantee valid behavior explanations and predictions, nor can they assure effective policies to change behavior. For that reason we need an approach that may help us enter the ‘black box’ of the mental processes of target persons more systematically. One way of doing so is to ask them directly what they do or plan to do, and why. That is, by doing research. For example, policymakers may hire a research agency

Introduction

to conduct a survey. While this approach seems foolproof at first sight, it suffers from several shortcomings. Three are discussed briefly here. 1. Too often, policymakers judge the quality of a particular research method at face value, not realizing that the choice of method may lead to misinterpretation of the results and, consequently, to erroneous explanations and predictions. However, compared to the other approaches to behavior understanding described earlier, research does provide the possibility of validity checks. For example, questions may be included to see whether the pattern of responses is consistent. But policymakers tend to feel that the extra costs outweigh the validity surplus. Validity is often not an issue for self-confident policymakers with tight budgets, while cost is. Introspection is definitely ‘cheaper’. 2. People’s statements and explanations regarding their own behavior may deviate from objective measures of that behavior. For example, even for a rather straightforward type of behavior like engaging in physical activity, Prince et al. (2008) concluded that ‘self-reported and directly measured physical activity can differ greatly’. This conclusion was based on a review of 187 studies. Also the reasons that respondents indicate for their own behavior may not reflect their actual or true reasons. This is particularly true for reasons that are ‘personally sensitive’ (see, for example, the so-called ‘social desirability effect’ indicating that people are reluctant to freely give answers that might prompt negative evaluative reactions from others). There are methodologies which avoid the problem altogether. For example, one may use field experiments or unobtrusive indicators. Here people are not even aware of participating in a study. However, these methods have other limitations and are used relatively infrequently. 3. Questionnaires used in a survey often consist of items that are produced by the policymakers themselves. After all, they know what they want to know about behavior. But here they face a circular problem involving two elements: a. Research only helps to better understand behavior when the critical questions are asked, and b. The critical questions can only be known if behavior is adequately understood. So when it comes to understanding behavior, the three general approaches (intuitive thinking, more elaborate thinking and using behavior surveys) provide precarious options only. The resulting paradox is almost painful: behavior is all around us, we live with it every waking moment, we observe our own behavior and that of others, we accumulate behavior experiences day after day and people often communicate about the reasons for their

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behavior, and yet: behavior is poorly understood. Hidden behind this paradox is yet another: while the problem is apparent and significant, it does not seem to bother us. The same biases that prevent us from making adequate behavior explanations and predictions prevent us from understanding and accepting our own limitations. We translate our presumed understanding of past behavior into optimism with regard to the prediction of future behavior. And if we correctly predict that some behavior will take place, we implicitly assume that we also understand why it takes place. If we truly understand a particular behavior, it is easy to predict it. But we should not reverse the order: the fact that we can predict some behavior should not allow us to infer that we understand it. A few examples: we can quite adequately predict that most people watch television in the evening, that the neighbor mows his lawn on Saturday mornings and that people use their heating systems more in winter than in summer. We just know, on average, at what time colleagues come in to work every day, when they drink coffee, when they have their lunch, and so on. This might give us the impression that we fully comprehend what is going on. However, if we had to demonstrate our understanding by explaining these behaviors for different people – question marks would be scattered all over. For example, some people watch television because they are actually interested in the show. Others may watch TV to combat boredom, escape reality, find a topic for conversation, avoid conversation, have a rest, forget work, or simply to pass the time, not having anything else to do. People may be engaged in sports because of their built-in ambition, in order to lose weight, beat others, impress fans, get rid of surplus energy, enjoy the activity or to keep up the habit. Simply knowing that a behavior takes place does not signify why it takes place. Attempting to understand behavior is like trying to understand how a leaf that has fallen from a tree is transported by the water of a mountain stream. We know that water obeys the laws of gravity and runs from the highest to the lowest point. And so does the leaf. But how this translates at a more specific level into a particular route between the many rocks and pebbles is impossible to figure out beforehand. Similarly, we know that people, in general, behave in order to survive, to reach goals and to avoid serious risks and dangers. However, what this means at the level of individual behavior in concrete daily situations tends to be a mystery. For general types of behavior, we are quite effective in making crude predictions, like: ‘When a house catches fire, the inhabitants will try to put it out’. We label this type of understanding as ‘common sense’. It is common because the relevant notion develops over generations and is shared by the vast majority of peo-

Introduction

ple. It makes sense because it reflects our rock-bottom knowledge that people generally try to prevent danger and protect or save what is valuable to them. However, common sense is not helpful when people have different behavior options that call for more finely tuned predictions. If a house actually catches fire, some people try to extinguish it, some run around yelling for help, some dial 112 (or 911 depending upon the country), some call the fire department, some save only themselves, some – paralyzed by shock – just stare at the flames, and some risk their lives to rescue the goldfish. Policymakers responsible for public safety or fire fighters should be interested in knowing whether, how, how often and especially why these various behaviors occur, because it would allow them to design and implement more effective safety protocols and rescue procedures. If policymakers lack an adequate behavior understanding, they have to revert to more shaky bases for their decisions. They may opt for a trial and error approach in which success and failure are determined primarily by intuition and chance. Obviously, this latter remark is not meant to suggest that policy decisions are made at random. Many involve careful trade-offs and are effective. Fortunately, society is not a complete chaos. On the other hand, careful decisions on behavior do not imply success. There are simply too many unexpected, unwanted and disappointing effects or side effects, which implies that a systematic approach to behavior is badly needed. For such an approach we may turn to a discipline that attempts to do just that: psychology. It focuses on the scientific understanding of the behavior of individual persons and has a dual goal: (1) to broaden and deepen the understanding of behavior for academic purposes – that is, theory formation, and (2) to foster successful application of behavior theories, models and concepts in real life contexts. In the psychological literature many behavior models have been presented. These apply to particular types of behaviors (e.g. behaviors of employees, children, consumers, criminals, victims, motorists, sportsmen and women, judges, members of a jury and addicts, and behaviors relating to health, sex, suicide, media usage, religion, conflicts, interaction and communication), particular processes (e.g. learning, information processing, attitude change, decision making, persuasion, coping and need development), particular effects (e.g. satisfaction, happiness, attribution, dissonance reduction, reactance, withdrawal and adaptation), and/or particular behavior aspects (e.g. biological, neurological, personality, social and cultural aspects). The many possible combinations result in an abundance of models that, to some extent, may meet academic needs but do not necessarily match the need of practitioners. The latter pre-

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fer a single, simple and dependable model that can be applied indiscriminately to different types of behaviors by different types of persons in different types of settings. They want, in short, a handy and versatile instrument that works for them. There have been several attempts to introduce more generally applicable models. The most widely known are the theory of reasoned action (Ajzen and Fishbein, 1980) and the theory of planned behavior (Ajzen, 1985). These models predict behavioral intentions and behavior ‘quite well’ (that is, they can account for about 30 per cent of the variance) (Madden et al., 1992). Although this result is respectable, it does mean that quite a lot of variance is still unaccounted for. But there are also other characteristics that render them not very suitable for practical purposes. For example, the number of variables to be considered is relatively high and the methodological and analytical requirements are too elaborate to readily apply the models in everyday practical situations. Both academic researchers and practitioners would benefit from a generally accepted and universally applicable behavior model – an analytical instrument that represents (virtually) all behaviors and behavior determinants. Existing behavior models range widely in scope. The simplest models only describe the relationship between a predictor variable and a criterion variable; other models are more comprehensive and involve multiple determinants and consequences. Specific models relate to particular behaviors, particular types of persons, particular situations and particular time frames. Ideally, however, a model applies indiscriminately. A good model is effective and efficient. It is effective if its validity allows for an accurate description, explanation and prediction of behavior. It is capable of successfully guiding behavior interventions. It may be generalized to many behaviors, persons and situations, yet also be sensitive to finer differentiations. An efficient model adheres to the rule of parsimony: it contains as few elements as are necessary to be effective. Einstein stated: A scientific theory should be as simple as possible, but not simpler (see Hawking and Mlodinow, 2010). After all, a theory is supposed to simplify things, not to complicate them (a notion that some practitioners reverse by thinking that a theory can only mean trouble). A model is also efficient if it stimulates communication between academics and practitioners. Academic models may be effective in the particular behavior domain for which they are developed. In most cases, however, their efficiency is limited. Highly sophisticated and esoteric models may be effective but tend to be also highly inefficient.

Introduction

This book is based on the presumption that the development and dissemination of behavior knowledge is not the exclusive domain or privilege of academic researchers. Practitioners too develop very useful knowledge that is passed on through professional education, expert media and informal communication. Both academic and practical approaches to behavior have advantages and disadvantages, assets and shortcomings. For example, the rigorous and systematic approach that academic researchers exhibit is aimed at the benefit of having reliable, valid and generalizable results. Unfortunately, these benefits may be limited by the rather artificial nature of the research settings in which the insights are obtained so that research findings may not correspond to real-life effects. Conversely, practical behavior insights often lack a rigorous, systematic basis, but are valuable as they emanate directly from on-the-spot observation and application. So academic and practical insights may correct and supplement each other. If academics produce a behavior model, practitioners can be invited to indicate whether it ‘works’. If practitioners suggest a behavior insight, academics can reflect on the basis of scientific knowledge. Both parties should be open to input and feedback from the other party and neither party should judge itself superior to the other. In order for open communication to take place, the interaction and exchange of information will have to improve considerably. If an executive or manager is asked what specific elements of psychological knowledge are used in daily practice, the most likely reaction will be a puzzled look and an embarrassing silence. Vice versa: if a practitioner asks an academic to predict a particular future behavior because of an urgent problem, the academic is probably more than willing to comply, but will not hesitate to add that prediction is impossible without prior research for which, obviously, elaborate funds and sufficient time are needed. Both responses are disturbing because they reflect an enormous communication gap. Filling that gap requires a good model. In sum, this book hopes to contribute to the construction of a model that (1) is effective and efficient, (2) is simple (‘but not too simple’), (3) structures academic knowledge, (4) stimulates the development of new insights, (5) supports the design of effective and efficient policy measures and (6) fosters communication among academics of different disciplines and between academics and practitioners.

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1.3

The goal of the book

This book is about explaining, predicting and influencing behavior – the theme of psychology. There are literally thousands of books on psychology. This is yet another. What news value can it possibly have? At first sight, considerable overlap with other books seems unavoidable, but this is exactly what it intends to avoid. Existing books tend to be part of mainstream psychology, obedient to the ‘laws’, rules and conventions of the prevailing paradigm. By contrast, this book presents a rather rebellious point of view. It claims that mainstream psychology has passed the peak of its life cycle and is sliding down the slippery slope of decline. Psychology has produced a myriad of detailed behavior insights but this wealth is viewed as having a serious downside: the discipline has reached a point where the overview has been lost and the production of additional insights no longer contributes proportionally to our understanding of behavior. Rather than contributing to the development of psychological theory, additional detailed insights may even obstruct it. Stated in economic terms: the marginal utility of additional studies is rapidly declining. Figure 1 depicts the inverted U-shaped relationship that is assumed here between level of detail considered in psychological research and the level of behavior understanding. Increasing the amount of detail initially contributes to the understanding of behavior, but beyond a critical point understanding suffers from fine-grained insights. Knowledge and understanding do not necessarily develop in parallel. Paradoxically, knowledge may block understanding. Beyond the critical threshold that psychology seems to have passed, we know more but understand less. (Surprisingly, the same behavior researchers who pointed out the possibility of general information overload in Western societies did not question whether the phenomenon also applied to their own discipline).

Introduction

General understanding of behavior

Amount/level of detail in behavior insights

Figure 1

Presumed relationship between the development of specific psychological insights and the development of a general understanding of behavior.

The critical question is at what point on the horizontal axis (which happens to be a time dimension as well) psychology should be positioned. Moving further to the right on the horizontal axis/time implies a further increase in the level of detail. For theoretical and practical purposes there should be a balance between general and specific or detailed insights. This notion applies in any field. What is the benefit of knowledge regarding the structures of molecules if this knowledge cannot be used in the development of, for example, pharmaceutical products? In psychology, academic researchers put a disproportionate amount of attention and effort into generating highly specific knowledge. The over-emphasis on detail creates the illusion of academic progress. In the way psychology develops as a discipline, it strongly favors the search for deeper, more specific insights relative to the exploration of a badly needed more general understanding of behavior. This argument is not new: ‘Psychology has so many unrelated elements of knowledge with so much mutual discreditation, inconsistency, redundancy, and controversy that abstracting general meaning is a great problem. There is a crisis, moreover, because the disunification feeds on itself and, if left unchanged, will continue to grow.’ (Staats, 1991, quoted by Henriques, 2003). More than two decades ago, Staats (1991) was right: the crisis was and is left unchanged and it does continue to grow – relentlessly. Extended specialization holds the risks of fragmentation. In the literature the notion of frag-

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mentation is contrasted with the notion of unification or synthesis of existing psychological knowledge. Some authors are critical with regard to unification, however. For example, Clarke (2007) notes: ‘There is a multiplicity of unifying models and proto-models of the behavioral sciences available, none of which has won anything close to general acceptance. Plausibly, this is because none of these offers explanations that are clearly better than those offered by its rivals. Given this state of affairs, it would be extremely reckless for behavioral scientists as a whole to conduct work only within the framework of one such model.’ (p. 22). While Clarke (2007) is undoubtedly right in claiming that there is no generally accepted unifying model as yet and that behavioral scientists should be reluctant to give up their present research activities, there are four problems with his argumentation. The first is that it implicitly suggests that the choice of a type of model (unifying or specific) is dichotomous: a positive choice for one type implies a negative choice for the other. However in principle there seems to be ample room for both options. The second problem is that working with a unifying model can only be judged as reckless if the model is known. Without the identification of a particular model, Clarke’s statement is no more than a ‘reckless’ generalization. Problem number three is that Clarke seems to favor risk avoidance. However, psychology as an academic discipline needs innovation and there is no such thing as innovation without risk. Risk avoidance should not be a primary motive of scientists, behavior scientists included. In other words, the risk of sticking to traditional domains may be higher than the risk of venturing into a new area. The final problem is that Clarke refers to the past development of the discipline as a basis for its future development. The fact that we have not yet identified a generally acceptable model does not mean that attempts to develop one are bound to be fruitless. In fact, the argument could be reversed: the fact that a unifying model has not been found so far warrants extra effort to intensify the search. This book does take a risk in introducing a general model of behavior with the ambition to unify and restructure existing knowledge. It will present an approach that differs fundamentally from prevailing approaches in psychology. It intends to provide news value and to contribute to another type of understanding of the ‘nature and causes’ of human behavior. In order to

Introduction

avoid ‘reckless’ behavior, however, the alternative approach will be developed and presented with due care and with elaborate argumentation. It may be suggested that a general model that is applicable to all types of behaviors, persons and circumstances is impossible as it would be too complex to handle. But let’s invert the argument rather than capitulating prematurely. When we are overwhelmed with complexity, there is actually only one option, which is to simplify. So if we want to understand complex behaviors we need a simple model. Behavior complexity and model simplicity should be positively related. A simple model needs to be constructed on the basis of a limited number of universal and generic determinants of behavior. A general model simply cannot be built with the help of a broad range of specific insights. So far, unifying models have failed because they sought to unify what was already fragmented, apparently under the assumption that a fragmentation process can be reversed. In the case of psychology, it is an illusion to think that unity can be constructed from the myriads of knowledge fragments. By analogy, the pieces of a shattered Ming vase can never be glued together to reconstruct the authentic object, whatever the quality of the glue and whatever the expertise involved in the restoration. Even after a high quality restoration, the vase’s market value will only be a fraction (a ‘fragment’) of the original value. Both for Ming vases and behavior models it is more effective and efficient to ignore the pieces and restart from scratch, even if that would mean that a considerable portion of the historical investment would need to be depreciated as sunk costs. So here we adopt an alternative approach rather than trying to piece together the countless knowledge fragments that, when stacked up, constitute the discipline of psychology. We will attempt to construct an understanding of behavior using simplicity as a critical starting point. (‘One of the principal objects of theoretical research is to find the point of view from which the subject appears in the greatest simplicity’ (Gibbs, 1881). The goal of this book is to design a simple, yet generally applicable behavior model. More concretely, it will introduce a simple model that, in its crudest form, consists of generic behavior determinants only. The name ‘Triad model’ already hints at the number of determinants it includes. In later chapters, the model and its many propositions will be described and explained more elaborately. Here the description is limited to the basic ideas. The ideas presented in this section are not completely new. Even though they were developed independently from other authors, they do show over-

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lap with notions already published in diverse areas of the discipline. Later it will become apparent, however, that the Triad approach is different from existing insights. The differences refer to meta-theoretical, theoretical, methodological and practical notions. The crux of the model is that a particular behavior is explained by a set of three determinants: motivation, capacity and opportunity to engage in that behavior. For now, ‘quick and dirty’ definitions suffice. Motivation is the ambition or drive, capacity is the skill or competence and opportunity refers to the favourability of the circumstances in which the behavior takes place. More elaborate definitions follow later. These three variables or their equivalents were already suggested more than half a century ago by Ripple (1955). The mathematical product of the values of the three variables can be visualized as a cube or as a tetraeder (the geometrical shape with four angles, one of which is a 90° angle). See Figure 2 for a situation in which all three values are high.

Motivation

Capacity Opportunity

Figure 2

The Triad cube or tetraeder (random size).

The size of this cube/the volume of the tetraeder is assumed to be able to serve as a reflection and representation of behavior aspects such as behavior likelihood and behavior quality. Changing the size of the cube/tetraeder will impact the likelihood or the quality of the concerning behavior. Metaphorically, the combination of the three determinants is proposed as the ‘DNA’ of behavior: the foundation on which all general behavior principles can be built and from which more specific behavior insights may be derived. At first sight, the model may seem so simple as to be almost tautological. It

Introduction

appears almost too plausible and self-evident to add any explanatory or predictive value to existing behavior knowledge and insights. However, this first impression is misleading as later inferences and examples will show. The Triad model differs from existing psychological models in several respects. The various differences are summarized here and will be clarified more elaborately in the following chapters: 1. Models, in general, are supposed to simplify reality. The same applies to psychological models. Relative to other models the Triad model takes simplification to a higher level. At first sight it even suggests the risk of oversimplification. However, quite a lot of complexity is hidden behind the Triad model’s façade of simplicity and straightforwardness. It is simple but, paradoxically, it is its very simplicity that enables us to address complex questions that otherwise tend to remain unanswered. For example, simplicity allows us to consider the model’s inherent interrelationships (how do the determinants relate and combine to produce behavior?) and its dynamic characteristics (how do the three determinants and their relationships change over time?). A simple model may attempt to answer complex questions that complex models may not be able to ask. In this book complex questions about a simple model are preferred to simple questions about a complex model. The intention of the Triad model is to acquire a deeper understanding at a more general level of theorizing. This is fundamentally different from trying to achieve a deeper understanding at a highly specific theoretical level – the predominant approach in psychological research. 2. The Triad model presumes that the endless variety of different behaviors does not result from myriads of random combinations of ad hoc variables, but relates to the same underlying fundamental behavior principles. These principles cover all behaviors and intend to explain how people, in their particular circumstances, achieve their goals with the help of available resources. The goals may vary from survival to personal growth. So the Triad model assumes that fundamental behavior principles are the same whether the behavior concerns designing a strategy, fishing for salmon, playing the piano, purchasing a camera, building a power plant or boiling potatoes. 3. The Triad model is highly flexible. The model is like a spring that can be stretched or shortened at will: it can be used in its most simple and uncomplicated format and it can be extended to include specific psychological effects. In this respect it can better be viewed as a theoretical system rather than a single theory or model. Both in its simple and more

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complex forms (plural!) the model is intended to contribute to the understanding of behavior. The simplest version does not require a background in psychology and has shown to be highly attractive and functional to practitioners. More elaborate versions may be interesting to professional psychologists. Finally, the most extended model may turn out to be intriguing to academic behavior researchers. This way, the model hopes to be flexible enough to be simple, but not too simple for each type of user. 4. Academic standards and procedures require an empirical test for each individual theoretical statement. A statement is only taken seriously if it is empirically confirmed. Conversely, until a claim is established empirically it can be ignored as irrelevant. Because of the strong emphasis on empirical evidence, most theories are doomed to endless refinement and never make it to the point of application. They never see the light of day. Other theories, the ‘happy few’ that happen to do well under highly controlled conditions, may be fortunate enough to escape from the laboratory. However, once these theories hit reality, they have a high fatality rate. In real life, the theories explain behavior only marginally, the predicted effects manifest themselves differently ‘out there’, or carefully researched determinants are cruelly swept away by other factors that are simultaneously present. This stimulates further testing – in the laboratory! – as this is the safest place for a theory to survive. The inevitable consequence is that the variables considered in empirical work have become more trivial over time. Also, their position in the ‘grand scheme of things’ is lost. Differentiation and specialization soar and potential relationships among different strands of theory remain unaddressed. The emphasis on empirical testing frustrates rather than stimulates the construction of theory. If, by analogy, the same approach were to be adopted in the construction of a house, there would be a broad range of specialists on stones, woods, windows, floors, roofs and so on, but there would be no generalist to put these materials together. Even continuous and rigorous material quality tests and improvements would not help. (Worse: preoccupation with element quality might even lead to the illusion of construction quality). The future owner, although deeply impressed by the available expertise, would feel primarily wet and cold. Similar metaphors are easy to produce: the taste of a restaurant dish cannot be derived merely from the quality of the separate ingredients; high quality ingredients just may not combine in likeable food. The evaluations of the individual strokes of a Rembrandt painting do not add up to the painting’s overall beauty and economic value. In construction,

Introduction

cooking, art and empirical work the strength of the structure does not lie in the quality of the individual elements only, but also (or particularly) in their combination. Mainstream psychology focuses almost exclusively on individual theoretical elements. It is fortunate that academic psychologists do not build houses, prepare food or create art objects. For the reasons implied by these metaphors, the Triad model will pay considerable attention to the structure of theoretical principles. Rather than focusing on the empirical testing of each individual theoretical aspect, it places a strong emphasis on the way the principles support each other to form an overall framework. Both element quality and structure quality are considered important. Empirical testing is considered useless and potentially misleading if the position of an element in the total theoretical structure is unknown. An effect may be statistically significant and at the same time meaningless in a general framework. By comparison, in the construction business it makes no sense to test a material if the particular function it serves in the building is unknown. Some elements serve to reinforce the structure, other elements provide comfort and yet other elements contribute to its design. They derive their particular function from the overall concept of the building. It would be meaningless or even counterproductive to test the elements irrespective of this total concept. Based on this line of reasoning, the Triad model reverses the standard order of steps in the process of psychological research. Individual elements are not tested before they are allowed to contribute to theory; here the theory is built first. The testing of the elements and the overall structure should take place later. 5. The Triad model differs from other psychological theories in that its development took place along three avenues: (1) the construction of theory, (2) a comparison with the psychological literature (attempting to find both corroborating and falsifying evidence in the existing academic literature and (3) a comparison with real-life cases (attempting to find supporting and non-supporting examples). The risk of subjectivity was handled by having persons not involved in theory construction (both academics and practitioners) conduct initial checks and provide feedback. With regard to the practical cases, the model was tried out and successfully applied in a wide range of areas. These include general management, marketing, communication, market research methodology, strategy implementation, introduction of innovations, human resource management, traffic and employee safety, health care, environmental policy, social work, education, crime prevention and conflict mediation. Almost

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without exception, executives and managers welcomed the Triad model’s practical value. At the same time the theoretical underpinnings of the model were carefully compared with the academic literature and several publications and dissertations were produced that referred to the model (e.g. Nabih, 2003; Nederstigt, 2011; Vermeulen, 2011; Poiesz, 1999; Robben and Poiesz, 1994). Practical experiences and some empirical work will be referred to later in the book. 6. The model also differs from mainstream psychology in the sense that it is intended to provide a solution to a number of the problems relating to the discipline’s development. Its fragmentation is often mentioned as one of the critical issues that need to be addressed. The model does hope to provide an approach that may be complementary to existing approaches. Sternberg and Grigorenko (2001) presented a similar argument: ‘We propose that unified psychology can and should supplement traditional approaches to psychology.’ (p.1069). The Triad model does not question the relevance of the publications in the psychological literature, but does question the balance between more general and more specific theoretical approaches. 7. Because of the general nature of the Triad model it may serve as a bridge between psychology and the other behavioral sciences, between the various subdisciplines of psychology, and between academic knowledge and practical experiences. In this respect, it deviates from a large number of existing, highly esoteric psychological theories. So the Triad model hopes to do several things simultaneously. It simplifies psychological theory, it respects behavior insights that have been developed in practical settings, it questions the implicit or explicit claim by some academics that the understanding of behavior is the exclusive privilege of psychology, it takes into account that academic research may be flawed and that academic researchers, just like practitioners, may be subject to heuristics and biases. And it suggests that mainstream psychology relies too much on empirical work and not enough on both general theory construction and practical behavior evidence. Too often, academic research is conducted in isolation from day-to-day reality. The notion of the ivory tower refers not only to the location where researchers do their work. It also seems to refer to the location where most products of their work exclusively apply. In attempting to do justice to the diverse types of behavior knowledge, the present goal is fairly ambitious. Before introducing an alternative approach

Introduction

to understanding behavior (‘redesigning psychology’), the current approach of psychology will be analysed. In the following two chapters, we will make an inventory of its many contributions (Chapter 2), but also of the symptoms that suggest crucial problems (Chapter 3). The combination boils down to an alarming diagnosis on the status and future of the discipline. In the search for an effective treatment, several alternative solutions are discussed. A careful trade-off finally leads to the proposal of the Triad approach.

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development of Thepsychological knowledge

2.1

Chapter summary

In this chapter we attempt to present a picture of the nature of the field, its progress and productivity, and of its many contributions to the solution of a large variety of practical problems. Psychological insights have a positive impact on the wellbeing of many people. As an academic discipline, psychology has many branches, subdisciplines, sub-subdisciplines, and the like. These have generated important theories and concepts that are applied in many real-life situations.

2.2

Psychology and its contributions

We may be interested in understanding the behavior of an individual person or in the understanding of group behavior. The former may serve as a basis for the latter. Individual behavior may be aggregated to dyad, group and society level. On the other hand, if we know how dyads, groups and society ‘behave’, this knowledge cannot simply be translated to understanding behavior at the individual level. Macro observation does not necessarily imply micro understanding. An example: There are three political parties A, B and C. Their sizes are about equal. Party A loses approximately 10 per cent of its voters. Party B wins about the same percentage. Party C neither wins nor loses. A deductive approach would invite us to speculate about the nature of the change. In that case several interpretations are possible. One is that the voters who have left A have moved to B. Another interpretation is that one tenth of people who originally voted for A did not vote this time and that one tenth of the people who did not vote previously now voted for B. A third interpretation is that the two changes are unrelated. This shows that a deductive approach (making inferences from group level to the individual level) does not justify a particular selection from these potential explanations. Here an inductive approach might help out. A

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check at the individual level (‘Who did you vote for? Now and last time?’) could reveal the correct interpretation. Knowledge of individual behaviors may be cumulated to knowledge of group behavior. Group behavior can only be expressed in crude indicators like means and standard deviations. However, these do not provide a full reflection of the underlying individual behaviors. If behavior is considered at an aggregate level, much of the complex and interesting variety of individual behavior is lost. But the same holds for the wealth of information: aggregation can only be achieved at the cost of loss of information. So if we want to comprehend behavior to the fullest extent, we have to start at the most difficult level possible: the individual level, which happens to be the playground of psychology. By focusing on individual behavior, psychology adopts an inductive approach. Individual data may be aggregated to group statistics like means and variances. Most psychological studies examine group behavior with the help of individual data. While academics love variance because it allows them to conduct statistical analyses, variance is troublesome for policymakers. It blurs their picture of reality (which they find complex enough as it is). They therefore prefer to use straightforward indicators like means and to ignore distribution parameters that only seem to complicate things. This comfortably simplifies their world, but in doing so they risk serious misinterpretation. If a distribution is collapsed into a single generic indicator, segmentation of the target population and differentiation of policy measures can only be based on speculation. Therefore, for practitioners too, attempts to understand behavior should actually focus on the smallest unit of analysis: the individual. The extra effort of paying attention to differences between people is likely to pay off in more fine-tuned and effective policy measures. Of course, this does not mean that policies have to be focused on individual persons. For practical and/or financial reasons, this may not be possible. Rather, individual data may serve as a solid basis for the formation and designation of relevant target segments. Psychology attempts to understand what makes individual persons move and change. It tries to identify the relevant determinants of behavior. Psychology is an academic field of inquiry using scientific principles, methods and techniques. The positivistic approach is dominant, meaning that theory is tested with the help of observation-based evidence. For this a broad range of qualitative and quantitative types of research is available. Methods may

The development of psychological knowledge

vary from in-depth interviews and laboratory experiments to field experiments, surveys and brain research. Mathematical analytical techniques in varying levels of sophistication are used to identify data patterns, associations between variables and single or multiple cause-effect relationships. Reliability, validity and generalizability serve as criteria for data quality; parametric and non-parametric statistical parameters are utilized to judge the significance of a finding (e.g. a difference, a correlation or a pattern). The level of significance shows how likely it is that the observed finding is attributable to mere chance. It does not specify whether a finding is relevant in theoretical or practical terms, however. A statistically significant finding may be theoretically and/or practically meaningless. The opposite may also be true (a non-significant outcome may be informative and relevant). A problem that behavior researchers have to deal with is that behavior is prepared and organized at a location where direct observation is practically impossible, technically complex or unethical: in the brain. Even indirect observation, for example by questionnaires, may not be feasible, as many mental processes do not take place at the conscious level. If questioning is physically and mentally possible, it may not be possible for privacy or pragmatic reasons. For example, a researcher hypothesizes that a respondent will cast a left-wing vote at the elections. The respondent’s intention to do so might be indirectly observed through a pre- or post- election questionnaire but privacy and validity issues would complicate matters. The direct method – where the researcher physically follows the target person into the voting booth to see what vote is actually cast, would not only violate privacy rules but would also be very time consuming and expensive. This means that psychological studies often can only resort to establishing relationships between indirectly observed determinants and indirectly observed effects. Behavior researchers attempt to tackle the slippery nature of such analyses through ingeniously designed studies and with the help of highly sophisticated analytic techniques. Psychology developed into a highly productive academic discipline in little more than a century. Wundt (1832 – 1920) is considered its founding father. The contribution of psychology to society is based on the combination of theory and evidence or, more elaborately, upon the combination of metatheories, paradigms, theories, concepts, methods, data, techniques, research findings, interpretations and applications. This cycle produced an impressive body of knowledge and insights that continues to expand over time. One of its major contributions is that it shows how actual behavior may

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deviate from the behavior that might be expected on purely rational, intuitive or logical grounds. Psychology provides the surplus value relative to what is referred to as ‘common sense’. It has made us sensitive with respect to exceptional behavior phenomena, individual differences and whatever goes on in the human brain. In many areas of daily life, psychology contributes in a wide variety of ways. Psychological insights help or even cure mentally ill, troubled or confused individuals. Examples of contributions made by psychology can be found in healthcare, labour conditions and labour relations, economic domains (investment, financial decisions), markets, man-machine interactions, education, sports, management, traffic, economics, marketing, social work, the judicial system, housing, social relations, communication, conflict management, international relations, public policy and so on. In these areas, psychological knowledge and insights promote people’s wellbeing, harmonize the interaction and communication between people in various positions, functions and roles, improve the ‘fit’ between people and their respective environments and support the match between people and their instruments. Psychology helps to save lives, to cope with constraints and limitations, to function better and to improve the quality of life. This is not the place to give a complete, structured and balanced overview of the types of psychological insights that have contributed most. Instead, a more or less random sample will be provided of insights that tend to be appealing to both academics and practitioners. This sample cannot possibly give an impression of the full productivity of the discipline, but it can indicate the broad variety of relevant insights. The discipline may be very crudely divided into content oriented fields, application oriented fields and areas with expertise on psychological methods and analytical techniques. Content oriented fields focus on fundamental aspects of mental, social and emotional functioning like sensory psychology, physiological psychology, neuropsychology, social psychology, cultural psychology, developmental psychology and the psychology of individual differences (personality factors). Application oriented fields are, for example, clinical psychology and psychotherapy, economic psychology, consumer psychology, health psychology, child psychology, educational psychology, labour psychology, industrial and organizational psychology, forensic psychology, environmental psychology, cultural psychology, political psychology, sports psychology and traffic psychology.

The development of psychological knowledge

The areas of thinking and research that proved important in various phases of the development of psychology and which produced many classic publications can be classified or categorized in a variety of ways. There is no generally accepted classification. The informal classification presented below is used because it gives an impression of the vastness of the field. Again, the theories mentioned in each category are presented for illustration purposes and are not intended to suggest completeness or balance. It is not a review of the literature: each theory is sketched in the crudest possible way. Also, the categories should not be taken as strictly separated: some overlap is unavoidable – as are the gaps between them. (Readers who are familiar with handbook psychology may wish to skip the next sections and directly proceed to the conclusion of the chapter). Motivational theories • Psychoanalysis (Freud, 1923; 1915): How do conscious, preconscious and subconscious processes interact? Psychoanalysis focuses on the effects on behavior of deep, inner drives like sexuality. • Classical conditioning (Pavlov, 1927). How does learning take place (with special reference to the function of the autonomous nervous system)? Through repetition, an external stimulus is associated with another stimulus that causes a physical response, so that eventually the external stimulus elicits the physical response by itself (the famous example of the dog that is submitted to the bell – food – saliva sequence. Initially, the food causes saliva to flow; the bell does not. However, if the sound of the bell repeatedly precedes the food, the bell starts to trigger saliva production). • Operant conditioning or learning (Ferster and Skinner, 1957). How do people react to different schedules of rewards (reinforcements) and punishments? For example, a schedule that rewards a particular behavior irregularly has a more lasting effect on that behavior than a schedule that rewards the behavior every time it is shown. • Motivation research (Maslow 1943, 1954; McClelland, 1961). What motivates human behavior? Different motivational levels can be distinguished ranging from basic physical levels (survival) to cognitive/emotional levels (personal growth). How do people with different ambition levels react to tasks with different complexity levels?

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• Two-factor theory (Herzberg et al., 1959). The important contribution of this theory is that it shows how people’s evaluations are not positioned on one dimension (from negative to positive) but on two dimensions (from negative to neutral and from neutral to positive). Becoming less dissatisfied does not necessarily mean becoming more satisfied. • Atkinsons (1964) theory of achievement motivation: an individual’s tendency to achieve success, Ts, is greatest when the probability of success, Ps, is intermediate or 0.5 on a 0.0-1.0 scale. The expression is: Ts = Ps(1Ps). The Ts is lowest at both extremes of the Ps axis. Interestingly, the expectancy theory (Vroom, 1964) predicts that the incentive value of success will increase with its probability Ps. Although partially conflicting, both theories were supported by a reasonable amount of empirical evidence (Deci, 1975). • Rokeach (1973) presented an inventory of terminal or end values (desirable end states of existence) and instrumental values (the means of achieving the desirable end states). Examples of end values are happiness, inner harmony, social recognition, wisdom, freedom; examples of instrumental values are courage, politeness, honesty and independence. Cognitive theories/ theories concerning mental functioning • Prospect Theory and decision making (e.g. Kahneman and Tversky, 2000). How do people perceive gains and losses and how do these affect their judgments and decisions? People tend to be more sensitive to losses than to gains, size held constant. (This is a very productive area of research for which Kahneman received the Nobel prize in 2002). • Balance theory (Heider, 1958). People prefer to balance their affect with regard to different persons or objects. If person Z likes person A, and A does not like person B, then how does Z feel with regard to B? • Attribution theory (Rotter, 1966). The attribution theory addresses perceived cause-effect relationships. For example, behavior is caused by a number of determinants, but are these identified correctly? Sometimes a person points to another determinant than actually applies. For example, if a student fails an exam, he is more likely to blame the professor, the complexity of the material or the difficulty of the test’s questions than himself. Interestingly, it has been found that the type of attribution failures is gender-dependent. • Reactance theory (Brehm, 1981). If a person is told that she is not allowed to engage in a particular activity, the attractiveness of that activity increases relative to the attractiveness of the same activity before the

The development of psychological knowledge

message. Shop sales of a particular product can be stimulated by informing customers that there is a limit to the number of items of that product that may be sold. • Edwards (1954) tried to combine insights from psychology and economic theory to study decision making. His central question was: how do people make decisions on the basis of their perception, interpretation, evaluation and comparison of alternatives associated with different probabilities and values? He showed how psychology might contribute to economic theory. • Leon Festinger’s (1957) theory of cognitive dissonance: how do people deal with incompatible ideas? And how do they bring their thoughts and evaluations into line with the behavior that they (are forced to) show? Parenthetically, the term ‘cognitive dissonance reduction’ has become relatively popular among non-psychologists. It has many proud users – for the right and wrong reasons. Stimulus perception theories • Gustav Fechner (1836): Psychophysics. Fechner showed that for determining whether a stimulus will be noted, there is a fixed ratio (the Weber-Fechner law ) between a stimulus and its background. The same applies for a change in stimulus intensity. • Green and Swets (1966): Signal Detection Theory. This theory makes an interesting distinction among subjective identifications of stimuli. They identified correct hits (the stimulus is correctly perceived as being present), correct alarms (there is no stimulus and no stimulus is perceived), false hits (a person indicates that s/he has perceived a stimulus, but there was none) and false alarms (there was no stimulus, yet the person reports having perceived a stimulus). • Gestalt theory (see, e.g. King, 1994 referring to Wertheimer). How do people form complete mental images on the basis of just fragments of these images? People show a tendency to see wholes where only parts are provided. Affect theories • Attitude formation (Fishbein, 1980; 1967): How are attitudes formed and how do they influence behavior? Fishbein assumes that attitudes consist of two types of element: beliefs and affect. For each aspect of the attitude object, the relevant beliefs are associated with affect so that the overall attitude towards an object is the sum of the products of beliefs and affect

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of its individual aspects. For example, a person may view safety, mileage and durability to be the most relevant aspects of a car. It is possible to ask this person two questions: to what extent s/he believes that each of these characteristics applies to brand X and how positively or negatively s/he feels about this. According to the theory, the combination of these beliefs and evaluations, added up over the three aspects, represents an attitude, and attitudes may be used to predict behavior. The attitude theory is actually a fairly rational theory. • Theories of emotion. The best-known theory was presented by Plutchik (1991, 1980). On the basis of a biological, evolutionary approach he identified four basic emotions, each with positive and negative opposites: joy and sadness, trust and disgust, fear and anger, and surprise and anticipation. He designated the various levels of intensity for each of these emotions. For example, the more intense level of anger is rage; the more moderate level is annoyance. Many other types of classification have been proposed in the literature. The Schachter-Singer theory (Schachter and Singer, 1962) is another well-known classic theory suggesting that experiencing an emotion is the result of a bodily response in combination with an interpretation of that response. According to the Opponent-Process theory of emotion (Solomon and Corbit, 1974) in normal conditions any two opposing emotions, like fear and relief, balance each other out. If one of the two emotions is triggered and the opposing emotion is suppressed, the balance is lost. When the dominant emotion subsides, the other restores the balance so that a feeling of relief follows fear. Theories on individual behaviors in social relations • Equity theory (Adams, 1963). Addresses how inequity is perceived in social relations and transactions. Adams describes the nature, the causes and the consequences of inequity. • Social Comparison Theory (e.g. Festinger, 1954; Rijsman, 1974). For the evaluation of the quality of their lives or the quality of their performance, people often make a comparison with the lives/performances of other people. However, in doing so, they are fairly selective. The tendency to compare differs, for example, depending on whether the persons compared fare better or worse. Also, people who are viewed as distant on the criterion axis, are less likely candidates for comparison (the very poor do not compare themselves with the very rich, nor vice versa). One implication is that very rich people may be dissatisfied with their financial status if other persons in their social environment are even richer.

The development of psychological knowledge

• Social facilitation (Zajonc, 1965). The mere presence of others engaging in a particular behavior may stimulate that behavior. For example, the mere observation of other people eating stimulates eating behavior in the observer. Similarly, an athlete tends to perform better in front of an audience than when acting alone. • It is not clear whether Hofstede’s (1984) work on cultural dimensions should be categorized as psychological, sociological or anthropological in nature. So we include it here in order to be on the safe side and do justice to this important field. Hofstede indicated that the values held by members of a society could be placed on a limited set of dimensions: individualism–collectivism, uncertainty avoidance, power distance (social hierarchy) and masculinity–femininity. Additional dimensions have been suggested in recent years. The research by Hofstede has contributed greatly to the understanding of intercultural differences. • Learned helplessness (Seligman, 1975). In the course of their lifetime, people learn to be dependent or independent. Upbringing and educational practices may be important here. The concept of learned helplessness has shown that if people are supported for a long time, they are likely to become psychologically dependent, so that prolonged help may in fact be counterproductive. General theories • Self-efficacy (Bandura, 1986; 1977). With his theory on self-efficacy, Bandura attempted to explain how expectations of personal efficacy determine coping and success-oriented behavior – behavior that deals with the limitations, threats and opportunities in the environment. The theory served as a source of inspiration for psychotherapeutic treatment. • Arousal (Berlyne, 1960). People function best at a modest level of physiological arousal. When the level is low, there is tedium or boredom; when the level is high, there is stress. Arousal enhances the quality of performance on simple tasks and hampers performance on complex tasks (Yerkes and Dodson, 1908). • Theory of Reasoned Action (Ajzen and Fishbein, 1980) and the Theory of Planned Behavior (Ajzen, 1991; 1985). These are important theories as they predict behavior quite effectively with a relatively limited set of determinants. We will return to these later.

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Individual differences • A considerable amount of psychological research has been done on identifying the nature of structural inter-individual differences. People’s personalities differ, but in what sense? Literally hundreds of tests have been developed to identify the dimensions of personality. A few decades ago these were combined into the so-called Big Five (not to be confused with the five largest game animals living in South African wildlife parks). The five personality dimensions (e.g. John and Srivastava, 1999) are: extraversion-intraversion, agreeableness, conscientiousness, emotional stability and openness to new experiences. People have their own individual profile with regard to the positions on these dimensions. Three examples of classic personality variables that received considerable attention in the academic literature are Need for Ambition (to what extent is a person characterized by high ambition?), Need for Cognition (Cacioppo and Petty, 1982): ‘the tendency for an individual to engage in and enjoy thinking’ (p. 116) and Locus of Control (to what extent does a person attribute his/her personal situation to his own behavior or to the effect of external conditions – that is, systematically? (e.g. Rotter, 1966). The major theoretical developments took place in the last decades of the previous century. New, comprehensive developments are scarcely noted. The intriguing, relatively new field of neuropsychology provides an exception, although it is not yet clear whether it primarily concerns theory development or methodological development with the potential to stimulate theory development. It is generally known that particular areas of the brain are activated when specific physical behaviors are shown, but locality appears to be also important for the understanding of mental processes, psychological and emotional phenomena. New neuroscientific methods are being developed that may clarify the nature of neural systems and processes. Knowing what locations are active before, during or after a particular behavior or psychological effect may help us gain a deeper insight into what goes on inside the brain. Vice versa, behavior may be better understood if a link can be established with concrete brain structures known to have specific functions. This field is also promising for methodological reasons: it presents the opportunity to empirically relate invisible psychological effects and processes (such as perceptions or emotions) to objective data obtained through neuroimaging techniques. Potentially, such data are highly fruitful in a discipline that is dominated by subjective data. After all, brains do not lie.

The development of psychological knowledge

Theoretical developments in psychology have benefited greatly from continuous methodological and analytical advancement, which in turn has been enormously stimulated by advances in information technology. New analytical possibilities enable new types of structures and relationships to be found in data sets on behavior phenomena. Sometimes, however, they are used – or misused – as ‘fishing’ gear to search for patterns in a data set, whether these are theoretically meaningful or not. Thus new analytic techniques may be highly functional in supporting theory development, but may also be employed to disguise a lack of theory. The position taken here is that analytic techniques may support theory development but should not supersede it. Knowing cannot serve as a substitute for understanding. And description cannot replace explanation. It was already indicated above that this inventory can never be more than incomplete. This is not a handbook of psychology. For that reason we have to disregard important theories of eminent psychologists. And we have no room to discuss areas like environmental psychology, developmental and child psychology, ergonomics and research methods and techniques. We ought not to end the inventory here, as that would fail to give due respect to many authors and publications. Nevertheless we do so. While fully aware that we are brutally ignoring many areas of research, we return now to more general observations on the field. Let’s start by noting that psychology is not a stand-alone discipline. It provides functional support to other disciplines like economics, organizational science, marketing, education, political science, law and communication. In some cases, psychology even blends with these fields, thus creating new areas of interest. Examples are economic psychology (with the emphasis on psychology) and behavioral economics (emphasizing economics). Psychology shows how people identify their needs, process information, make trade-offs, arrive at explicit or implicit decisions and implement these. Psychology presents evidence on heuristics and biases that may seriously affect the quality of decision making processes (see, for example, Tversky and Kahneman, 1973). It provides diagnostic tests that reveal people’s skills and competencies. It identifies people’s deficiencies which, when corrected or coached, improves their performance. And it provides recommendations on the optimal mutual correspondence between a person’s psychological profile and the characteristics of various environments. On the practical side, then, psychology significantly increases the quality of many life domains for many people. It contributes to the prevention of individual, family, group

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and societal problems. It supports people when coping with serious existential issues. Through psychotherapy it helps people find a new balance when suffering from psychological problems. And it increases the conditions for general wellbeing. Interesting new application areas relate to contemporary societal issues such as, for example, leadership, aggression, conflict and mediation, eating disorders, social relations, employment, financial coping, the internet, addiction, happiness, bullying, stress and trust. Interventions concerning these issues are developed and executed, with the help of psychologists. Psychology consists of many specialties – particular topics of interest that, during a particular period, are given close attention in the academic media. Together they create a vital discipline. The various areas sprout, mature, flourish and eventually wither away. In fact, many topics in psychology that were ‘hot’ in earlier decades are no longer significant. The same applies to whole fields of interest (e.g. research on the involvement construct, a motivational variable specifically addressed in social and consumer psychology in the 1980s. See Engel and Blackwell, 1982; Krugman; 1967). In any event, psychology is a highly dynamic discipline. The modest inventory of psychological theories and concepts presented earlier in this chapter gives only a narrow and superficial impression of what is available in the literature. There are simply too many titles to refer to. Collectively, psychologists know a great deal. But there is no coherent ‘body of knowledge’. Fortunately, recent technological facilities in the form of virtual libraries have dramatically increased the accessibility of publications. But, as always, there is a price to pay: confrontation with the large number of publications often intimidates the potential user. For example, concepts like ‘satisfaction’, ‘perception’, ‘attitudes’, ‘decision’ and ‘communication’ have been addressed in about 22,000, 26,000, 30,000, 67,000 and almost 100,000 publications, respectively. At least these were the numbers when this manuscript was written. There will be hundreds or even thousands more by the time it is read, alongside the numbers relating to synonyms or equivalent concepts. Fortunately the same information technology that provides these counts allows us to narrow down our searches. But even a combination of ‘satisfaction’ and ‘decision’ alone, for example, produces more than 300 publications. That quantity looks at first sight like a promising, rich source of information blending together all knowledge on the relationship between the two concepts. However, closer inspection reveals that almost all publications on this particular combination relate to very different, incomparable

The development of psychological knowledge

issues. To mention a few: jobs, careers, partners, health facilities, libraries, production in Sri Lanka, computer use, residential choice in Estonia, an earthquake, forest management in British Columbia, fishery, genetic counselling, transportation, tourists in Mexico, travel agents in Turkey and so on. The variety in the list (of 300 publications) seems unlimited. Common elements are difficult or even impossible to locate. These examples may seem unlikely and far-fetched, but they have been selected at random and are fairly representative of the academic literature. The latter observation alludes to the paradox already referred to in Chapter 1. On the one hand, the volume of knowledge is enormous; on the other hand, the huge quantity of publications may constrain or even prevent the construction of an effective knowledge system. We know more – that is, we can find more – but does it really help? To what extent does psychology generate surplus value relative to informal behavior theories, the combination of which is known as ‘common sense’ or ‘naive psychology’ (e.g. Poulin-Dubois, 2009)? Or does the infinite accumulation of highly specific findings create an imbalance between knowledge and general understanding – in favour of the former? Markman (2007) notes: ‘When any science defines its theoretical constructs narrowly with respect to particular phenomena, it may miss key generalizations across situations. Chemistry would be limited indeed if it had separate theories for each element [of the periodic table – note by the present author].’ (p. 34). So here we have an issue. Fragmentation of psychology is an undesirable phenomenon and its unification may be impossible. The vast majority of academics tend to ignore the problem by defining the fields of study very narrowly and neglecting what is beyond their boundaries. As long as the discipline itself prefers to ignore this problem rather than to confront it head-on, it is likely to persist (see Staats, 1991). But problems do not vanish by sweeping them under the carpet. The longer they exist, the more they expand and the more vulnerable the discipline becomes. Although fragmentation is suggested to be one of the major problems facing psychology, there are also other problems and issues. The first step now is to identify these and to assess their nature and their possible effects. Only then may we search for a corresponding solution. After all, without an

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adequate diagnosis, an adequate solution or treatment is impossible. The next chapter gives an inventory of the problems and issues that are perceived as threatening or damaging to the discipline. That will be followed by a discussion of their implications. Ultimately we hope to find a direction in which a solution may be found and to identify the criteria it should meet.

Limitations of psychology 3.1

Chapter summary

The previous section showed that psychology is a highly productive and valuable discipline. However, critical questions were raised with regard to the nature of its development. The reasons for concern will be elaborated in this chapter. They do not call the overall positive contribution of the discipline into question. Rather, they call for a more balanced and realistic picture of its current position: they invite us to rethink psychology.

3.2

Problems, limitations, risks and pitfalls

Below, we describe nine different, but more or less related types of issue that give reason for concern regarding the development of the discipline of psychology. These are: the societal reputation or image, the fact that people study people (that is, their own kind), the academic publication pressure, peer reviews, the (lack of) prominence of psychological variables, the ‘ceteris paribus’ clause, the emphasis on evidence based insights, research process biases and, finally, fragmentation. 1. The societal reputation or image There is an awkward contrast between the academic status of psychology and its societal reputation. In spite of its impressive scientific and practical contribution, psychology has an image problem. Relative to other academic disciplines, psychology is often confronted with scepticism or even cynicism in society. Correctly or incorrectly, psychology is blamed for explaining behavior using highly esoteric terms for mundane, common sense notions rather than providing surprising new insights that uniquely contribute to the solution of societal, organizational and individual problems. To lay persons it often seems that psychologists provide quasi-intellectual, noncom-

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mittal explanations for behaviors that they already (presume to) understand. They just may be right. In addition, Staats (1999) blames psychologists for using different labels for what, essentially, might reflect the same underlying concept like, for example, ‘the self’, ‘the self-concept’, ‘the self-image’, ‘selfperception’, ‘self-efficacy’ and ‘self-esteem’. Another critical argument is that psychologists explain behavior problems after the fact, but avoid making predictions, that is, at a time when precautionary measures can still be relevant. For example, psychologists are more than happy to explain in the popular media why a particular outburst of violence did take place in a school, but do not issue warnings that such incidents will take place in the near future, for the reasons best understood and explained by them. Is the reputation issue merely the result of modesty, lack of clarity and bad communication? Or is it a problem inherent in the discipline itself? 2. People study people (their own kind) Actually, the study of behavior involves two behaviors. The first behavior is that of the person(s) as the object(s) of analysis. The second behavior is the behavior of the researcher. Psychology needs behavior in order to study behavior. This calls for a psychology of psychology. The double loop renders behavior interpretations vulnerable to unintended and unobserved effects. To what extent can researchers truly filter out subjectivity when studying the behaviors of others? This fundamental question addresses the validity of psychological research. From time to time academic researchers demonstrate that they are aware of the risk, but they do not seem to be overly concerned about the risk of research biases. The issue presented here resembles the Heisenberg principle in quantum physics, which indicates that the position and speed of an object cannot be measured simultaneously. By comparison, in psychology, it is impossible for a researcher to measure his/her own behavior and that of the target person at the same time. In any one study, it is only possible to measure one of the two behaviors. Although the comparison with a notion in quantum physics is rather far-fetched, it may pinpoint a basic, but generally ignored flaw in psychological methodology. (There are ways to circumvent it, for example through double blind studies. In such studies, no party directly involved in the study is aware of its objective so that the researcher cannot influence the subject or contaminate the study in any undesired way. However, double blind studies are far from popular – they involve a lot of hassle – and studies that specifically focus on the researcher’s behavior are extremely rare. As if academic researchers do not behave as other – ‘ordinary?’ – people do).

Limitations of psychology

3. Academic publication pressure In academia, the motto ‘publish or perish’ is widely accepted. When the national and international competition for research funds intensified, the rule gradually changed to ‘publish a lot or perish’. The resulting pressure on academics inevitably affected and still affects the sensitive balance between the quantity and quality of published studies. Here quality is defined as the combination of effectiveness and efficiency. Effective research contributes considerably to the understanding of behavior. ‘Considerably’ refers to size of impact, relevance and uniqueness or distinctiveness. Efficiency of research reflects the number of studies that have to be performed in order for a particular effect to be sufficiently demonstrated (Daniel Kahneman in a 2012 blog: ‘We are discouraged from (…) stating how many failures it took to get that one success’). It also refers to the number of studies that needs to take place in order to refine an effect after it has already been demonstrated. Did the pressure to publish cause the discipline to reach the point where quantity outweighs quality? The strong pressure to publish, which to a large extent may be held responsible for the high production level, is likely to produce collateral damage as well. The following possibilities can be mentioned: • Authors submit manuscripts for publication thereby suggesting that these present substantial and original contributions. However, their actual contributions may differ only marginally from the ones already published. Apparently, success breeds success – in both intended and unintended ways. • The pressure to publish calls for a very efficient type of research behavior. One way to be efficient is to follow the leader: the author who happens to be the focus of academic attention. While on the one hand there may be a good reason for this attention (a particular area may be truly promising), on the other hand it draws all kinds of publication hunters who produce not only meaningful but also trivial publications. • The pressure to produce publications in the short run competes with long term research orientations. Usually the former end on top. • Sometimes, a research theme is squeezed like a ripe orange so that, after a disproportionate amount of effort, a final, marginal drop of evidence is harvested. Again, data obtained in a single study may be split in such a way that a publication multiplier effect is generated: the squeezed orange is squeezed again. Even though data sets are not supposed to be used more than once for publication, the data gathering process is time con-

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suming and in the publication pressure cooker researchers can hardly be blamed for trying to be ‘creative’. If it comes down to creating the potential for publication, reality may be sliced in many different ways. 4. Peer reviews In large scale systems quality is generally safeguarded by (peer) review procedures. This is true for the banking system and it is true for the sciences including behavioral science and psychology. A system may become so complex, however, that the review procedures are only capable of overseeing and addressing the more specific areas and cannot judge the system as a whole. In the banking system this ultimately led to the financial crisis of 2008. What if the review procedures used in psychology support the idea of quality in the various specific research areas but, at system level, only provide the illusion of quality? Could it be that our focus on the parts has led to neglect of the whole? Review procedures are an integral part of the discipline. Actually, the discipline is controlling itself. Apart from the fact that, in general, this increases the likelihood of perverse effects, it also raises questions with regard to its independence. Note that a peer review system is a social system as well (see also Kuhn, 1970). Researchers function in an academic, professional and social context consisting of other researchers with similar ambitions, needs (for status, recognition and income), interests, competencies, research funds and facilities. Inter-individual relationships between researchers/colleagues enter into the equation and codetermine research quality evaluation. In doing so they inevitably affect the position and development of psychology. The discipline, then, seems to be the result of two different, closely related lines of activity – scientific and social. Ideally, the social line is independent from the scientific one. In reality, however, the two lines are closely interrelated,. It is even possible that the social line is to some extent selfsupporting, thereby affecting or even opposing the scientific aspect. An example would be an inner circle of academic colleagues who disproportionately refer to each other’s work for opportunistic reasons and in doing so create a ‘publication carrousel’. To the extent that this is the case, science is downgraded to the social game of competing for publication points. This may not be directly visible at the surface of the wide and incessant stream of publications, but the undercurrents may be quite strong. The double focus would ultimately call into question the legitimacy of the academic

Limitations of psychology

process, the integrity of academics, the relevance of research outcomes and the status of psychological knowledge. In a social context all kinds of desirable and undesirable inter-personal phenomena may take place, including cooperation and competition. If colleagues and/or rivals judge each other’s work, the following possibilities have to be considered: • Researchers attempt to copy other researchers with excellent publication records. On the one hand, this raises research quality; on the other hand it dampens critical perspectives and true innovation. • Reviewers and reviewees function virtually side by side in closely knit social networks, seemingly separated by independently functioning editors. While they are prepared to be critical within the review system, at the same time they are keen on protecting it. After all, here they know the rules of the game that is beneficial to them. Academic researchers who think beyond the accepted paradigm are prevented from damaging the system. They are unlikely to pass the review procedure. They are blocked by colleagues. Academic deviance is a self-solving problem. • In a system where researchers are forced to use research time as efficiently as possible in order to achieve the required number and quality of publications within a particular period, the wisest strategy – careerwise – is to obey mainstream conventions. • Researchers cooperate to mutually boost publication and citation records (a citation record is another production parameter). They exchange coauthorships (‘if I may co-author your publication, you may co-author mine’) and trade citations (‘if you refer to my publications, I will refer to yours’), even if the actual contribution is negligible or marginal. This, obviously, does not help the validity of the quality system. Nor does it stimulate scientific progress. The relative lack of independence in academic quality control is not solved by the internationalization of the research community. Already for many years, social networks in science have been crossing national boundaries. In international networks the same risks can be noted as have been described above. True independent evaluation is only possible by parties who are not part of the system, like sponsors (governments, non-academic parties) for example. However, the problem is that these parties often lack the expertise to judge the quality of research. They consequently adopt the very criteria that the system itself is willing to provide, not realizing that this turns them into volunteer hostages, directly supporting flaws inherent in the system.

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The picture presented here is possibly grimmer than reality warrants, but it seems justified to be critical: independence in academic quality control appears limited at best. The combination of the disproportionate pressure to publish and the restrictions of the peer review system render the discipline vulnerable and may cause it to lose integrity (which is viewed here as an all-or-nothing characteristic). Note, parenthetically, that fraud is not suggested here. Academics tend to be smart people and smart people play the game according to the freedom that its rules and conventions provide. Yet, it is well known that fraud has taken place and is possibly still taking place in psychology research. Because of the lack of transparancy and the complexity of the system it is impossible to detect the extent to which it actually does. This in itself is a reason for concern. It calls for a code of conduct (including sanctions in cases of violation) to be signed by every academic researcher. A possible adverse effect of the publication pressure is that the system goes ‘into reverse’. The normative order of steps in the research process is: (1) Select a research theme on the basis of its potential contribution to the understanding of behavior; (2) Select the most suitable research method; (3) Collect data that best describe the phenomenon of interest; (4) Apply an appropriate analytic technique to these data and (5) Submit the results for publication. The reverse order that seems to emerge is: (1) Identify an area with a high publication potential; (2) Choose an analytic technique that is most likely to impress reviewers and editors; (3) Select data that fit that analytic technique; (4) Select a method that may produce the required type of data and (5) Formulate a matching research question or hypothesis and produce the argument that underscores the relevance of the study. For a long time, academic career criteria ran parallel with scientific criteria. Now the two types of criteria appear to diverge, leading to a differential emphasis. The difference can be expected to be all the greater when publication points translate into financial gain. 5. The ‘prominence’ of psychological variables Overall, effects obtained in controlled research settings – the laboratory – often prove to be more significant, prominent and stable than when the same effects are generated in actual, more complex situations. Here, in the cocktail of ingredients constituting real life, the effect of a single variable is easily obliterated by other influences or swamped by the impact of more dominant psychological variables. Here an important difference should be noted between psychology and natural sciences like physics and chemistry.

Limitations of psychology

In studies of the latter disciplines, conditions can be controlled more easily because the object of analysis is a ‘thing’ or a substance, not a person. What is more: the conditions that are built on a small scale in the physics or chemistry lab may be rebuilt on a larger scale. A lab may be expanded to a larger size – a factory – while all conditions are kept the same. The same trick does not work for psychology labs, however, as we would easily run into practical and, above all, ethical problems. However, this means that one might question the relevance of studying very specific isolated variables that, when released from the ivory tower to function on their own, are most likely to evaporate or dissolve on the way down, or be devoured by other, more dominant variables even before touching ground. 6. The ‘ceteris paribus’ clause Psychological studies center on the particular variable or variables that the researcher is interested in for the explanation of some behavior. While the researcher may have a good reason to highlight the selected variables, it is unlikely that these are the only variables that affect the relevant behavior. If the other variables are known, their influence may be downsized or eliminated (for example by control conditions or by analytical techniques that filter out theoretically irrelevant co-variation). Another option is to assume that unknown variables either have no impact on the studied behavior, have a random effect, or cancel each other out. For obvious reasons it is impossible to assess the nature and size of the impact of unknown variables. This raises a problem as these may have an autonomous effect or interact with the critical variables. If so, they increase, decrease or change the nature of the influence of the latter; unknown variables may cause misinterpretations of the true impact of the critical variables. Psychology has an elegant solution for this general problem: the ceteris paribus clause. It refers to the assumption that apart from the particular variable(s) focused on in research, all other variables are held constant. An example: a researcher hypothesizes that a text message is more persuasive if a picture is included than without it. One group of students receives the message plus picture while the other group – the control group – only receives the text. The text provided to both groups is the same. The results show that, indeed, the picture makes the difference in the expected direction. For a correct interpretation of the results, however, it is important that the researcher can feel confident that no other variables affected the outcomes. For example, the message of the first group might have been printed on heavier paper than the message of the second group. Or the first group judged the message before an exam

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and the other group after the exam. Or: the picture makes the message of the first group look longer so that message length may be the critical factor, not the picture itself. If the researcher is keen on unwanted differences between the groups, these may be eradicated. But even then there may be unknown influences. Here the ceteris paribus clause comes in handy. This rule or principle (meaning: ‘keeping everything else constant or the same’) suggests that unwanted effects may simply be assumed away so that the researcher may exclusively attribute the finding to the studied variable(s). Thus, for researchers, the ceteris paribus rule is a blessing. It provides a comfortable alibi to restrict reality, select research conditions that are favorable to the hypothesis to be tested and steer the interpretation of variance in the desired direction. The clause is a perfect example of collectively legitimized ostrich behavior. On the one hand, the clause is a blessing as it increases the efficiency of behavior research. On the other, it is a curse as it basically means that in all publications on studies where the clause was used, a simple statement would have to be added: ‘It all depends’. After all, there is no need to fully specify the conditions under which the observed finding was obtained. The clause seems to stimulate comparison of studies, but in fact, hampers comparisons because it takes potentially relevant background information out of the equation. Over the course of time the clause has become so widely accepted in psychology and its status has become so self-evident, that in the vast majority of publications it is no longer even mentioned. It conveniently escapes attention. Here we may note that studies have become more fine-grained so that, in consequence, more variables need to be held constant. So while the clause has actually become more important over time, it is also more widely ignored over time. This should be a reason for concern for the discipline, but is not. It may even be argued that the clause demotivates the search for a general behavior theory. If researchers do not worry about the effects of disregarded determinants at a specific level, then why should they do so at a more general level? The counter argument that might be raised is that a researcher who is expert in a particular field is well aware of what determinants are held constant and would not allow the results to be biased by the exclusion of potentially relevant determinants. That may be true, but there is also a counter-counter argument: such determinants can only be known if a general model or framework is available. And that is not the case. So the problem with the ceteris paribus clause is not that it holds variables constant,

Limitations of psychology

but that it does not specify which variables it holds constant. This leads to only one conclusion: if we do not know what we do not measure, we do not know what we measure. Or, more generally, if we do not know what we do not know, we do not know what we know. 7. The emphasis on evidence based insights Evidence is a philosophical issue. In large part the advancement of science takes place by the continuous cycle of theory building and the acquisition of evidence that either verifies or falsifies. The cycle may be extended by making a distinction between evidence gathered under controlled conditions and evidence obtained in real life settings. Practical experience, often ignored by academics, completes the circle. It stands for evidence that is not intentionally gathered but builds up over time through observation. It also provides feedback on research-based recommendations. While one might expect academics to be interested in practical information relating to their theoretical notions, this type of information tends to be considered inferior to information in the academic literature. For example, references to professional publications in the academic literature are very rare. For the full cycle, see Figure 3. Theory construction

Acquisition of evidence under controlled conditions

Cumulation of experience in real life

Acquisition of evidence in real life situations

Figure 3

The cycle of the academic process (smaller ellipse) and knowledge generation in general (larger ellipse). The ellipses run clockwise.

Theories that are not based on facts are the equivalent of subjective beliefs and may culminate in fantasy-drenched areas like astrology, quackery and

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witchcraft. Science is distinguished from intuitive thinking by the corrective function of carefully and systematically obtained evidence. Because of the use of evidence, scientific theories are subject to falsification. The interplay of theory and evidence provides the basis of science. Evidence is essential and self-evident. However, some critical statements can be made. In psychological research two types of evidence are dominant: the central value (like a mean) and the variance around the mean. In principle these two measures are equally important. Both present an incomplete picture without the other. However, the central value tends to play the star role in the discussion of research findings even although it typically only describes a minority of the observations. The number of persons that are exactly described by the mean tends to be relatively small. ‘Usually, the variation around the average (or prototype) is discounted’ (Omi, 2011, p. 119). The central value is thus more likely to be discussed than the variance. The same applies to central value and variance differences. Variance is needed to assess the representativeness of the central value and to help determine the statistical significance of differences between means. The over-emphasis on central values suggests that researchers favour evidence that is easily interpretable, even if it presents only a prototype or caricature of the broad range of evidence observed. So academics seem to behave according to the information processing and decision making heuristics (Tversky and Kahneman, 1973) as well. Evidence is an instrument in the development of knowledge. It is a means to an end, not an end in itself. In principle, theory and evidence sing in close harmony. Neither should lag too far behind the other. If theory advances too quickly, intuition is taking over. If evidence outruns theory, the researcher is data-happy: s/he collects new evidence without really knowing why. The appeal for some balance between theory and evidence does not specify how his balance should be achieved. For example, should each small theoretical step be confronted with evidence before the next step is taken, or is it preferable to postpone the acquisition of evidence until a theory is more fully developed? Let’s address this question with the help of a metaphor. When Frederick Cook set out for the North Pole in 1908, he had to find his way with the help of a compass – his source of evidence. In deciding how to use the evidence, he had to make a trade-off between effectiveness (or precision) and efficiency. A high level of precision would keep him exactly on track but would also require him to check, after every few steps, whether he was still on the predestined course. This

Limitations of psychology

approach would be very effective (it would lead him to his destination), but not very efficient: it would slow him down dramatically. A disproportionate focus on efficiency might even block effectiveness. To put it differently: he might perish, but at least it would be in the right direction. A more efficient or time- and life-saving approach would require him to consult his compass only once in a while and cover larger distances between checks. So Cook had to decide by which approach the pole should be reached: an orderly linear route based on almost continuous measurements or a more zigzag approach with occasional measurements. In principle, both approaches were acceptable. But the low temperature, the bad weather and the limited food supply probably affected the trade-off in favour of efficiency. In psychology a similar trade-off can be made regarding the use of scientific evidence. A highly precise but painstaking approach is aimed at providing evidence for every minor theoretical step, in the hope that the combined steps eventually lead to the researcher’s goal. This process is very time and money consuming. The other approach, the zigzag route, covers larger theoretical distances before the result is confronted with evidence. Psychology, probably through its desire to imitate the natural sciences, has adopted the former, positivistic approach in which effectiveness is favoured over efficiency. Evidence is gathered for every theoretical detail rather than for more general theoretical constructions. Again, this guarantees precision but slows down progress. If Cook had adopted the same approach, he would still be on his way to the pole (which, by the way, some believed he never reached after all). There is yet another difference between Cook and academic researchers: Cook had an overall theoretical framework in which he knew where he was going and why. Academic researchers lack such a framework. Their scope is confined to a particular area and a particular period in time in which every step is counted as worthwhile – whatever the direction. As a result, the discipline is scattered all over the place, spread awfully thin. Psychology looks like Cook without a compass. In the discussion on the development of the discipline, evidence plays a critical yet double role. On the one hand, the availability of evidence supports theoretical and conceptual arguments put forward by many authors. Systematic attempts to refine academic rigour produces reliability and validity. On the other hand, it would be surprising if no confirming evidence were to be found in studies that are carefully designed to produce that evi-

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dence. So the question is not whether evidence is found, and whether this evidence is technically reliable and valid, but whether the evidence makes sense in an overall theoretical system. It seems fair to argue that the reliability and validity of available psychological evidence is limited to aspect theories. The same evidence that stands for progress in psychology produces the illusion of understanding. Extending this argument: as long as there is no unified theory, evidence relating to aspect theories risks being ambiguous or even deceptive, whatever the quality of the study that produced it and whatever its statistical significance. In conclusion, evidence is a key instrument in knowledge development, but if it is overemphasized in the absence of a general theory (as is the case in psychology) it has a paralyzing, detrimental effect on the discipline. 8. Research process biases Apart from a statistical bias in the form of spurious significance, there may be other possible evidence interpretation biases (see also Ferguson and Brannick, 2012): 1. Selection bias. In the ideal case, a researcher selects a research theme because of the anticipated contribution to behavior understanding. However, publication pressure may persuade him/her to select a topic more opportunistically. This renders the popularity of a research theme a selfsupporting phenomenon. Researchers tend to approach or stay close to an area that is already popular, thus boosting its popularity. The same reasoning applies, in reverse, for unpopular themes. The trick of ambitious researchers is to be only slightly different from others within the prevalent research focus. 2. Research design bias. As it is risky to deviate from the dominant research paradigm, it is strategically smarter to adopt the existing research methodology than to branch off in an innovative direction with little or no history. It is most efficient to take small steps at a time: small enough to avoid risks, large enough to suggest at least some progress. Although science should be one of the innovative forces in society, most behavior researchers are risk averse, making one tiny, cautious step at a time. Often, researchers are interested in finding a particular result. It is unlikely that they are indifferent with regard to the results after carefully designing their studies. After all, a study with a supported hypothesis has a better chance of getting published. So it is reasonable to assume that research conditions are selected or created that provide a more than fair

Limitations of psychology

3.

4.

5.

6.

chance that the expected/desired outcome will be obtained. This bias reflects a combination of creativity, pragmatism and opportunism that is not necessarily in conflict with academic integrity. After all, the data may not support the hypothesis. But it does not necessarily match scientific standards either. The evidence is acquired with a purpose, may be biased, and it is not obtained in a random slice of reality. Submission bias. If the evidence does not support the researcher’s hypothesis or if the results are inconclusive, the researcher may not submit a manuscript for publication. Instead, researchers may prefer to adapt the study or repeat it until a significant effect is obtained. Or the data are subjected to variations of analytic techniques until they fit the researcher’s preconceived notions. Production bias. In order to ‘stay in business’ (to meet academic production criteria), researchers may deploy publication strategies and tactics. An example of a strategy is to write articles in academic journals, not academic books. The latter tend to score lower on publication criteria (which is questionable in itself, but that is not the point here). Another strategy – or ‘sales technique’ – is to refer to authors who are frequently cited, thus suggesting that the submitted manuscript emanates from (or at least closely relates to) the inner circle of productive researchers and should not be discarded easily in the review process. Although reviewing is blind, highly productive researchers may intentionally or intentionally plant various types of hints in the manuscript to reveal their identity, thereby possibly affecting the outcome of the review process. Reviewer bias. If a submitted manuscript is to be evaluated, a journal editor is likely to invite reviewers who know how to judge the submitted material and who represent the paradigm. Reviewers and reviewees share the same area of expertise. This is positive as it guarantees review quality. It is negative as it stimulates academic inbreeding. When reviewing is not totally blind (when the origin of submissions may be deduced with relative certainty), the parties hold each other hostage in incestuous relationships that may hamper academic fertility. Editor bias. Due to the sharp contrast between the high number of submissions and the limited amount of publication space, the competition for publications is fierce. Journal editors must be highly selective. The basis of selectivity may depend on different quality criteria, but also on different interests. Is an editor prepared to publish highly controversial material, or will s/he stick to middle-of-the-road research? And does s/he favor mediocre research with highly significant counter-intuitive findings or high quality research confirming a null-hypothesis? (In this

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context it is interesting – and reassuring – to turn to Pautasso (2009), who found no evidence for the ‘file drawer problem’ in psychology – as opposed to other disciplines. This problem refers to the tendency of journals to preferentially publish studies with a statistically significant result). This list of publication biases is definitely not meant to call the quality of published material in general into question. After all they are only possibilities, and may even reflect biases of the present author. But the possibilities need to be mentioned as the publication pressure builds up to the level where researchers may be tempted to engage in perverse behaviors. 9. Fragmentation There is no unanimously accepted basis for behavior explanation, prediction and influence. The many different knowledge streams, rivers, creeks, currents and rivulets that together form the discipline of psychology have no common origin. A possible reason is that human behavior is simply so volatile, dynamic and varied that more general behavior principles simply cannot be formulated. But this should not deter us from realizing that the absence of a central foundation implies serious risks and limitations that call for a solution. One limitation is that attempts to acquire new insights primarily boil down to research activities that are delving for deeper available insights. Publications conclude with the more or less standard request for levels of additional studies in the same line of interest. This cliché inevitably leads to more fine-grained research that, over time, addresses an increasingly higher level of detail. The process is often referred to as ‘fragmentation’ (Zittoun et al., 2009; Yurevich, 2009) or ‘differentiation’ (Valsiner, 2005). In the following discussion the former term will be used. For a visualization of fragmentation see Figure 4. In the figure, more general concepts, relationships and conditions are dissected into more specific concepts, relationships and conditions, et cetera. One of the major problems with fragmentation is that there is no end to it.

Limitations of psychology

Individual concepts

time

Et cetera

Figure 4

Visualization of the fragmentation of psychology.

More than half a century ago, Cronbach (1957) already saw fragmentation as a core problem. A decade later, Bertalanffy (1968) warned against the problem that researchers of different disciplines get caught up in their own small universes and, as a result, are unable to develop a more general theoretical system. Over the years, many authors have addressed the phenomenon (e.g. Yanchar and Slife, 1997) but in fact it appears to be highly persistent despite its association with a number of unfavorable side effects. These can be grouped into several categories: metatheoretical, theoretical, methodological and analytical, and lastly application issues. 10.1. Fragmentation: meta-theoretical issues • Fragmentation entails the risk of trivial research and trivial outcomes. At higher levels of fragmentation, statistical significance is more likely to be confused with meaningfulness. • Fragmentation causes the formation of relatively isolated knowledge ‘silos’ – mutually independent schools of thought that create their own idiosyncratic standards. These schools do not establish cross-references to other schools or fields of research. While schools in themselves are

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•

•

•

•

important instruments for knowledge creation (Latour and Woolgar, 1986), they hinder the development of overarching knowledge. True scientific progress is dependent not only on the production within the ‘silos’ but also – or even more so – on the quantity and quality of interconnections between different knowledge domains. Fragmentation distorts the balance between specialization and generalization to the benefit of the former. The balance between depth and width can be illustrated with the help of a simple metaphor. A child is trying to dig a deep hole in the sand of the beach. She notices that when she only focuses on depth, the sides of the hole cave in. If she spends much energy on making a wide hole, not enough energy is left to make it deeper. So the trick is to find the optimal relation between depth and breadth, where depth is not counteracted by a lack of breadth. Psychologists are primarily focused on digging deeper. Most of their holes eventually cave in, which forces them to start digging all over – a little stretch further. The result is a quite peculiar landscape. Fragmentation or trivialization may redirect attention from the content (theory and concept development) to the sophistication of the method and the analysis. Reviewers who assess the quality of an article submitted for publication may confuse theoretical relevance with technical complexity. Fragmentation may result in a stronger emphasis on how the study is done, rather than on what was done, why, and with what effect. Increasing myopia. As fields of expertise shrink to highly specific and esoteric areas, communication between these fields becomes problematic and the distance between the fields increases. By comparison, the inflation of a dotted balloon results in larger inter-dot distances. In this ‘balloon’ the tendency to stick to one’s own expertise (‘dot’) increases and the willingness to build theoretical bridges decreases. This causes psychology to disintegrate into a huge diversity of micro areas of expertise. Parenthetically, over-inflated balloons have a grim perspective. Increasing lack of transparency. The multitude of publications, even on a particular topic, outnumbers the quantity of studies that can be overseen at the same time, notwithstanding the help provided by search facilities in virtual libraries. When a particular research theme becomes popular, it attracts productive researchers with a good nose for fertile ground. The number of studies and publications accelerates. After a period of high research intensity the theme is no longer capable of managing its success and crumbles under its own weight (see also Kuhn, 1970, for the rise and fall of paradigms). Before the marginal contribution of additional studies decreases, how-

Limitations of psychology

•

•

•

•

ever, the most ambitious researchers will abandon ship and will start the process all over with another promising theme. Here, history repeats itself. In a sense, researchers look like satellites that are flung from planet to planet without ever really touching ground. Most research themes never reach closure and dissolve into oblivion. ‘It is simply a sad fact that in soft psychology theories rise and decline, come and go, more as a function of baffled boredom than anything else.’ (Meehl, 1992/1978, p. 524). The phenomenon of fragmentation violates one of the discipline’s own most important rules: the rule of parsimony or Occam’s (or Ockham’s) razor (e.g. Sober, 1981). It states that simpler explanations are to be favoured over more complex explanations if both are equally effective. In fact, the rule stimulates a search for the simplest explanation and, in doing so, demands scientific efficiency. Fragmented academic research is anything but efficient, however. Fragmentation increases the risk of random search for evidence, not guided by theory. When a particular significant effect is observed, it is considered interesting in its own right. Theory may be developed opportunistically, after evidence has been obtained. This may be referred to as reverse interpretation. It is rather cynically described by Adkins (1984): ‘Basic research is like shooting an arrow into the air and, where it lands, painting a target’. (p. 212). It is confrontational even to suggest the possibility, but the lack of transparency may invite some academic researchers to engage in copying behavior, plagiarism and even fraud. In the recent past there have been painful incidents that were exposed not only in the academic media but also in the public press. Different fields of psychology have drifted so far apart that publication material that is ‘borrowed’ from other areas is likely to remain unnoticed. The same holds for creative data management. The intense craving for publications may invite researchers to breach the boundaries of neighboring disciplines in order to poach ‘new inspiration’. In principle, finding new sources of stimulation and inspiration is highly recommendable, but if the search in other fields is only meant to find an easy route to publication, it is a perverse activity. Finally, fragmentation raises the fundamental question of what model of man emerges from the process and what the answer means for the general understanding of behavior. Is behavior the result of some mysterious, complex and instantaneous computational cognitive/affective process in which relevant fragments somehow combine to produce motor and/or mental activity? Even though the brain is an extremely sophisticated organ, this notion is difficult or impossible to grasp. The thousands of

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(statistically significant!) fragments presented in the literature render it extremely difficult to imagine the working of the human mind, let alone the understanding of behavior. Our own personal, daily experience with impulse, routine and deliberate behavior does not seem to correspond to a notion of behavior that involves myriads of simultaneously operating determinants. A highly complex model is not very plausible. The same holds for the opposite notion in which a specific behavior is viewed as the specific effect of a specific determinant for a specific person in a specific situation at a specific point in time. Psychology would end up being an endless catalogue of possible combinations. It is unclear what function the two types of model might have other than keeping academics happy. Let’s conclude this section with a small metaphor. A young boy has a deep interest in watches and hopes to own one some day. He is overjoyed when his grandfather gives him a watch for his birthday. The boy, academically inclined, wants to know how the watch operates and immediately takes it apart (unaware of the consequences). As soon as all parts are spread out in front of him, he suddenly realizes that he does not know how to reassemble it. He proceeds by carefully studying the individual fragments one by one, hoping that these will provide a partial clue about the correct time. 10.2. Fragmentation: theoretical issues • On the cover of his recent book, Henriques (2011) summarizes the theoretical problem poignantly: ‘Following the demise of the so-called grand theories (…) (see the brief overview of theories presented earlier – note by the present author), the field of psychology largely gave up its early aspirations to paint a broad picture of the human condition, and now the discipline focuses primarily on empirical problems that have a relatively narrow scope. The consequence has been a proliferation of interesting findings with no real capacity to answer big questions or to generate a shared general understanding of the human condition’. • In the absence of a general behavior theory, fragmentation may cause behavior determinants to be overlooked, over- or under-emphasized or misrepresented. • Fragmentation results in a stronger research focus on behavior determinants, or determinants of behavior determinants, rather than on behavior itself. The question of how behavior is explained is replaced by the question how particular behavior determinants ‘behave’. Psychology claims to

Limitations of psychology

be the discipline of behavior, but in its execution its very object of study – behavior itself (what people actually do) – is gradually disappearing. See Figure 5. Figure 5 shows two effects. One is a shift of emphasis from the explanation of a particular behavior by a set of determinants to the explanation of the impact of one or two variables on that behavior (the shift from the grey arrows to the black arrow). The other effect is that there is a shift from explaining the impact of a behavior determinant to explaining a determinant of a determinant of behavior (the shift from the black arrow to the white arrow). In psychological research, behavior has become a fuzzy topic in a thick fog.

Direction of research

Determinants of determinants

Figure 5

Determinants

Behavior

A comparison between a focus on the explanation (etc.) of behavior and focus on the effect of a determinant.

• The risk of partial overlap between studies. Note that partial overlap is a larger problem than complete overlap (which would amount to replication) or no overlap at all. The metaphor of the jigsaw puzzle applies here. Assume that we have a jigsaw puzzle with pieces that may be linked with one other piece only, sometimes with two pieces, but never with three or four. Obviously, such a puzzle will never present the whole picture. To extend the metaphor: if we were to ask behavior researchers to complete the puzzle, they would ignore the linkages problem and would simply make more and smaller pieces, particularly the type of pieces they are familiar with. We would end up with a huge pile of miniature pieces and no picture whatsoever. With an increasing number of pieces

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•

•

•

•

the likelihood increases that some pieces fit. But finding a needle in a haystack is simpler. So the outcomes of partially overlapping behavior studies cannot be matched to complete the picture of behavior. In psychology, they simply don’t add up. The development of myriads of research lines is associated with development of incompatible nomenclature, conceptual tools and methodologies (Leontyev, 1977). The same concept may be labelled differently, depending upon its background. This causes confusion, both within the discipline and beyond its boundaries. The desire of individual researchers to make their own unique contribution results in an explosion of concepts. After all, the introduction of a new concept suggests impact (newness and relevance). This causes the risk that the same concept is available under a number of different labels, which seriously hampers the search of publications and their comparison (see also Staats, 1999). Another risk of conceptual overlap is that it produces spurious correlations: two concepts appear to be correlated, but the correlation is based on the overlap of linguistic meaning only. Fragmentation produces (near) synonyms and, as synonyms happen to be highly correlated, invites circular reasoning. At the same time, fragmentation increases the risk of seemingly inconsistent or even contradictory results. The higher the level of fragmentation and the smaller the fragment, the higher the number of conditions that have to be held constant in order to assess the influence of that fragment. Studies that focus on the same fragment are likely to differ with regard to background conditions. At higher levels of fragmentation, specific background differences tend to remain unnoticed (or ignored). After all, that is what the ceteris paribus clause is for. Fragmentation is accompanied by the introduction of highly specific behavior determinants. Determinant specificity tends to be inversely related to explained variance. Researchers may solve this in several ways. One is to increase the specificity of the dependent behavior variable as well. Then, a trivial independent variable may explain a massive amount of variance of an equally trivial dependent variable. The obvious drawback of this solution is a combination of reduced relevance and a higher risk of tautological explanations. Another solution is to increase the number of specific determinants, expecting that this will also increase explained variance. However, a larger set size increases complexity. With an increasing number of theoretical and conceptual fragments, the number of potentially relevant interactions also increases. Interactions contribute significantly to complexity. Note that the number of possible

Limitations of psychology

first order interactions approximates half of the square of the number of elements (1/2n(n-1)). For example, a relatively small set of six variables implies 15 first order interactions. (Let’s not even try to think about higher order interactions. At higher levels of fragmentation interactions are more likely to be ignored for pragmatic reasons, even if their identification is critical for the understanding of behavior. 10.3. Fragmentation: methodological and analytical issues • A highly specific study has a very narrow focus. It limits the subject’s or respondent’s behavior and it pinpoints a particular behavior determinant. In this methodological straightjacket, the person’s behavior has very little room to deviate from the researcher’s hypothesis. Thus, fragmentation may artificially increase the likelihood of a statistically significant relationship between the independent variable and the dependent behavior aspect. • Fragmentation implies the risk of unintended repetition: a study from a different line of research is more or less repeated. The concepts, method or analysis differ only slightly. The wheel is reinvented albeit under a different name. Because of the large number of wheels, the likelihood of detection is negligible. Repetition may even occur between different behavior (sub)disciplines. • The corrective function of replications (which may be manifested in phenomena like regression toward the mean; cancelling out spuriously significant outcomes) is used to a limited degree only. The most probable reason is that replications are not interesting to ambitious authors and judged not worthy of publication by news-oriented editors. On top of this we may note that if studies deal with relationships between very specific determinants and very specific effects under very specific circumstances, a pure replication is extremely unlikely as no researcher is interested in making one. • Without the availability of an overall theory to behavior, it is very difficult or impossible to accurately interpret observed research findings, however sound the methodology of a study may seem. The same results could have been obtained under different conditions and the same conditions might have produced different outcomes. • Behavior is a dynamic phenomenon. It takes place over time in a continuous fashion and is not a series of distinct, separate acts. Psychological research tends to focus on behavior that takes place momentarily: start of study – determinant – effect – end of study. There is no apparent

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interest in the dynamic characteristics of either the determinants or the behavior effects. This implies a fundamental difference between the nature of the research object and the type of research. It looks like making a heart rate analysis on the basis of a single heartbeat, or like a soccer coach who analyses the performance of his team by making a snapshot rather than a movie. Behavior research is replete with snapshot studies. • In psychology, the significance of a result is a critical criterion for a difference, effect or pattern to be statistically meaningful. A significant finding means that there is only a very low probability that the particular result could have been found by mere chance. The conventional cut-off points are 5% or 1%. So if an outcome has a probability of only 5% or 1% of having been found ‘by accident’, the finding is considered statistically significant. We may also note the flip-side of the same principle: a cut-off point of 5% means that five random findings in 100 findings are considered ‘significant’, even though these should be attributed to chance only. This means that we have two problems: one is that it is impossible to tell which results are truly significant and which are based on pure chance. Psychology works with probabilities, not with certainties. The second problem is that the likelihood of finding a spuriously significant result increases with the number of studies. If we were to look at the results of 1000 studies, for example, a cut-off point of 5% implies that there are 50 studies with findings that appear to be significant, but are in fact meaningless. (Thus, out of 100,000 publications on communication, about 5000 might be misleading). Fragmentation boosts the number of studies and that in turn amplifies the risk of spurious significance. As only significant results tend to be published, part of our ‘knowledge’ of behavior is based on quicksand. This, again, presents a twofold problem: (1) we do not know what part, and (2) we do not know how large that part is. (One might argue, on the contrary, that the high number of studies mitigates this problem: if the result of a study is only spuriously significant, it is likely to be non-significant in other studies on the same topic. However, as we indicated earlier, pure replications are extremely rare. Also, we should not forget that many studies are performed that do produce significant results but do not get published). In conclusion, at the level of individual studies, the notion of significance seems reasonable. However, for the correct interpretation of significance all research activities should be taken into account, even those that do not attain significance and those that remain unpublished. For the sake of argument, let’s assume that for every 1000 publications on a particular topic with a 5% cut-off point for significance, 2000 studies are required. This gives the

Limitations of psychology

cut-off point a different meaning compared with the situation where all studies are published. Unfortunately, the literature does not report the number of studies that did not attain significance or were turned down for publication. • The amount of behavior variance that is explained by fragmented variables may be expected to be small. In fact that appears to be the case in many studies. In general, psychological variables account for a small portion of behavior variance only. When psychological variables are combined with contextual variables to predict behavior, it is not uncommon for the latter to completely wash out the former. (To complete the picture, it should be noted that the reverse might also be observed. The subjective evaluation of one’s physical health, for example, is a stronger predictor of perceived happiness than the result of an objective health assessment (Diener and Seligman, 2004; Angner et al., 2012). So subjective variables may be expected to be a stronger determinant of health behavior). 10.4. Fragmentation: application issues • With increasing fragmentation, the generalizability and external validity of findings becomes more problematic. The specific conditions under which a particular finding was observed may be very difficult to find or replicate in reality. • In most cases, the list of contents of an academic journal is puzzling. The titles of a single issue often announce articles on completely different, sometimes even exotic topics. Special issues of academic journals, intended to focus on a particular domain of application, tend to list titles that only marginally relate to the common theme and to each other. Often, editors or guest editors have to go out of their way to suggest some cohesion between the various contributions. • With fragmentation there is an increasing likelihood of finding, somewhere, a result that supports one’s particular preconceived notion – whatever that notion is. Fragmentation supports opportunism. This is beneficial for the opportunist, but not for the solution of practical problems, nor for the credibility of the field. • Fragmentation widens the gap between academic research and practical application. For a practitioner with a particular problem it is almost impossible to select a strategy to construct a possible solution on the basis of the plethora of potentially relevant publications. Due to fragmentation, cross-references between academics and practitioners are excep-

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tional: most practitioners do not refer to academic insights and most academics do not refer to practical experiences. Popular science publications present an exception. Their popularity shows that the need for psychological insight is larger than academic research can provide – in spite of the millions of publications. • In academia, there is a close relationship between research and education. Fragmentation in research implies fragmentation in education. This means that highly intelligent students are educated to become experts in analysing ultra-thin slices of pseudo reality. In addition, we educate them in a culture largely defined by the limitations discussed above. After completing their studies, the vast majority of students leave the university to crash into real life.

3.3

Limitations of psychology: summing up

In spite of the fact that psychology produced and produces many relevant insights and proves to be a helpful discipline in many areas of society, there are reasons for concern about its progress as an academic discipline. The development of major theories and concepts is tapering off, to be taken over by the production of an overkill of miniature insights that float around in a theoretical vacuum. In a time where ‘connectivity’ is the new buzzword in society, psychology is becoming increasingly disconnected. We discussed nine limitations of the discipline. Most of these are just that: limitations. None of them presents a critical threat to the future development of the discipline. That is, individually. But their combination and interaction certainly does. The cocktail of fragmentation, publication pressure, disproportionate evidence focus, ceteris paribus and publication biases may prove to be a lethal one. If we assume, prudently, that each of the nine limitations reduces the effectiveness of the discipline by a mere 20 per cent, the interaction (multiplication) effect of the combined limitations would be disastrous (.809). The problem is that they create a self-propelling negative spiral that lacks an innate corrective mechanism. Fragmentation breeds fragmentation. This mechanism is depicted in Figure 6.

Limitations of psychology

No central theory

Research system

Social system

Biases

Focus on evidence

Focus on publication

Ceteris paribus

Expertise specificity

Fragmentation

No generalization

No integration

Trivialization

Lack of general behavior understanding

Limited practical application

Questionable societal reputation

Figure 6

Relationships between various characteristics of psychological research.

The figure shows how the absence of a central theory leaves room for the development of a research system and associated social system. The social system consists of cooperation, competition and peer reviewing. Together, these explain the disproportionate and premature emphasis on evidence, the strong pressure to publish and the possibility of publication biases. These, in turn, lead to expertise that is highly specific and esoteric. This, combined with the ceteris paribus clause causes fragmentation. Fragmenta-

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tion is inversely related to generalization, integration and general behavior understanding. Paradoxically, fragmentation stimulates the pursuit of even more specific insights. Statistical significance provides the justification (that is: alibi) to continue on the conventional track. Over time, the construction of a central theory becomes more and more unlikely and even disappears from the discipline’s agenda. Psychology turns away from the explanation of behavior and thereby cancels out the very reason for its existence. Academics allow themselves to be spun around in an accelerating loop of theory, evidence and publications, in the meantime falsely assuming that system flaws are either negligible or self-correcting. For those who have to pay their mortgages it is wiser to go along with the system than to question or confront it. Earlier we mentioned the life cycle idea of psychology: the notion that after the stages of growth and maturity, the discipline may enter a stage of decline. It seems that this stage has been reached, but has just not been noted. The very productivity that suggests growth may be illusory and, in fact, may signify the contrary. Productivity and fragmentation form one paradox; another concerns the difference between the discipline’s productivity and its problematic societal image. These paradoxes will at some point in time combine to raise the question of whether society (that is, governments and funding institutions) will be prepared to continue financing research which risks being perceived more as an intellectual game among academics than as a sincere attempt to contribute to the understanding of behavior and the solution of behavior problems. Fragmentation acts like a nuclear reaction: once set off, it is virtually impossible to stop. Early in its development fragmentation already reaches the point of no return: differences and distances between the various streams of research become so large that attempts to reunite them are bound to remain futile. A final paradox: while fragmentation consumes the discipline from inside out, in the short run individual academic researchers benefit from its expansion for the simple reason that it increases the opportunity to publish. That may explain why most of them do not see – or prefer not to see – that the prevailing approach in psychology has entered a deadend road.

Limitations of psychology

3.4

Towards unification

Although they form a striking minority, a number of authors warn against the risks of fragmentation and make a plea for integration or unification (e.g. Baars, 1985; Bower, 1993; Drob, 2003, 1987; Gintis, 2007; Giorgi, 1985; Kimble, 1989, 1994; Koch, 1981, 1993; Milton, 2010; Rand and Ilardi, 2005; Rychlak, 1993; Staats, 1981, 1986, 1991, 1999; Sternberg and Grigorenko, 2001; Yanchar and Slife, 1997). Yet other authors are critical with regard to unification or conclude that synthesis is not possible (Clarke, 2007; Dixon, 1983; Koch, 1993; Kukla, 1992; McNally, 1992; Watanabe, 2010). Sternberg and Grigorenko (2001) claim that methodological pluralism is a main cause for the lack of unification. Similarly, Smith (2007) states that reunification is not possible because of significant epistemological divides. Hammond (2007) views as a distinct binding element in the behavioral sciences the ‘fundamental ever-present (…) methodological flaw that causes the mishap’ (p. 29). He argues that some level of unification exists, but for the wrong reason: ‘All previous attempts to build a unifying framework in psychology have failed because the scientific base keeps collapsing’ (p. 29). Clarke (2007): ‘There is a multiplicity of unifying models and proto-models of the behavioral sciences available, none of which has won anything close to general acceptance’ (p. 22). Goldfried (1980) concludes that it is unlikely that we can ever hope to reach common ground. Clarke (2007) even categorically argues against unification: ‘If (…) reality is disunified, then explanatory unification in the behavioral sciences can only be had at the cost of descriptive inaccuracy’ (…); ‘it may prevent new perspectives from being developed from which criticism of the presuppositions of the accepted model might be made’ (p. 22). Note that Clarke’s first statement is a conditional one: ‘If (…) reality is disunified (…)’, unification would lead to descriptive inaccuracy. The word ‘if’ is critical here, but Clarke seems to presuppose that reality is, in fact, disunified. Later, we will try to present arguments to the contrary. Also, his suggestion that unification may prevent new perspectives from being developed does not appear to sprout from a careful analysis. In fact it does not seem difficult to reverse the argument: unification may produce insights that so far have remained obscured by fragmentation. Although a generally accepted ‘grand theory’ is not available, several attempts at unification have been made. For example, Gintis (2007) presented his BPC model (Beliefs, Preferences and Constraints), which he also refers to as ‘the rational actor model’. In this model he combines the evolu-

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tionary principle of biological fitness (which explains preferences) with the economic notion of expected utility under conditions of informational and material constraints. According to him, the behavioral sciences are linked up with the natural sciences through statistical and mathematical techniques, computer modelling and a common scientific method. Also ‘(…) structuralist social theories developed several decades ago (e.g. Althusser, Poulantzas) can be understood as attempts to unify the behavioral sciences’ (Resch, 1992, in Clarke, 2007, p. 22). Tooby and Cosmides state in 1992 (see Price et al., 2007) that ‘the unification of the behavioral sciences (…) is taking place within a neo-Darwinian framework which views all organisms as bundles of adaptations’ (p. 39). Some authors attribute unifying qualities to evolutionary psychology (see, for example, Tooby and Cosmedes, 1992; Buller, 2005). Sternberg and Grigorenko (2001) suggest methodological and organizational measures to stimulate unification. More specific unifying theories have been proposed for distinct areas within psychology like, for example, motivation (Forbes, 2011), language acquisition (MacWhinney, 2007), attention in associative learning (Kruschke, 2001), love relations (Gordon, 2006), self-efficacy (Bandura, 1977) and cognition (Newell, 1994). It appears that different authors interpret the notion of unification differently. Some present a framework rather than an explanatory model, some limit themselves to a particular domain (e.g. the social domain or the medical domain) and some focus on particular behaviors, functions or concepts. While each one of these attempts is aimed at some form of integration, they all fall short of presenting a unifying, general and universally accepted approach to the behavioral sciences. Interdisciplinary contacts and communication could be functional for the integration of different types of understanding, but in reality the notion of interdisciplinarity seems to be more talked about than practiced. Jones (2007) remarks: ‘It has become common, fortunately, for scholars to call for greater interdisciplinarity, believing important syntheses will follow. Suggestions on how to synthesize are much rarer’ (p. 30). Henriques (2004) strongly favours unification of psychology by referring to an inspiring analogy: ‘(…) the difference between fragmentation and unification is the difference between noise and music.’ (p. 1208). In later years he introduced an elaborate and ambitious attempt towards unification (Henriques, 2011). His vision on knowledge incorporates levels of complexity, classes of (behaviors of) objects and classes of science. The levels or dimensions of complexity are matter, life, mind and culture; the classes of objects

Limitations of psychology

are material objects, organisms, animals, and humans. Finally, the classes of science are physical, biological, psychological and social. He shows how the various classes are interrelated. Henriques’ (2011) approach combines the Behavioral Investment Theory and the Justification Hypothesis. The Behavioral Investment Theory consists of six principles that together account for the variation in human behavior: (1) the principle of energy economics, (2) the evolutionary (Darwinian) principle, (3) the principle of genetics – genetic differences result in behavioral investment differences, (4) the principle of computational control – the assumption of the nervous system as an information processing system, (5) the learning principle and (6) the developmental principle. Henriques (2011) refers to the Justification Hypothesis as ‘an exercise in reverse engineering’ (p. 115). According to him, it allows us to define ‘the key design features of the human self-consciousness system’ which ‘(…) is the languagebased portion of one’s mind that is narrating what is happening, why it is happening, and why one is doing what in that context.’ (p. 115). Although Henriques’ attempt to construct a new unified theory of psychology is admirable, the theory seems better suited to summarizing existing behavior research findings than to predicting future behavior. In that sense, it may better function as an organizing framework than as a theory. How it can contribute to the advancement of psychological knowledge remains unclear. And as it replaces one complex system by another, it is unlikely to reduce the distance between academia and practice. Anyhow, Henriques’ (2011) approach to unification has an ambition that differs from that of the approach advocated in this book: simplification. Summing up, unification attempts are relatively rare. In the confrontation of forces, unification is negligible relative to fragmentation. Most researchers are not opposed to the idea of unification, but in their actual work they (implicitly) favour the continuation of the dominant paradigm. As their object – behavior – is endlessly complex, dynamic and variable, researchers’ room to manoeuvre has no boundaries, allowing for sheer unlimited publication possibilities. The ensuing output suggests knowledge accumulation but in fact causes the discipline to explode into a myriad of micro specializations.

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In theory, several potential solutions seem feasible, most of which would require some change of academic culture in which editors of academic journals would have to play an decisive role: • The stimulation of publications that address theory integration or de-fragmentation with the help of already available data, multi-level analyses and meta-analyses. Zittoun et al. (2009) refer to the creation of new theoretical ‘nodes’ with an integrative function. See the vertical arrow in Figure 7a.

Individual concepts

Figure 7a

Visualization of de-fragmentation; Vertical integration or unification.

• The discouragement of new data collection if it only adds to fragmentation. Vice versa, the encouragement of data collection in studies specifically aimed at integration. • The stimulation of publications that focus on connections or ‘horizontal’ relationships between different subfields. The rule of parsimony can serve as an instrument to collapse concepts that are similar in nature but have been labelled differently by their respective authors. See the horizontal arrows in Figure 7b (which is a variation of Figure 7a).

Limitations of psychology

Individual concept.

Figure 7b

Visualization of the de-fragmentation of psychology; Lateral integration of concepts.

• A stronger emphasis on applied research to test the validity, relevance and generalizability of existing theory in daily life. Invite qualified practitioners to serve on review boards to stimulate research valorization. • Journals may explicitly denote the nature of each article. This is not the place to propose a particular categorization, but possible categories are, for example: specialization, vertical integration, horizontal integration, theoretical innovation, replication and validation. These categories might be associated with different incentive systems. Parenthetically, it does not seem advisable to establish journals specifically aimed at integration as it would suggest that integration is a distinct area of expertise. Rather, integration should be an issue that is addressed by the whole discipline. For several reasons, however, the implementation of these measures would be complex: 1. Review and incentive systems would have to be adapted in order to render these measures effective. The questioning and changing of established systems tends to be accompanied by fierce opposition, high transformation costs and considerable loss of time which, combined, would imply a serious loss of short term efficiency. While a change may be justified from a long term perspective, short term consequences render a critical assessment of the existing system unlikely. This applies even more to actual change.

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2. A multifaceted problem requires a multifaceted solution. The measures might only work as an integral package, not in isolation. This would involve considerable planning and coordination costs. 3. Measures like these would require a high level of acceptance and commitment among researchers, employers and sponsors in the broad field of the discipline. The problem is that these parties happen to constitute the very system that would be the object of change. And the system serves their interests well. So they are unlikely to discard prevailing, comfortable routines without incentive, sanction or coercion. They may agree that there are some flaws in the system, but are happy to accept these as long as the system works for them: it provides inspiring white collar work, status, recognition and a decent family income. 4. The fourth reason why a system change is unlikely is suggested by Clarke (2007): ‘(…) the unification of the behavioral sciences would involve the abandonment of much work that does not fit easily into the unifying framework adopted’ (p. 22). Radin (1996) refers to this effect as ‘the loss of local knowledge’. This can be countered by the argument that the validity of the assertion cannot be assessed without a careful comparison with unification attempts. (To which we may add that the notion of sunk costs should never be the guide to the future direction of an academic discipline). 5. There is no strategic leadership at the international system level. Leadership in academia is based on academic status and academic status is dependent on success within the system. This closed loop prevents potential leaders from questioning the very system to which they owe their position. In the absence of independent leadership researchers follow their own academic interests. In principle, fundamental research should determine its own direction, but if the discipline’s position is at risk through some underlying development, external governance is warranted. So measures aimed at limiting or reversing fragmentation would face serious implementation issues. But there is another even more fundamental problem of unification: most ‘solutions’ take the results of fragmentation – the fragments themselves – as the starting point for unification. However, this approach is valid only if it can be assumed that the fragments adequately represent the whole and that the whole can be constructed or reconstructed on the basis of all fragments combined. This assumption seems difficult to adhere to. Are all of the current fragments just waiting to be combined into a grand theory? If the Ming vase of Chapter 1 is shat-

Limitations of psychology

tered, we may reunite the thousands of fragments in an attempt to reconstruct the original. But this would be an example of defragmentation – after fragmentation of an existing whole. In this sense, it does not resemble the unification of psychological insights. In psychology there never has been a ‘whole’ theory. In principle, if there is no whole, there is no fragmentation and there are no fragments. So there can also be no defragmentation. We just have a huge collection of more or less isolated, partly incomparable insights. As we do not know the frame in which these insights might or should fit, we cannot judge their relevance, we cannot determine whether they complement or overlap one another, we may overlook some insights, there may be insights that hide the whole from view and we may have conflicting insights. The whole might be more or less than the sum of its parts or may be different altogether. The combined insights could produce a whole that is more complex than necessary. For example, the notion of sunk costs might compel us to include as many insights as possible, including erroneous and superfluous ones – the fallout. So unification may not be a good idea at all if we take the available research findings as a starting point. We would not know what to combine, how and in what general direction. So unification and a unified theory happen to be two different things. Unification may not even lead to a unified theory. For that reason, we abandon the notion of defragmentation or unification of existing material and adopt a completely different approach to arrive at a unified theory: we simply start from scratch – even if it implies ‘the loss of local knowledge’ (Radin, 1996). In the following chapter, we will ignore existing psychological insights and proceed as if all behavior knowledge needs to be constructed anew. The goal is to build a basic behavior model. If that proves somehow feasible and justifiable, an attempt will be made to build upon it by adding more specific insights. In doing so, we will adopt a reductionist perspective: the attempt is to reduce the complex, variable and dynamic phenomenon of behavior to its most simple and fundamental mechanism. The intention can be visualized with the help of adapted versions of Figures 7a and 7b presented earlier. See Figures 8a and 8b. Figure 8a shows the defragmentation approach, using existing insights to construct a unifying theory. Figure 8b visualizes the negation of existing insights, starting theory building all over.

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Individual concepts

Figure 8a

Defragmentation: a reductionist approach.

The arrow shown in Figure 8a depicts defragmentation to the point where there are only a limited number of generic behavior determinants. Existing theoretical insights (the fragments) are combined into more comprehensive theoretical notions that together form a behavior model. Figure 8b presents a different approach. It reverses the implicit assumption in psychology that behavior, as a highly complex phenomenon, can only be analysed by means of a highly complex and sophisticated approach. In fact, a highly complex phenomenon can best be dealt with by a very simple approach. Let’s attempt to start therefore with a simple, general theory from which increasingly more specific insights can be derived, thus adding complexity in a gradual and controlled fashion. At some point, these specific insights may (or may not) tie in with the insights already available in the literature. Instead of merely producing the same results from a differ-

Limitations of psychology

ent starting point, the alternative approach may shed a new light on unsolved mysteries and inconsistencies in the available literature. Individual concepts

Figure 8b

Starting theory construction from scratch, the derivation of more specific insights which later may possibly connect with existing insights.

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Chapter summary

This chapter will provide the basis for a simple, general model. The basis concerns fundamental issues in the analysis of behavior. When we refer to behavior, what exactly do we mean? What are the objects (people; behaviors) of analysis? What are the requirements for the development of a fundamental behavior model and how should it be set up? The metaphor of movement will prove to be helpful here. The chapter ends by selecting models in the literature in which three key behavior determinants are used: motivation, ability and opportunity. It proposes to use their combination as the starting point for more elaborate theory construction.

4.2

People

Although it seems almost too obvious to mention, a behavior analysis starts with the exact identification of the persons whose behavior is under consideration. Of course, we do not mean their personal information. But it should be absolutely clear what type of persons we are focusing on. With regard to this point there should be no confusion among researchers or behavior analysts, whether in academia or in practice. If the identification of the target person(s) is not precise, behavior differences may simply reflect sample differences. Incorrect interpretations are the likely result. For example, a manager may express interest in analysing the behavior of his/her customers. But does he mean buyers, payers, people collecting the merchandise, potential clients, ex-clients, loyal customers, frequent buyers, recent buyers, some combination of these or all of them? For both theoretical and practical reasons, this calls for clear selection criteria. It is suggested here that inconsistencies in behavior findings can to a considerable extent be attributed to carelessness in the selection and identification of the target persons.

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This is a book on human behavior. So who are the humans that we will be considering and what are the behaviors that we will be referring to? In two brief sections below we will identify the critical characteristics of both. This book does not exclude any type of person. Its goal is to construct a behavior model that applies to all individual persons, whatever the nature of their backgrounds, position, function, role or living conditions. People function not only as individuals, but also as members of groups and as participants in institutions. Individuals often associate themselves with other individuals to form groups, so the analysis of behavior should address both individuals and groups. Conversely, when we analyse group behavior, we cannot ignore the behaviors of the individual persons who constitute that group. The starting point of the analysis of behavior will be the individual person.

4.3

Behavior

Behavior is a set of physical and mental activities aimed at the satisfaction of needs, goals, preferences or ambitions that relate to survival, procreation, coping, maintenance and growth. Coping, maintenance and growth serve the quality of life; coping (see also Carver and Connor-Smith, 2010; Snyder, 1999) is aimed at developing a functional and harmonious relationship with the environment under conditions of constraints, limitations and restrictions. If this is achieved, maintenance is aimed at securing a favorable balance between benefits and costs and is expressed in activities that prevent a deterioration of this balance. Growth implies achievement. It has three functions: it creates a buffer of resources, it avoids habituation/tedium (it fights boredom) and it manages one’s position relative to relevant others in the social domain. Coping and maintenance are associated with certainty and stability, while growth is more associated with mastering, enjoyment and variety. Coping is a relatively defensive type of behavior as compared to growth-oriented behavior. Behavior is viewed here as the collection of all possible activities, actions or acts that a person may engage in. Behavior involves some type of change: a change of position in a literal, physical sense or in a cognitive, affective, emotional or even spiritual sense. Change involves motion or movement, but not all movement implies behavior. In order to be able to speak of behavior, the person should somehow control the movement. If the move-

Back to basics

ment can be attributed purely to external factors, it does not make sense to speak of ‘behavior’. For example, a person is moving if s/he is transported by a train, but this is not behavior. To travel, on the other hand, involves concrete activities that are at least partly under the control of the person and to the extent that this is the case should be considered behavior. ‘Behavior’ is a generic term. It comprises ‘activities’, ‘actions’, and ‘acts’ that are more specifically associated with some notion of direction, drive and energy. Behavior inevitably involves some type of internal and/or external change. Concrete behavior is expressed in a verb that relates to some type of action and excludes verbs expressing passive or static states like, for example: to be, to have, to know and to worry. Strictly speaking, ‘to have a lot to do’ and ‘to be happy’, are examples of verbs that do not refer to behaviors. But the identification of behavior may be tricky. At first sight, ‘to stop a car’ seems to be a static example, but the static nature only refers to the end state; the behavior itself is dynamic and involves controlled movement. ‘To be active’ presents an example of the opposite. It appears to be dynamic, but no active behavior is involved. In this sense, ‘to be active’ is like ‘to be rich’. ‘To wait for the doctor’ on the other hand, involves the trade-off between two actions: to wait or to leave. Both are active behaviors. So a particular behavior may be active in nature even if no motor activity seems to be involved. Stated more simply: behavior in this book comprises all things that people may do. This simple definition will be elaborated with the help of 10 dimensions that will be described below. At the same time they will give us the opportunity to indicate what types of behavior will and will not be addressed in this book. In random order: 1. The level of behavior aggregation Behavior may range from very simple to very complex. Examples of simple activities are punching a key on a keyboard, turning a page, taking a bite from an apple and kicking a ball. Examples of complex activities are carrying out a project, chairing a meeting, studying at a university, educating children and leading a multinational company. Almost all activities can be aggregated to more complex, comprehensive behaviors and almost all activities can be divided into more specific behaviors. At the extremes we find the simplest possible motor movements (like ‘to raise an index finger’) and the most complex behavior: ‘to live’. Behavior can be compared with an endless series of Russian Matryoshka dolls. None of the levels (dolls) will be excluded from the analysis.

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2. The origin of behavior Behavior is ‘organized’ in the central nervous system or in the autonomous nervous system. In the former, activities result from thinking, making trade-offs and decisions (at ‘free will’) such as reading this book. Other ‘behaviors’ are controlled by the autonomous nervous system like breathing, sleeping and blinking the eyes. These take place mostly involuntarily and without a person’s conscious awareness. The distinction is not an absolute one. Some autonomous behaviors can (also) be the result of some cognitive activity. People may decide to take a deep breath, for example. Conversely, some behaviors that originally required deliberate thinking may develop into almost autonomous behaviors like pressing the accelerator when driving the car to work. In this book, purely autonomous behavior (breathing etc.) will not be the focus of our interest. As indicated before, we will primarily address activities that persons have some control over: that they may decide to perform, although the decision itself may be made subconsciously. In this sense it differs from reflexes. Reflexes are not under the control of the person. Blinking the eyes may be a reflex (and in that case is not considered an object of analysis here) or can take place at free will – in which case it is taken as a behavior. For the same reason, falling asleep and waking up are not regarded as behaviors here. Staying awake is not either, unless it is the focus of an effort (for example, when reading a dull book on behavior). 3. Behavior observability A particular behavior can be placed on a dimension that relates to the degree to which others can directly observe it. Here, the relevant distinction is between ‘overt’ and ‘covert’ behavior (as proposed by Skinner, 1990). Overt behaviors are activities that involve visible or otherwise observable motor movements. Thinking is a covert activity, taking place ‘between the ears’. Note that some authors require that something change in the environment in order to refer to behavior (see, for example, Cooper et al., 2007). The focus on external change, however, would exclude internal, covert behaviors like considering alternatives, forming a preference, agreeing with a statement, making a mental trade-off and making a decision. Although these behaviors are not directly visible and do not represent physical movement, they can be viewed as manifestations of what we may call ‘mental movement’ and therefore should be dealt with. It would seriously limit the scope of our analysis if we were to disregard them. So we will attend to both overt and covert behavior. (For sake of clarity it should be added that covert

Back to basics

behaviors possibly can be made overt, for example with the help of behavior research, brain imaging techniques or, more simply, by direct questioning). 4. Normal/abnormal behavior A distinction can be made between normal and abnormal behavior. Also these behaviors are positioned on a continuum. This is not the place to determine what distinguishes normal behavior from abnormal behavior. In fact, it is impossible to provide a distinct dividing line. A solution might be to refer to bell-shaped ‘normal’ distributions and view as abnormal all behaviors that fall beyond the two or three standard deviation range on either side. However, this would be tricky for all kinds of reasons, including ethical ones. Anyhow, the dimension is not associated with a judgment or evaluation here. In another interpretation all behaviors that can be placed under the bell of the distribution are normal, only some are more extreme than others. It would suggest a difference between ‘extreme but normal’ and ‘abnormal’. Here we will adopt the comfortable position that behavior is labelled ‘abnormal’ if it is qualified as such by formally trained professionals. All other behaviors are considered ‘normal’ – whatever that may be. Anyhow, we will not deal with abnormal or pathological behavior in this book (although several trained psychiatrists do see possibilities for using the Triad model for the explanation of specific abnormal behaviors). In the remainder of the text we will stay safely under the bell. 5. Action and reaction Some activities are the result of a person’s initiative. Other activities can be viewed as reactions to external stimuli or events. We will consider both actions and reactions. But again, if a particular behavior is the pure result of coercion or some external force, that ‘behavior’ or movement is excluded from analysis. Falling down the stairs tends to be an effect of gravity and is usually not the result of a deliberate decision to do so. It will not be considered here. 6. Individual and group behavior Some behaviors are strictly individual – solo – in nature, like daydreaming. Other behaviors are socially oriented and are very unlikely without the presence, help, assistance, input or feedback of other persons. Individual persons are very unlikely to communicate, cooperate and compete all by themselves. The same seems true for dancing the tango. In these cases, understanding behavior may involve understanding the

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7.

8.

9.

10.

behaviors of more than one individual person. In principle, both individual and social (individual) behaviors fall within the scope of our analysis. Incidental and repetitive behavior Even although behavior is enormously varied, many behaviors more or less repeat themselves. To a large extent, behavior is repetitive. Behaviors may be placed on a dimension ranging from routine and habitual to incidental, innovative and exploratory. All positions on this dimension are considered of interest here. Impulsive and deliberate behavior; behavior consciousness Some behaviors take place after careful deliberation; other behaviors are shown on impulse. All positions on the dimension of which of these two behaviors are the extremes are relevant in the analysis of behavior. Passive and energetic behavior Some behaviors are passive, calm and restrained, while other behaviors are lively, energetic and exuberant. The distinction refers to the level of arousal (see e.g. Berlyne, 1960 for a seminal publication) or behavior intensity. It is related to the notion of involvement (e.g. Petty and Cacioppo, 1986). Behaviors may vary in intensity, which implies that it should be included in the analysis of behavior. Behavior on a time dimension The final dimension that may be used for the identification and description of behavior concerns time. Behavior may not only be aggregated and disaggregated; it may also be dissected in different phases. Behavior often involves a chain of activities that each may be regarded in their own right. There is no right or wrong way of dividing behavior into a string of different activities. Compared to the possibilities for aggregation or disaggregation, the potential for division over time is endless. The shortest behavior phase is determined by the minimum amount of time it takes to show some motor or mental activity. The longest behavior phase concerns the duration of a lifetime.

This list of dimensions is far from complete. Many more dimensions and behavior characteristics might be added. Their combination indicates that it is quite ambitious to present a theoretical approach that applies indiscriminately to all people and to all possible behaviors, however enormous their frequency, diversity and variety.

Back to basics

4.4

Towards a basic model

The first question that may be raised is whether there is any reason to be optimistic with regard to the possibility of presenting a fundamental, general model that is not based on a reconstruction of available evidence. Before answering this question with a firm response, let’s refer to another metaphor. Independently from each other, Peters and Williams develop an interest in retailing and both start a paint store. Peters writes an elaborate business plan, goes to the bank, requests and receives a considerable mortgage for the construction of a building and a loan for the purchase of a broad inventory of different colors of paint. He is proud to have about 800 different colors on the shelves. Customers like the store, its nice atmosphere and the wide selection of paints. However, they also feel that the price level is high and that choosing among the enormous amount of different colors is quite a hassle. Peters tries to consult each individual client but there is just too little time to do so. Three months after the Grand Opening, the paint cans go on sale at a large discount. And another few months later, Peters is broke. Williams adopts a different approach. He rents a small shop close to a doit-yourself centre and uses his savings to buy three large containers with red, blue, and yellow paint. To these he adds a container with black paint and a container with white paint. Williams attaches a color scheme to the wall that invites customers to identify and select their preferred color. With the help of a unique mixing formula for each color, Williams produces the requested paint on the spot. While the number of basic colors is very limited, the range of colors and hues that customers can choose from is virtually unlimited. Because of his low operating costs Williams charges low prices, has a simple but effective marketing strategy and twitter does the rest. After five years Williams retires to the Bahamas. The contrast between the examples indicates that less may be more. Peters’ case also shows that complexity may not be reduced to simplicity. High complexity, both in the paint business and in science, functions like an astronomical black hole: it is much easier to get in than out. Similarly, it is virtually impossible to reduce (or ‘defragment’) 800 different colors to just three basic colors. Entropy happens to be a one-way street. However, the other way around, expanding simplicity to complexity is relatively easy.

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The three basic colors may be combined in an endless number of ways, producing a sheer infinite variety of colors. Black and white are only added to create the desired shade. So, in general, an approach that originates from simplicity has three advantages: (1) the reference point is always the same – in the paint example it is the combination of the three basic colors, (2) elaboration or expansion can take place in a systematic and controlled fashion and (3) elaboration can stop at any moment – for example, when the numbers of options becomes confusing or the overview is lost. These notions constitute the basic idea of the approach that will shortly be introduced. In other words: we will try to identify the basic colors that together constitute the colorful phenomenon of behavior. Simplification can be understood in either a negative or a positive way. A negative interpretation is that the only goal of simplification is to reduce the mental burden of the analyst. According to this view, simplification is an easy way out, a cowardly escape from the responsibility of having to deal with complex reality. A more positive interpretation takes simplification as a sincere attempt to focus on the crux of the matter. From this latter perspective, simplification can be a true analytical challenge as the relevance and meaningfulness of the key concepts should be inversely related to their quantity. After all, a smaller number of determinants implies that each of these should account for more variance. And these determinants should be mutually independent in order to guarantee efficiency and avoid redundancy. So in a very simple model the determinants must be limited in number, relevant, meaningful and independent. At first sight it appears that information is lost when the number of determinants decreases. (This was Clarke’s (2007) argument: ‘If (…) reality is disunified, then explanatory unification in the behavioral sciences can only be had at the cost of descriptive inaccuracy’ (…) (p. 22). However, with the help of simplicity other types of information may surface than are revealed by a complex model. Dörner (1996) shows how simpIe models – consisting of key determinants which he refers to as ‘super signals’ – provide the opportunity to fully assess interrelationships and interdependencies between the variables and to study the system’s dynamic characteristics. This is usually not possible with complex models. At the same time it is important to note that the formulation of a simple model does not prevent the possibility of including more complex explana-

Back to basics

tions – as long as the core of the model remains simple. A simple model can be inflated to a higher level of complexity, but a complex model probably cannot be deflated to simplicity. Theory development requires not only the identification of key variables or factors but also the specification of their interrelationships. Variables and relationships together form ‘structural knowledge’ (Dörner, 1996). As noted before, the number of inter-variable relationships increases rapidly with the number of variables (nr = nv(nv-1)/2, where nr is the number of relationships and nv the number of variables). If we were to opt for a ‘theory’ with a single independent variable there would obviously be no relationships to consider. There is only one relationship where two independent variables are concerned. Among three variables there are three relationships and among four variables there are six. So the structural knowledge of the latter option would require information on four variables, on six interrelationships and on the system’s dynamics – that is, the development of the variables and the relationships over time. Even though the complexity of a fourvariable model appears fairly limited, it seems reasonable to argue that this combination is untenable if the goal is to develop a simple model. In a serious attempt to produce a general model it seems ridiculous to pay attention to the number of variables first and subsequently decide about the content of the model. However, it has become apparent that the converse simply does not work. We do have a wealth of knowledge and a multitude of findings, but when it comes to a basic understanding of behavior in general, neither academics nor practitioners have a clue – at least not a systematic one. Thus we seem to have arrived at a point where it is necessary to rethink psychology. It is not useful to continue thinking inside the old box of the discipline, so let’s start thinking out of it. The question whether we need a one-, two-, three- or possibly four-determinant behavior model is, to some extent, comparable with the question whether we would like a one-, two-, three- or four-legged table. We know that the functionality of a one-legged table is very limited and time-bound: without support it will topple over within a second and it will do so in any inconvenient direction. In this respect a table with two legs is more predictable. It will tip over but in only one of two directions. A table with three legs is very stable. It stands even firmer than a table with four legs when the floor is uneven. In a similar way, a ‘three-legged’ behavior model may be more stable than a model with one, two or four determinants. The latter types of models seem to have serious limitations, albeit for different reasons. One- and two-factor

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models may provide less information than we can manage, while a four-factor model seems to provide more information (four variables, six interrelationships plus dynamics) than we can handle. The latter option is not considered feasible and will be ignored. Before we dispose of the one- and two-factor models as well, let’s judge them somewhat more elaborately on the basis of meaning and explanatory value. Obviously, the one-factor model is the simplest, but it is not likely to provide much understanding whatever the content of its single Grand Determinant. ‘Intention’ provides an example. This variable may have predictive value but lacks explanatory value. So if the researcher or practitioner is only interested in the question whether a particular behavior will occur in the future, intention may be a relevant variable. However, if there is an interest in the question why the behavior would take place, intention does not help out. A single determinant risks being tautological as it directly taps into the behavior concerned. The overlap between the answer to the question ‘To what extent do you have the intention to walk?’ and actual walking reflects redundancy and that is not what we mean by simplicity. So intention does not clarify why a behavior does or does not occur. What is more: it is not even very effective as a predictor. A meta-analysis of metaanalyses (!) indicated that intention explains only 28 per cent of the variance of actual behavior (Sheeran, 2002). So, overall, people’s actual behavior tends to deviate considerably from what they intend to do (or from what they indicate that they intend to do). As intention may fluctuate over time and as the amount of time between the assessment of intention and the behavior concerned may vary, two additional concepts have been proposed to increase the predictive power of intention: intention certainty (Sheeran & Abraham, 2003) and temporal stability of intention (Conner et al., 2002). However, these two sub-concepts do not really provide an explanation of behavior either. In fact, they only provide a more complete description of a concept that does not explain behavior. The conclusion is that a one-variable model like intention is not an acceptable candidate for a simple model, even if satellite variables are included in the equation. It is tautological, it leaves much behavior variance unaccounted for and it does not contribute to understanding. The same conclusion may be drawn for other single determinants like the ‘subjective likelihood to engage in a particular behavior X’. When we increase the number of factors to more than one, the nature of the relationship(s) between the factors has to be considered. In general and

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ideally, the determinants in a simple multiple factor behavior model should meet the following requirements: 1. Together with the dependent behavior, they constitute a complete model, meaning that they are capable of representing all possibly relevant behavior determinants. So beyond the key determinants there are no loose, free-floating explanatory fragments that would have to be taken into account. 2. They are conceptually clear and appealing to academics and, preferably, also to practitioners. 3. They are conceptually independent. The value of one determinant does not contain or reflect information on the value(s) of the other determinant(s). 4. The determinants are, or may be assumed to be, approximately equal in terms of their relevance to the understanding of behavior. A model with more or less equally weighted determinants is simpler than a model consisting of variables with very different weights (which would amount to one major determinant and one or two minor determinants). Of course, at the end of the day the importance, relevance or weight is an empirical matter, but when the model is constructed, much effort should go into semantically forming variables for which there is no a priori reason to assume a significant difference in importance. It boils down to creatively playing with words and meanings until conceptual equivalents emerge. Two-factor models open up new analytical possibilities relative to a one-factor model. There are several models in the literature that include two key variables. Examples are provided by expectancy–value models (Weiner, 1992; Eccles et al., 1983; Edwards, 1954). In these models, behavior is seen as the product of (1) the subjective likelihood that a behavior will lead to a valued result and (2) the perceived size of that value. Expectancy-value models are limited in that they primarily address motivational aspects. They do not really help us to understand why a behavior does or does not take place. Of course, the concept of expectancy may represent a whole range of behavior conditions, but we are looking for a model that is somewhat richer in content. The well-known Theory of Reasoned Action (Fishbein and Ajzen’s, 1975) assumes that intention is the main determinant of behavior. Intention, in turn, is determined by two factors: attitude toward the behavior and subjective norms. Attitude toward the behavior is determined by the combination of beliefs and outcome evaluations (comparable to expectancy and value).

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Subjective norms are determined by the combination of normative beliefs and the motivation to comply with these beliefs. So the model is built up over three levels with one, two, and four variables respectively. The model has received much attention in the literature and has been applied successfully in many practical situations (see, for example, Albarracín et al., 2001). Its core consists of two main variables. However, if the goal is to reach simplicity, this model does not meet the requirements. Another limitation is that interactions and dynamic aspects are not taken into account. Furthermore it seems more suited for behavior in a social context than for behavior in general. Petty and Cacioppo (1986) present another example of a two-factor model. This model is specifically aimed at explaining human information processing; it does not address behavior in general. The two key variables are motivation and ability: information processing is claimed to take place to the extent that people are motivated to process and are capable of processing. The idea is that if both motivation and ability are sufficiently present, elaborate information processing will take place. If at least one of these is not sufficiently available, information processing takes place superficially or not at all. This suggests a multiplicative relationship, implying that the value of a determinant is meaningless if the value of the other determinant is zero (meaning that the condition is not met). Although the application of the Elaboration Likelihood Model is restricted to information processing, it seems to suggest an approach that may also be applied to behavior in general. In such a broader interpretation it proposes that a person will engage in a particular behavior if that person is motivated and has the ability to do so. This model is complete, simple, parsimonious and intuitively appealing. However, with regard to ‘ability’ it does not meet the condition of conceptual clarity. This concept seems to represent two fundamentally distinct types of behavior determinants: personal factors and environmental conditions. A person may be capable of engaging in a particular behavior (s/he is able) but at the same time s/he may not be engaging in that behavior (s/he is unable) because of situational constraints. For example, someone is able to drive a car because of the personal skill to do so and unable to drive a car because of a roadblock. In this example, the conditions of ability and inability occur simultaneously, which renders ability a hybrid, and therefore confusing concept. So while promising at first sight, this two-factor model is not the basic model we are looking for.

Back to basics

Clearly neither a one-factor model nor a two-factor model is going to do the job. These models are simple, but too simple. Both types of model stretch parsimony to the point where concepts are so compact that we risk ending up either empty-handed or confused. Now that we have discarded the options with one, two and four determinants, we are stuck with a three-factor model as the only option. Having determined the number of factors, we now have to find a set of determinants that can comply with the various requirements stated earlier. Also, it should provide us with the possibility to assess the three interrelationships and to consider the changes in the values and the relationships that may occur over time. On the one hand, a three-factor model should be inspired by the one- and two-factor models; on the other hand, it should be capable of capturing models with four-plus variables and of carrying the burden of representing the enormous wealth of (fragmented) psychological knowledge. (Note that we are not searching for a three-factor psychological model per se but for a three-factor behavior model. Three-factor models that pertain to the explanation of variables or behavior determinants do not qualify here. For example, we will not discuss the three-factor model of motivation by Vroom (1964).

4.5

A three-factor behavior model

The Theory of Planned Behavior (Ajzen, 1991; 1985) is a well-known threefactor model (although it may also be argued that it is a four-factor model). Its three critical determinants are Behavioral attitude, Subjective norms and Perceived behavioral control. These feed into a fourth variable – Intentions – that, in turn, has a causal relationship with behavior. Behavioral attitude is formed by the combination of three variables: Relative advantage, Compatibility and Complexity; Subjective norm is the result of Normative influences, and Perceived behavioral control is dependent upon Efficacy and Facilitating conditions. Although the model contains concepts that are intuitively appealing, the combination of these concepts does not seem to cover the full array of possible determinants. Moreover the mutual relationships between the concepts are not specified. Although the theory has quite successfully and frequently been applied in practice, if the purpose is to adopt a complete, simple and clear model, the Theory of Planned Behavior does not seem to qualify.

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For that reason, we attempt to find an alternative candidate. We do that on the basis of a metaphor that, in itself, is only remotely related to behavior. Yet it seems to provide a useful suggestion. The proposed metaphor refers to movement, that is: movement in general. The idea is that if we can understand why movement takes place, we may possibly infer a basic understanding of why organisms move – or behave. For the sake of simplicity we will first look at the movement of objects. Then we will move on to the next level: the physical movement or behavior of simple organisms. Later, we will assess whether the provisional insights obtained here may also be transferred to more complex organisms and more complex behaviors. So we will try to identify the quintessence of behavior by covering the continuum ranging from the movement of objects through the physical behavior of animals to the complex mental behavior of human beings. See Figure 9.

Movement of objects

Behavior of simple organisms

Behavior of mammals

Behavior of human beings

Underlying principles

Figure 9

Continuum of different types of movement.

Why do things move? The answer to this seemingly simple question should apply to any object, so let’s take a random one: a golf ball. How can its movement be explained? One option would be to start with the identification of all possible determinants: the ambition, experience and self-confidence of the golfer, her height and weight, the position of the hands on the putter, the material, length and weight of the putter, the location of the sweet spot, the brand, age, size and condition of the ball, the number of dimples, the force exerted by the golfer, the type and length of the grass, the inclination of the slope, the humidity, the position of the sun, the wind velocity, et cetera. After the inventory, we might study the effects of these particular (and other, even more specific) determinants and possibly even some of their interactions. As we have witnessed elsewhere, this type of research might continue forever to the point where fragmentation would suffocate it.

Back to basics

Another option would deliberately avoid fragmentation: simplification, in this particular case to a three-factor model. This approach would reduce the quantity of information on the one hand but would probably increase the overview and quality of understanding on the other. At this point we should address the question what the three basic determinants of movement are. The answer seems fairly straightforward. First, there needs to be some kind of force. This is a critical determinant for any motion to take place. The mere presence of a force may not cause an object to move, however. Hitting a golf ball with a putter will send it rolling towards the hole (the golfer hopes); if the same putter is used to move a brick, the effect is dramatically different. This brings us to a second major determinant of movement: the object should be capable of moving, in this case rolling. A car can move because of its wheels. Without them, it is very unlikely to move, whatever the horsepower its engine can muster. The form of a ball gives an object a perfect capability of moving. Compared to a golf ball a golf cube would present serious complications. But also the combination of force and rolling (moving) capacity may not be enough for movement to take place. Also the conditions under which the force and the rolling qualities act together are important. If the ball is in the ‘rough’ – a golf term for high grass – movement is not as easy as on the green where the grass is very dense and short, even if the force and the movement capacity are the same. So the remaining third factor should refer to the conditions under which motion is to take place. In sum: if we want to predict and understand why a golf ball rolls (and how quickly and how far), we would need information on the exerted force, on the rolling qualities (that is, movement capability) of the ball and on the favourability of the circumstances. Just as red, blue and yellow are the basic colors that can account for all possible colors, force, capacity and situation together can explain motion. As a golf ball is an inanimate object, the force exerted on it can only have an external origin. The ball will not start to move on its own. This is different for organisms. Simple organisms (such as, for example, amoebae) move to survive. In order to cope with their environment, they generate their own force. They also have the physical apparatus to move in order to do whatever is necessary to live, to stay alive and to create new life. In doing so they are dependent upon the circumstances in which movement has to take place. Circumstances relate to the availability of water, the concentration of oxygen, the presence of predators and weather conditions. In the case of mammals, the cause of the force may be externally and/or internally located. Animals may be stimulated to engage in a particular behavior or

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they move because of their internal programming. And for them, the presence of a stimulating force and a capacity to move is not sufficient to bring about movement. A dog on a leash will not chase a rabbit, even if the sight of the rabbit is extremely inviting and the dog is physically well equipped to run (an exception is provided by a very large dog and a very small owner). Basically similar examples can be provided for infants, children and adults. For all positions on the continuum ranging from the movement of inanimate objects to intellectual human activity, the same universal principle applies: movement/behavior takes place if and to the extent that the three conditions of motivation to behave, the capacity to behave and the opportunity to behave are fulfilled. When it comes to the explanation of their movement or behavior, a golf ball, a grasshopper, a hippopotamus and a human being are very much alike. However, different positions on the continuum have their own unique way of dealing with the variables and their interactions. For example, for inanimate objects, only the objective force, capacity and circumstances are relevant. For people, not only the objective versions of these variables count, but also their perceived or anticipated counterparts. This renders a human behavior model more complex than a model on the motion of a golf ball, although the core determinants are essentially the same. The combination of the three determinants is proposed as playing a pivotal role in the explanation and prediction of behavior. The three factors together can be interpreted, metaphorically, as constituting the ‘DNA’ of behavior: they can be applied to all types of behaviors of all kinds of persons in all kinds of situations. In itself, the combination is not a model. It provides only a framework that may serve as a basis for model construction. The three determinants are intuitively appealing. They are not esoteric. Their meaning and relevance can be explained relatively easily to both academics and practitioners as compared to many other psychological concepts. They seem easy to apply in practical decisions regarding behavior. As a matter of fact, some practitioners use them already. For example, the judicial system refers to the combination of the three determinants to establish, post hoc, whether a suspect committed a crime. A judge or a jury requires evidence on the presence of motive, means and opportunity to have engaged in the act of a crime. These terms are the conceptual equivalents of motivation, capacity and opportunity. They allow judges to make predictions in reverse. This concludes the introduction of the three variables that we will work with. They are presented as the fundamental building blocks of behavior. They are assumed to always apply. That is, all behaviors of all people

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under all circumstances can be explained with the help of these three generic factors. The only condition is that the behavior involves some type of activity. Theoretically, the model is straightforward. It claims that for behavior to take place, there needs to be sufficient motivation (giving direction and energy), sufficient resources and sufficiently accommodating circumstances. Together, the three determinants, their interrelationships and their dynamics constitute the ‘Triad model’ (Poiesz, 1999; 1994; 1988), a three-factor model that originally was developed in response to the two-factor information processing model of Petty and Cacioppo (1986). The Triad model split the somewhat ambiguous ability concepts into two distinct determinants: capacity and opportunity, thereby explicitly specifying the two aspects of ability. Also the Triad model adopted a broader field of application than information processing alone. The model will be discussed in more detail (!) later. However, before doing so, it is important to note that, in various disciplines, similar frameworks or models have been developed over the past decades. These will be discussed first. It is interesting to observe that the combination of motivation, capacity and opportunity can be found at quite different locations in the academic literature, in a broad variety of application fields and at various moments in academic history. The different lines of research seem to have developed more or less independently from one another. For example, the three factors have been studied in areas such as consumer behavior (Hoyer and MacInnis, 1997), information processing (Andrews, 1988), the processing of advertising (Lord and Potrevu, 1993; Robben and Poiesz, 1992; MacInnis et al., 1991; MacInnis and Jaworski, 1989; Batra and Ray, 1986), community organization (Cox et al., 1974), social work (Olson, 1994; Compton and Galaway, 1975; Ripple, 1955), social/societal functioning (Horesji, 1976), life performance (Singh, 1988), Human Resource Management/employee performance (Blumberg and Pringle, 1982; Boudreau et al., 2003; Maier, 1955; Boselie et al., 2005; Boxall and Purcell, 2008; Knies, 2012), organizational performance (Applebaum et al., 2000), decision making in firms (Wu et al., 2004), knowledge management (Siemsen et al., 2008; Argote et al., 2003), the use of social capital (Adler and Kwon, 2002; Binney et al., 2006), the use of information and technology products and systems (Hughes, 2007; Ramaswami et al., 1998; Strader and Hendrickson, 1999), education (McGaslin and Good, 1994; Southard et al., 1992; Larson et al., 1987), transport (Bhat and Koppelman, 1993), cultural participation (Wiggins, 2004) and house-

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hold metabolism (Gatersleben and Vlek, 1997). More recently, the three factors have been used to analyze shoplifting behavior (Nederstigt, 2011) and employee participation in organizations (Vermeulen, 2011). Siemsen et al. (2008) summarize the history of the motivation, opportunity and ability (‘MOA‘) framework. They conclude: ‘It has been successfully employed to explain a wide array of behaviors’ (p. 428). Fogg (see, for example, Moraveji, Akasaka, Pea and Fogg, 2011) presents a three-factor behavior model in which two factors are similar to the determinants of the MOA model: Motivation and Ability. He refers to the third determinant as ‘Trigger’. This is a somewhat confusing concept. To some extent it is tautological (a determinant is a trigger by definition) and it seems to tie in directly with the meaning of Motivation. A trigger is a motivational concept by nature. This would imply a conceptual overlap between two of the three determinants in his model. The various publications on the MOA approach add up to a growing recognition of the relevance of the combination of the three key determinants. However, for several possible reasons, its role as an integrative theoretical approach is still very limited. The first reason is that the three-factor notions are scattered over very different areas of the literature where they seem to have originated and still function largely independently from one another. So while the MOA framework may serve as an integrating framework in various subfields of psychology, it does not seem to surface as an integrating framework for the field of psychology as a whole. The second reason is that the number of studies on the MOA framework is negligible relative to the countless studies in psychology. The third reason is that the MOA framework does indeed primarily function as a framework, a threeway grid to categorize behavior determinants, rather than a model. Although the three categories are labelled as the determinants, their mutual relationships, their relationships to behavior and their dynamics over time have not been elaborated extensively (as far as the present author is aware), although several authors propose a multiplicative model. The interpretation of the empirical results on the MOA determinants reported in the literature is hampered by the lack of theory. So the MOA framework is functional for the categorization of existing variables, but thus far its contribution to the understanding of behavior is limited. The various authors interpret the MOA framework differently. One difference relates to the use of terminology. Some refer to ‘motivation, opportu-

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nity and ability’, while others prefer ‘motivation, capacity and opportunity’. In the former case, the model is abbreviated to the ‘MOA framework’ (or ‘AMO-framework’, see Knies, 2012; Hughes, 2007, not to be confused with the ‘OMA framework’ for organization-motivated aggression – see O’LearyKelly et al., 1996). Some authors specify other behavior determinants in addition to MOA determinants. For example, Applebaum et al. (2000) refer to ‘effective discretionary effort’. Hughes (2007) discusses the positioning of a MOA-, AMO- or MAO-like model. He states that such a model ‘is a meta-theory, a high level generalization about the origin of human behavior. As such (…) it can be and has been applied across disciplines. Another indicator of its high generality is the wide breadth of focus. AMO is a formal theory (…) could be used to develop a series of mid-range theories, which have a more limited scope and generate testable hypotheses.’ (pp. 3-4). Although ‘MOA’ is the more frequently used term, thus favouring ‘ability’ over ‘capacity’, questions may be raised with regard to the former concept. Earlier we noted that it has a hybrid meaning: ‘Being able to do something’ relates to both personal qualities and situational or task characteristics. If interpreted in this way, there may be an overlap with the third concept: opportunity. As conceptual clarity is critical for theoretical purposes, a strict separation of person-related and situation- or context-related factors is called for. So in order to avoid confusion, we will not use the word ‘ability’ and will reserve ‘capacity’ for person characteristics and ‘opportunity’ for situational aspects. In line with the terminology in the literature this would amount to the ‘MOC model’. However, the approach that will be explained differs in so many respects from the existing three-letter frameworks that a different label seems justified. The alternative is dubbed ‘Triad model’ or ‘Triad approach’. This model/approach does use insights from the various MOA/AMO/MCO approaches and frameworks. Yet it adds particular theoretical elements that render it quite different. Some initial differences are mentioned here and will be discussed more elaborately later. The Triad approach: • Is based upon a critical assessment of the development of psychology. The MOA framework is part of the prevailing paradigm. This amounts to meta-theoretical, theoretical, methodological, analytical and practical differences between the two approaches. To take one example: the philosophical approach to theory building of the Triad model is very different from the approach adopted by the MOA model(s). The Triad approach

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puts less emphasis on empirical substantiation of each individual step in the process. Rather, it stresses the need to build a full theoretical structure first. • Aspires to integrate insights of psychology, not just to provide a framework for various types of determinants in the various subfields. • Simultaneously considers methodological implications by including the behaviors of research participants and researchers in the analysis. • Places a special emphasis on the relationships between the three main determinants Motivation, Capacity and Opportunity as they develop over time. The bottom line of these characteristics is the argument that the theory or model needs to be elaborated more fully before any hypotheses can be formulated or conclusions can be drawn. If isolated theoretical aspects are empirically tested before a more complete theory is developed, the results may reflect incidental rather than structural effects. In a theoretical vacuum such results risk misinterpretation. An under-emphasis on theory results in an over-emphasis on evidence. This, in turn, may have an impeding if not paralyzing effect on theory construction. Here we take the alternative route of first developing the theory more fully before any empirical test is suggested. The development takes place on purely psycho-logical grounds. No empirical material is presented in this book. This is a mortal sin in a positivistic academic culture, but the risks of presenting theory without evidence are considered acceptable relative to the risks of presenting evidence without a general theory. So the goal is theoretical elaboration, not empirical verification or falsification (as yet).

4.6

Conclusion

The psychological literature provides detailed insights into the nature and effects of particular psychological variables, but does not present an overall, unanimously accepted framework for the explanation of behavior from which more detailed insights may be derived. In academia many experts may be found on particular types of behavior determinants, but even if their expertise is pooled, they may not be able to explain behavior. They are capable of presenting a wide range of possibly relevant explanations, a broad inventory of interesting variables, a large collection of esoteric terms and an overview of methodological complexities, but when it comes to concrete predictions they are likely to stand as empty-handed as lay persons (see, for

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example, interviews with psychologists in the public media). If a similar situation existed in the academic field of physics, there would be detailed insights into the chemical nature of fuel and oxygen, and there would be a deep understanding of temperature dynamics, but physicists would be reluctant to light a barbecue without first doing research. Specialization in psychology increases at a pace that cannot be matched by integration. Insights become deeper and deeper, not broader, thus preventing knowledge domains from becoming interconnected. Gradually, psychology is being reduced to a chaotic mosaic of trivial findings. The field consists of specialists unconnected by generalists. Therefore, a supplementary system is needed that restores the balance between specific and general insights – and the relation between them. Integrative attempts have been relatively scarce. Although the MOAapproach seems the most promising, it is limited because it only presents a framework for structuring behavior determinants; it falls short of presenting a behavior theory. The next chapter will finally present the propositions that together form the Triad model. We will be referring to it as a theory, an approach or a system. And, although its ambitions are higher, it may also function as a framework for the inventory of known behavior determinants. Neither one of these qualifications is excluded beforehand. However, we will prefer to use the term ‘Triad model’ as a summary term for the sake of simplicity.

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The Triad model 5.1

Chapter summary

This chapter explains the various propositions that together form the Triad approach. The nature of the propositions is described in a brief introduction. Their description and justification comprises the major portion of this chapter. The first propositions relate to the three main concepts, their definitions, their values and the general relationship between them. Subsequently, the propositions generate more complex questions like: to what extent are the three concepts independent; what happens if the values differ; is it possible to further differentiate the three factors; how do existing psychological concepts tie in with the model?

5.2

Introduction

The Triad model consists of a set of interrelated theoretical propositions. Propositions are theoretical principles that have not been empirically substantiated. They differ from hypotheses in that they present relatively strong statements that invite the reader to agree or disagree. The propositions of the Triad model are based upon a common background notion (here: the explanation of movement), insights derived from the general psychological literature, logic, notions inspired by other disciplines, experience derived from practical examples and even some common sense. The propositions are intended to be mutually consistent and supportive. Some propositions provide missing links in the overall theoretical structure. While no formal validity can be claimed at this point, the propositions may combine into a structure that is sufficiently self-supporting to suggest the base-level plausibility necessary for formulating research hypotheses. Common sense seems to be a shaky element in the construction of a psychological theory. However, we should not forget that most people are perfectly capable of surviving and even performing well in the absence of a formal training in

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psychology, which means that common sense should not be equated with nonsense. Common sense, the explicit and tacit knowledge that has been accumulated in the history of mankind and in the lives of individual persons, cannot simply be discarded as irrelevant. People do not walk around being stupid, waiting for psychologists to guide them. Most behaviors of most people are quite well understood in everyday life. In (academic) psychology, practical experience is often viewed as an inferior type of evidence because it lacks the precision of scientific inquiry and it suffers from the high risk of observer biases. True, the rigour displayed in psychological research cannot be found in the development of popular behavior knowledge. However, this in itself does not render lay knowledge necessarily invalid. What is more, sometimes painful or embarrassing examples show that behavior researchers themselves can be ‘victims’ of the very biases that they may expect to take place in the general population only. It will be argued later that a considerable part of the empirical evidence published in peerreviewed journals may be seriously and systematically biased as well. This means that the single, simple and unsubstantiated model that will be proposed here is quite arrogant: it will question the validity of a wealth of empirical results acquired in numerous careful studies of highly respected authors in reputable academic journals. However, considering the problems of the discipline discussed earlier, some arrogance seems warranted. Together, the propositions of the Triad model are intended to form a stable structure. As a weak element can endanger a whole structure, a weak proposition may cause the whole model to collapse. This observation should be seen as an invitation to the reader to be very critical. Theoretical progress can only be made if corrections and amendments are proposed. Needless to say, such adaptations should be based on arguments, not emotions. Some academics have declined the Triad approach (or for that matter the three-factor approach) merely on the basis of the number of factors involved. Being involved in highly specialized research, they just cannot accept that, in the end, behavior may possibly be based upon very simple principles. As we will turn now to the development of behavior theory, we have to consider the criteria by which its legitimacy may be judged. Note that it may not be reasonable or fair to judge a new approach on the basis of criteria dictated by the ‘old’ approach. Where relevant, new criteria will be proposed.

The Triad model

5.3

Triad model application conditions

The word ‘application’ is to be used in a broad sense here. It includes the application in research settings, in field studies and in real life. So the meaning of the word is not limited to usage in actual situations here. The model is intended for use in attempts to explain behavior in retrospect, to understand behavior taking place at present and to assess the probability of future behavior. Prior to these attempts, a Triad analysis needs to take place. For the sake of efficiency, instead of referring to explanation, understanding or prediction, we will more generally refer to the analysis and to the analyst. The model’s application requires a careful and explicit approach to avoid confusion and misunderstanding. The first two steps in the analytical process have been dealt with already: (1) the identification of the target person or group and (2 the identification of the target or criterion behavior. There should be no difference between the behavior as identified by the analyst and the subjective definition of that behavior by the target person or group. A difference might lead to serious interpretation problems later in the analysis. For example, a behavior analyst sees a man running in the street, which he interprets as a form of physical exercise. To the runner, however, his behavior is simply an attempt to catch the bus. Although the two behaviors appear the same at first sight, ‘to exercise’ and ‘to catch a bus’ are quite different behaviors. Obviously, in this case only the latter behavior should be the focus of analysis. Some policymakers erroneously apply the model to persons rather than to their behavior. It is very important to note that the Triad model is a behavior model, not a people model. According to the model it does not make sense to refer to people as being motivated, as being capable or as having sufficient opportunity. This would falsely suggest that these are general personal characteristics that always apply, regardless of the situation and the point in time. A person is not motivated at all times, nor is s/he capable of anything all the time. The same applies to opportunity. A highly intelligent person may seem capable all the time, but high mental capacity cannot be mobilized for all behaviors of that person, ranging from solving a mathematical equation to climbing a mountain. So the model only applies to behavior, that is, a particular, specified type of behavior. Lack of clarity on the target person(s) and/or the target behavior(s) is a source of much avoid-

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able confusion among users of the model, even when warned beforehand that such confusion is likely to take place. In many policy decisions the question of what the target persons should DO (differently) is not even considered, which may be a major cause of implementation problems. For example, if a change of policy is announced by the board of a company, the information often fails to indicate what concrete behavior changes are required. The employees are then most likely to continue their typical behavior, thus unknowingly preventing the new policy from being implemented. Generally, when the aiming is off, the target is missed. This applies to policy measures as well. The choice of a particular behavior involves a choice of a behavior level (specific-general) and for a behavior phase (earlier-later in the sequence). The combination of a level and a phase is called ‘behavior unit’ which will be referred to further as ‘behavior X’. An example of behavior X would be ‘doing homework in the second semester’. Behavior X may consist of several adjacent cells in the matrix of Figure 10.

General

Level

Specific 1

2

3

4

.. Process phases

Figure 10

The identification of a behavior unit (‘behavior X’). All rectangular or square shapes that can possibly be drawn in this figure – some examples are drawn – depict behavior units, the largest of which (‘to live’) would fill the total matrix.

The Triad model

There is no particular rule that indicates how behaviors should be split in order to be subject to an analysis. The general idea is that if the goal is more important and/or more precision is required, relatively small behavior units are considered in combination. For example, if behavior X is to successfully finish a project, a behavior analysis may be performed for each of the consecutive phases of the project. The outcome of the previous phase determines the input to the next phase. As is true for the links in a chain, the weakest phase is the most dominant for the outcome, as it may not be compensated by more successful phases. The terms ‘Triad determinants’, ‘Triad variables’, ‘Triad dimensions’ and ‘Triad factors’ imply no differences and may be used as equivalents of independent variables. For the behavior we want to explain or predict we use the terms ‘dependent behavior’, ‘criterion behavior’ or ‘behavior X’.

5.4

Triad model propositions

Some preliminary remarks: • Many of the propositions are likely to raise questions. Unfortunately, these cannot be answered simultaneously. The questions that are most frequently asked will be dealt with at some point in the text. Since the propositions can only be dealt with consecutively, the reader may have to be patient. • There are many different possible orders or sequences in which the propositions can be presented. The particular order selected may not match the order that the reader might have preferred. • The large number of principles seems to violate the notion of simplicity. However, the basic idea is and remains simple. It is the foundation of all propositions. Again: complexity cannot be reduced to simplicity, but simplicity may be extended to complexity – as long as the core remains simple. Where simplicity ends and complexity starts is up to the individual reader. Most practitioners are happy to work with a limited number of propositions, while students in behavior disciplines may be interested in a larger number and academic researchers may judge the number presented here as too small (or too large if they cannot agree with the model). • It is possible that the meaning of a particular proposition only becomes clear if it is associated with another proposition. Not all combinations can be discussed here.

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• Although the model can be applied to both individual and group behavior, the focus here is on individual behavior. • In order to distinguish between determinants that relate to the Triad model and equivalent determinants of other models, the Triad determinants will be written with a capital. So ‘Motivation’ is a Triad determinant, ‘motivation’ is not. • The description of the model is structured by way of 12 categories of propositions: Triad determinants and values, Triad value assessments, Triad model basics, behavior aspects, satisfaction and wellbeing, intrinsic and extrinsic values, balance effects, reactance effects, over-stretched Triad values, the role of feedback, emotions and individual differences. • The propositions will all be denoted by the capital letter P and a number. They are presented in a particular order. Earlier propositions are supposed to support the interpretation of propositions presented later. Therefore, they should not be read in a random order. Triad determinants and values P1

Behavior is the result of an explicit or implicit comparison between positive outcomes and costs in the context of facilitating or limiting conditions. Positive outcomes relate to ambitions, goals, needs, desires, et cetera. The prevention or avoidance of a negative outcome may be viewed as a positive outcome. Costs refer to the expenditure of scarce resources and should be understood in a broad sense. They also include behavioral costs (Verhallen and Pieters, 1984), such as physical and psychic costs. Boredom, irritation, uncertainty and fear are examples of the latter.

P2

The attractiveness of a positive outcome may be represented by the concept of Motivation, the availability of behavior-relevant resources by Capacity and the condition of the context by Opportunity. Motivation, Capacity and Opportunity are referred to as the ‘Triad determinants’.

P3

The combination of Motivation, Capacity and Opportunity is used to explain, predict and influence behavior. The three determinants function simultaneously, as a set. None of the three can be ignored. The determinants should not be considered consecutively. Compare the movement of the golf ball that cannot be explained by either the force, the rolling capacity or the rolling conditions alone, or by

The Triad model

looking at these determinants at different moments in time. This is important as it means that it does not make sense to try to analyse behavior without taking the entire combination of determinants into account. Both in academic research and policy making there is a strong tendency to focus on one or two of the determinants only and to ignore the remaining one(s). Ceteris paribus. P4

Motivation is the extent to which a person is attracted to the outcome of behavior X and/or to being engaged in the behavior itself. A person may like to play golf because of the prize that can be won or because of the intriguing nature of the sport. Motivation can be viewed as the energy potential (physical, mental, emotional) that a person is willing to allocate to achieving a particular goal or to being active in a particular behavior. Motivation is a summary term for a myriad of motivation related concepts, ranging from ambitions, needs, motives, drives and interests, to rewards, incentives, punishments, compliments, and so on. Capacity is the extent to which a person has the personal qualities, characteristics and means to engage in behavior X. Capacity reflects the resources that are available for behavior X. Capacity is also a summary term for a large variety of more specific power and competence related factors. Examples are physical condition, intelligence, knowledge, expertise, creativity, skill, talent, tools, et cetera. Opportunity is the extent to which conditions external to the person facilitate or hamper the engagement in behavior X. Opportunity refers to the favourability/unfavourability of the situation to being involved in a particular behavior. Examples are the geophysical context, material conditions, the temperature, the weather conditions, the amount of space, the quality of the space, the distance, the availability of time, the quality of the infrastructure, et cetera. Like Motivation and Capacity, Opportunity also summarizes the different notions in one concept. For Opportunity to be interpreted correctly it is important that: • Its interpretation is not limited to the meaning of (bad) luck or the (dis)advantageous likelihood of achieving an outcome (as in: ‘This business relationship provides a great opportunity’). It has a

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much broader meaning than that. It concerns the overall favourability of the circumstances. • Facilitation is not equated with stimulation or motivation, and hampering should not be understood as a form of negative motivation. Opportunity refers to the degree to which circumstances allow for the possibility of a particular behavior. For example, if the crowd alongside the road cheers a marathon runner, the stimulation is an aspect of Motivation, not of Opportunity. P5

Each of the Triad determinants acquires its specific meaning in relation to the selected criterion behavior: behavior X. For example, if the target behavior is ‘to pay attention to a traffic sign’, Motivation refers to sensory triggers (e.g. a blinking light) or words signifying danger (‘Caution!’). In the case of sports activities, Capacity is likely to be of a physical nature like physical condition, technical skill and talent. If the sport is playing chess, Capacity refers more to mental characteristics like intelligence, expertise, experience and concentration. If behavior X is ‘to buy a motorcycle’ Capacity refers to financial capacity: having money. A mountain climber considers the quality of his equipment – ropes, anchors and locks – as part of his Capacity. In the case of job activities, Opportunity refers to the working conditions and the time available to do the work; in the case of ice skating, Opportunity relates to the presence and quality of the ice and, if the skating is in the open air, the weather conditions.

P6

The Triad determinants are conceptually independent. Knowledge of the value of one of the factors does not hold or reflect information on the value(s) of the other (two) factor(s). For example, a person may have a high Motivation and have a low Capacity at the same time. A high level of Capacity does not imply a high or low level of Opportunity. A high level of Opportunity has no implications for the level of Motivation. So the three determinants are conceptually distinct. (Over time, however, the values may influence one another. This will be discussed later). In practice, it is important to avoid confusion between the three concepts. For example, the assumption that a person who is competent to show a particular behavior will be motivated as well may reflect a serious interpretation error.

The Triad model

P7

Motivation (energy potential), Capacity and Opportunity are scarce resources. They are not available in unlimited quantities. The allocation of the resources to behavior X implies that these resources are not available to behavior Y. Motivation, Capacity and Opportunity each has a stock or supply and a budget. The budget can be the same as or lower than the stock. There may also be a difference between the budget and the allocation. For example, a person may have an excellent physical condition (stock of Capacity). However, his Capacity budget for a particular behavior – cleaning the garage – may be rather low. And the actual allocation out of this budget may still be lower. This proposition underlines, once more, that the Triad determinants apply to behavior (budgets and allocations) and not to persons (stock). So if a person has a very high intelligence (stock), then this does not necessarily mean that he also has a large capacity budget for all behaviors that require intelligence as a resource. Time is a resource that, in principle, has a stock of 24 hours per day, seven days per week, et cetera; the time budget for some behavior is bound to be lower. Stock does not determine budget and budget sets the tentative limits to the allocation. A budget for a particular behavior may be limited because resources have been reserved for or used by one or more other behaviors.

P8

The three determinants apply to all behaviors of all organisms. Higher-level organisms differ from lower level organisms in the degree of sophistication when dealing with the set. For example, for lower level organisms only the objective values of these determinants count, while in the case of higher-level organisms perceptions, interpretations, anticipations, et cetera also enter into the equation.

P9

The three determinants subsume all possible behavior determinants: it is assumed that there is no active behavior that cannot be explained by their combination. This proposition implies that all existing concepts in psychology can be related to the set of the three determinants and that no other concepts are needed to position them. There is not necessarily a one-to-one relationship between existing concepts and one of the three determinants. A particular concept may relate to two determinants at the same time. For example, a message may have a positive effect on both Motivation and Capacity to process information (behavior X) due to its clarity

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and visual attractiveness. Vice versa, a particular cause of behavior always relates to either one or a combination of the three factors. The present proposition also implies the far-reaching assumption that, ultimately, the model’s principles cannot be in conflict with any empirical finding in the academic literature, including the findings of studies which are conflicting between themselves. Of course, this does not rule out the possibility that the interpretation provided by the Triad model may deviate from that provided by the author(s) of the original publication. (Note that a Triad interpretation tends to be far more generic than the detailed explanations provided in the academic literature. Also, a post hoc Triad interpretation risks being subject to wishful thinking and, therefore, can only be suggested with utmost care). (Ramaswami et al. (1998) suggested an ‘Ability, Motivation and Opportunity’ model for understanding consumer behavior in electronic markets but, somewhat mysteriously, included demographic factors as a fourth determinant. The added value of the latter is not clear, especially because, in principle, the effects of demographic factors can be subsumed by the other three factors. Depending upon the type of behavior under consideration, education, for example, may strongly relate to Capacity. Geographic location, on the other hand, may tie in directly with Opportunity). P10 A person’s Motivation, Capacity and Opportunity to engage in a particular behavior may have different origins. These are, for example, biological, genetic, physical, physiological, historical, educational, pedagogical, social, cultural, situational, financial, judicial and chemical (drugs and alcohol). In principle, these and other origins might all be included in the analysis. However, it was decided earlier to simplify matters by focusing on generic determinants only. So we will not make any attempt to explain Motivation, Capacity and Opportunity. Such an attempt might be meaningful or interesting, but it would amount to an analysis of determinants of determinants – the type of research that already dominates the literature and that was criticized earlier. P11 Each of the Triad determinants can be viewed as a continuum ranging from completely absent to completely present. Corresponding

The Triad model

numerical values may be set, more or less randomly, as ranging from 0.0 to 1.0. The value 1.0 corresponds to the maximum level of Motivation/Capacity/Opportunity. A score of 1.0 represents the maximum level that is functional for a particular behavior. P12 There are no negative values, nor should a low value be interpreted as a negative value. Value changes can be either positive or negative. Steps can be taken in sizes of, for example, 0.1, but may vary in size as long as the steps are equal over the three dimensions. The values are referred to as the ‘M value‘, ‘C value’ and ‘O value’ or simply as ‘M’, ‘C’ and ‘O’. Values may be referred to as, for example, ‘a medium M’, ‘a low O’ or a ‘high C’ (opera singers might consider the latter term somewhat confusing here). P13 A distinction can be made between subjective, objective or normative values of Motivation, Capacity and Opportunity. See the matrix presented in Table 1. Table 1 Values

Types of M, C and O values. M

C

O

Subjective Objective Normative

For most behaviors, subjective values represent the psychological reality of the person(s) concerned. These values form the basis of the analysis. Subjective and objective values may overlap, but everyday experience shows that people’s perceptions often seriously deviate from objective reality (which prompts the philosophical question: what is reality?). Persons who feel that they have more Capacity than is warranted by their actual capabilities are referred to as being overconfident. Subjective perceptions are critical for the onset of behavior. Under the influence of alcohol, for example, people’s subjective assessment of their driving capacity tends to deviate positively from their actual, objective capacity. Drunk drivers overestimate themselves. The subjective perception of capacity allows them to drive, provided that the levels of Motivation and Opportu-

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nity are sufficient. Before they reach their destination, their objective Capacity may confront them with a harsh reality. So behavior takes place on the basis of subjective values, but if objective values pose constraints, subjective values may not circumvent them. A karate player subjectively feels that he has the capacity to break a brick with his bare right hand. However, it is his objective capacity that determines whether his hand breaks the brick or vice versa. The distinction between normative, objective and subjective values applies to each of the three Triad determinants. Objective motivation is a somewhat difficult concept, particularly because the word ‘motivation’ itself has a subjective ring to it. If we replace it with the conceptual equivalent ‘need’ for a moment, it becomes easier to see why it is relevant to make the distinction between an objective and a subjective version. Examples may be found in various areas of (social) life. An anorexic person claims to be overweight and subjectively does not feel the need to ingest food (is not ‘motivated’ to eat). However, from an objective, medical standpoint, s/he urgently needs nourishment. Vice versa, a drug user subjectively ‘needs’ his/her heroin; objectively s/he does not. The distinction between the three types of value also applies to capacity and opportunity. An employee is objectively capable of performing a task. However, he feels subjectively incapable due to a general lack of confidence. And his supervisor indicates that he should be capable of successfully finishing the task (normative value). Policymakers and professionals often fail to make a distinction between normative, objective and subjective values. ‘As this medicine is important for my patient, she will be motivated to ingest it’. In that case, normative motivation (referring to what should be done) is incorrectly assumed to be the same as subjective motivation – the motivation of the target person. Policymakers tend to ignore the subjective values of targeted persons. Too often, they produce policies that are more in tune with their own subjective values (or with objective or normative values) than with the subjective values of the targeted audiences. Such policies are not the most successful. A marketer ‘just knows’ that her product outperforms a competitor’s product and that, therefore, the market share of her product will grow more

The Triad model

quickly. An engineer ‘just knows’ that his highly sophisticated product is user friendly. These professionals are so convinced of their own subjective viewpoint that its validity is not checked. Their professional life holds lots of surprises. P14 The distinction between subjective, objective and normative perspectives shows that discussions on the rationality of behavior are fruitless – unless the perspective is explicitly identified. After all, what is rational from one perspective may not be rational from another. In principle, no perspective is superior to the other one(s). P15 The perceptions prompt the question whether a person correctly positions him/herself vis-à-vis a determinant. Another type of perception relates to the determinant itself: does a person correctly perceive the causes of his/her behavior? For example, a person is highly motivated to show behavior X. She perceives her performance as determined largely by her Capacity while, in fact, it was predominantly prompted by Opportunity. In a yachting race, the winner thinks that her success is due to her skill and insight (Capacity), while it was a local gust of wind (Opportunity) that pushed her over the finish. So perceptions relate to both determinants and values. In addition to perceptions and misperceptions of values, there are attributions and misattributions to behavior causes. Subjectively ‘correct’ attributions may in fact be objective misattributions. A behavior analyst should take the two types of perceptions into account. P16 The three components are assumed to be experienced differently. Over behaviors, Motivation, Capacity and Opportunity are hierarchically ranked in terms of the strength of the association with the person him/herself. Motivation is perceived as more characteristic of the person than Capacity. In its turn, Capacity is more central to the person than Opportunity. A person experiences a reduction of Opportunity as the least problematic and the reduction of a Motivation value as the most problematic (if we hold the size of the changes constant). In other words, Motivation is experienced as most personal, then Capacity and finally Opportunity. This centrality of the components can be visualized symbolically with the help of concentric circles (see Figure 11)

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Motivation Capacity Self Opportunity

Figure 11

The centrality of the Triad determinants relative to the self.

The hierarchical ordering is expressed in, for example, self-serving tendencies (see Brehm and Brehm, 1981). If a person is eager to win a bicycle race, he is more likely to attribute a successful outcome to his personal qualities (Motivation and Capacity) than to lucky circumstances (Opportunity). On the other hand, if he loses, he is more likely to attribute the outcome to unfavorable circumstances (Opportunity) rather than to a lack of Capacity and/or Motivation (compare Ross, 1977). When a race is lost, an attribution to Motivation is unavoidable only if attribution to either Opportunity or Capacity is impossible: ‘I did not care about winning today’. The dissimilarity of the three determinants will prove helpful in the interpretation of some of the effects that will be explained later. P17 The values of the three factors may change spontaneously over time. Examples can be found in everyday life. Here some examples are mentioned without reference to the corresponding behaviors. See Table 2. Table 2

Examples of positive and negative changes of the Triad values. Positive change

Negative change

Motivation:

An interest develops

Boredom/tedium sets in

Capacity:

A skill improves

Getting tired

Opportunity:

The weather clears up

Time runs out

The Triad model

121

A rather self-evident and superfluous observation is that behavior is likely to change continuously and may do so for different reasons. It is important to note that spontaneous change may be caused by various determinants, either alone or in combination, and that a focus on one or two of these determinants may prove to be misleading. Different determinants may be responsible for behavior changes at different points in time, making post hoc analyses fairly risky. P18 The Triad model is a model of individual behavior. For the critical or criterion behavior, behavior X, each individual has his/her particular Triad profile. Of course, the profiles can be averaged for an existing group of persons (if a normal distribution can be assumed to apply). Alternatively, people may be grouped or segmented on the basis of their Triad profiles – the combination of the three Triad values. If, for example, each of the factors only has two possible values (positive and not positive), then this would amount to 8 (23) combinations. See Table 3. Table 3

Triad segmentation (example). A plus stands for positive, a zero stands for not positive. A Triad profile is provided for each segment.

Segment

M

C

O

1

+

+

+

2

+

+

0

3

+

0

+

4

+

0

0

5

0

+

+

6

0

+

0

7

0

0

+

8

0

0

0

Segment 1 often represents the ‘ideal’ target group for a policymaker. This segment includes persons who are highly interested, who have the required resources and also have sufficient opportunity to engage in the behavior that the policymaker requires. Segment 8 presents the opposite Triad profile. This segment is hopeless from a policy point of view. Often, the first segment is one of the smaller segments (if not the smallest). The vast majority of the population tends to be found in the other segments with one or more unfavorable Triad values. From a policy perspective it may be

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wiser to focus on these latter segments than on Segment 1. It is remarkable, however, that policy measures show a strong tendency to overestimate the target audience and take the first segment as the primary focus of interest. As a result, many measures attempt to motivate people who are already motivated, to inform people who are already informed, and to create favorable conditions for people who already have them. Lopsided policies lead to ineffective and inefficient measures. Advertising is one area in which this is apparent. Many messages violate the rule that recipients have to be motivated and have to understand the message and have to have the time to process the message in order for it to be processed. Both advertisers and advertising agencies tend to be too optimistic with regard to the three conditions, resulting in highly creative, ineffective ads (see also Petty and Cacioppo, 1986). The advertiser usually views (and evaluates) a TV commercial before it is distributed through the media. Behavior X is then ‘to process the message’, which is the same behavior that will have to be shown later by the target audience. Message processing is an active behaviour and, therefore, is subject to Triad reasoning. The advertiser processing the commercial is likely to have high M, C and O values to process the commercial. He spends a lot of money on the campaign and the intended advertising effect is commercially important (high Motivation); he knows the content of the message as it was he himself who briefed it (high Capacity), and the conditions under which the concept message is processed (Opportunity) are ideal: a large screen in a spacious room with dimmed light, no background noise, no talking bystanders. The exposure of the message is even repeated. Under these ideal conditions, message processing is also ideal. The advertiser is very likely to regard the commercial as highly effective. When the message is aired, it meets an audience that is watching TV to see a movie and is not particularly interested in commercials. Many viewers are tired at the end of a working day and are distracted by phone calls and family conversations. Their M, C and O values to watch the commercial (the one in the example above) are low. So the effectiveness of the message is low too. Here we have a situation in which one party with high Triad values makes a prediction for another party with low or lower Triad values – a beautiful case of self-deception. The practical lesson is that if people have a particular interest, expertise or context that deviates from those of the target person(s) they should refrain

.

The Triad model

from an assessment of other people’s behavior. Engineers should be forbidden to write manuals for consumer products unless the objective is to confuse and irritate customers. Triad value assessments There are basically four ways to assess subjective levels of M, C and O. 1. A subjective, informal assessment of the values by the analyst. Note that this amounts to a subjective assessment of subjective values. Of course, subjectivity may entail the risk of incorrect assessments. 2. A subjective assessment by a team of analysts, experts or professionals. If differences are observed between the assessments, average values over assessors may be calculated for M, C and O (provided the assessments do not differ strongly). So in the case of group values, average assessor values are determined for average group values. This may seem complex, but it is in fact quite similar to what we do in daily life when we subjectively assess how other people make subjective assessments of their behavior conditions. The Triad approach only differs in that it acknowledges this and requires things to be done in a systematic manner. Although subjectivity effects may be reduced by means of multiple judgments, the risk of these effects still exists. The risk may be reduced with the help of the following two methods. 3. Qualitative research in which the analyst assesses the three values by directly interacting with the target person or a sample of the target group. Employee evaluation is an example of this method. In several organizations supervisors discuss with employees the assessments of the latter’s Motivation, Capacity and Opportunity to do the job. And to do the job right. 4. Quantitative research with the help of questionnaires in which M, C and O are addressed by way of multiple items. So far, quantitative research is scant. Some dissertations used quantitative studies (Nabih, 2003; Nederstigt, 2011; Vermeulen, 2011). One international corporation explicitly used the Triad approach in a large scale (100,000-plus respondents) survey in nine different countries to successfully assess future customer behavior with regard to a new mobile service. The results also made it possible to establish the required marketing policy. (The results are not available for reasons of confidentiality). The trade-off between the four methods involves an assessment of the need for precision, the amount of time available for making assessments, the

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presence of experts, the availability of a target person/group and the financial budget. The first two methods are the most popular, but are rather quick and dirty. These do not directly involve the target person or target group, and therefore involve (extra) reliability and validity issues. These issues can be addressed more carefully in methods 3 and 4. When assessing the values of the three determinants, first impressions may sometimes suffice. If an assessor is familiar with a target person or group, s/he often has a good idea of the level of Motivation, Capacity and Opportunity relating to the criterion behavior. In other cases, the assessor(s) may want to deal with these determinants more prudently and systematically. It is then helpful to distinguish them on the basis of their rigidity/flexibility. There are constraints, stable determinants and flexible determinants (in fact these three represent a continuum ranging from strict rigidity to ultimate flexibility). Rigid determinants or constraints do not change, or do not change easily. The geographic characteristics of the environment present an example. The conditions under which marathons are run in New York and in Crater Lake (Oregon) are quite different and will stay different. A person’s general fitness is a fairly stable Capacity characteristic, but may show some variability over time. By comparison, fatigue is likely to change relatively easily and within a relatively short period of time. The distinction applies to each of the Triad determinants. See Table 4 for an overview of examples if behavior X is mountain climbing. Table 4

Examples of constraints, stable determinants and flexible determinants for Motivation, Capacity and Opportunity (behavior: marathon running). Motivation

Capacity

Opportunity

Constraints

Physical Needs

Body height

Altitude; climate

Stable determinants

Ambition

Fitness

Route quality

Flexible determinants

Desire to win

Fatigue

Weather conditions

Σ

M

C

O

The different types of determinant may be considered in order to arrive at an estimated value per Triad determinant (M, C and O). This is a qualitative, not a quantitative procedure, intended to stimulate a broad inventory of possible determinants and to prevent one of the types being given a disproportionate amount of attention (positively or negatively). Note that this

The Triad model

does not amount to a plea for fragmentation. The model itself will make no distinction other than that between Motivation, Capacity and Opportunity (with one exception, to be discussed later). So the analysis will include only the summary values M, C and O. The only reason for the distinction between rigid, stable and flexible determinants is to make careful and structured assessments of the relevant Triad values. Triad model basics It is remarkable that in behavior analyses, both by laypersons and in many if not most academic behavior studies, the focus tends to be on only one of the factors (motivation, capacity or opportunity). By comparison: we are likely to think it is foolish when a person asks us to predict the movement of an object purely on the basis of information on the exerted force alone. We would also require information about its shape and the circumstances. Without the additional information we would not be willing to give an answer. And if we were to be persuaded to give an answer anyway, it would qualify as a mere wild guess. In contrast, when it comes to human behavior, we seem to be remarkably confident about explaining it on the basis of information on one of the three determinants alone. A possible reason is that such information is enough to create the illusion that we can project ourselves on another person. And as we feel that we can explain our own behavior, we assume that we can also explain that other person’s behavior. Motivation researchers tend to set up their studies in such a way that limitations in terms of capacity and opportunity cannot play a role as such limitations might compromise their analysis. So in innumerable studies motivation can do its work under optimal conditions and provide the relevant data. And guess what: over the years, research has shown that, indeed, motivation is an important variable for the explanation of behavior. Likewise, in capacity or ability oriented studies researchers take care that their participants are motivated and can perform under optimal conditions (after all, if you want to measure a person’s mental capacity/ability, do not play hard rock in the background). Opportunity research is scant, but probably the studies are set up carefully so that participants do not lack the relevant motivation and capacity to show the relevant behavior. As discussed earlier, analytical gaps in academia are easily and comfortably filled by means of the ceteris paribus clause. This clause causes researchers

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to fall into the trap of overly emphasizing one of the three general behavior determinants, thereby ignoring the other two. Experience shows too that in many fields of application practitioners rely excessively on motivation and understate capacity and often even ignore opportunity when trying to explain, predict or influence behavior. This is in line with the so-called fundamental attribution error stating that ‘people are often quick to draw conclusions about the attitudes and personalities of others – even when plausible external or situational causes for behavior exist’ (Tetlock, 1985, p. 227; Ross, 1977). If a policy effect turns out to be disappointing, policymakers disproportionally focus upon Segment 5 in Table 3. In practice, the three Triad concepts seem to take turns when it comes to a position on the public agenda. In Human Resource Management, for example, there was a period in which rewards, bonuses and incentives (Motivation) were highlighted as a means of boosting worker performance. In another period the working conditions (Opportunity) received disproportionate attention. And more recently the focus has been on competencies (Capacity). A similar carrousel of hypes can be observed in educational systems. Policymakers do not seem to realize that competencies are irrelevant in the absence of motivation and opportunity. The argument may be extended to the other two central concepts. P19 Motivation, Capacity and Opportunity are assumed to be equally important for the explanation of behavior. There is no a priori reason why there should be a difference. After all, the absence of any one of the three can annihilate the impact of the others, whatever their value. The three determinants can be considered a necessary condition and not just a sufficient condition. Each represents a conditio sine qua non. This proposition seems counter-intuitive: lay persons, professionals and academics are inclined to view motivation as a relatively important factor, probably because this is the factor that they can relate to most easily (is closest to the self, see earlier). However, let’s compare the motion of a golf ball again. The form of the ball and the height of the grass are no less important than the force exerted on the ball. The factors work together on the basis of a similar status. We cannot suggest that the form of the ball is more important than the impact of the club or the resistance presented by

The Triad model

the grass of the fairway. All three aspects are critical on the same spot and at the same time. In another random example, that of driving a car, we do not ask ourselves about the relative importance of three determinants: the engine, the wheels and the road to drive on. Wheels and a road alone are not going to do the job. An engine and wheels are helpful, but without a regular road, reaching some distant location may prove to be highly problematic due to farmland, bush country and woods. The combination of wheels and a good road sounds promising, but the journey is unlikely to make any progress without the availability of an engine as the third condition. Similarly, without a pole, a pole fault jumper does not make it over the bar at six meters, whatever his motivation and whatever the opportunity. (We do not need a formal behavior model to make predictions about the outcome in the latter example. Here logic and intuition are more than sufficient to come to a conclusion. But the fact that the outcome is perfectly clear if the value of one of the determinants is a straight zero does not permit us to be careless with regard to other possible combinations of Triad values). Even though the Triad model assumes the three determinants to be equally important, we will see later that the three factors have different functions in the interplay that may take place between them (in fact, it will be argued that motivation is as important as the other two, but plays a different, pivotal role). P20 Several types of relationship can be suggested between the Triad determinants and behavior, and among the determinants. If we make a comparison with the MOA/AMO literature, the dominant approach is to assume a direct effect of the three determinants on behavior (e.g. Applebaum et al., 2000; Ramaswami et al., 1998; Strader and Hendrickson, 1999). See Figure 12.

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Motivation

Ability

Behavior

Opportunity

Figure 12

A MAO/MOA model which assumes a direct effect of each of the three determinants on behavior.

Hughes (2007), like MacInnis and Jaworski (1989), proposes the AMO (Ability-Motivation-Opportunity) model in which he suggests that Motivation has a direct influence on behavior and that Ability and Opportunity serve as moderator variables. See Figure 13.

Ability

Motivation

Behavior

Opportunity

Figure 13

Hughes’ (2007, p. 2) AMO-model.

This approach views Motivation as the primary determinant: ‘Ability and Opportunity are not direct causes of Behavior (…)’ (Hughes, 2007, p. 5). Hughes’ model does not address the possibility of relationships between Motivation and Ability, Motivation and Opportunity, and Ability and Opportunity. We will discuss later why this is a serious limitation. The model assumes a particular sequence in which the determinants play their

The Triad model

role. Motivation initiates it, to be followed by Ability and Opportunity. So a person first becomes motivated to engage in a particular behavior and notices along the way that moderators stimulate or hinder the effect of Motivation on Behavior. Two objections may be raised against this rather mechanistic interpretation. First, the model seems to apply to very simple organisms only: they are motivated to satisfy a physical need, so they initiate and continue the necessary behavior to the extent that the two moderators allow this to happen. (Hughes’ model seems to imply that people are often frustrated). It may be argued, however, that people may not only experience the impact of moderators after they become motivated, but may also anticipate this impact before motivation sets in. A person might be very motivated to buy a Ferrari, but knows beforehand that financial constraints will prevent him from doing so. Motivation makes no sense here and will not develop. To include anticipation, Hughes’ (2007) model can be extended to the model in Figure 14.

Anticipated ability

Ability

Motivation

Anticipated opportunity

Figure 14

Behavior

Opportunity

Extended Hughes’ (2007) AMO-model, including anticipation of Ability and Opportunity.

The second, related objection to Hughes’ model is that the three determinants are assumed to be independent and occur sequentially rather than simultaneously. Instead, it may be argued that Motivation may be capable of influencing Ability and/or Opportunity and that under particular conditions the reverse may even take place. We will discuss these possibilities later. It is the intricate interplay between goal and resources that may or may not produce behavior. In order to accommodate this interplay, Figure 14 may be adapted as follows in Figure 15:

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Motivation

Ability

Behavior

Opportunity

Figure 15

A MOA/AMO-model which assumes a direct effect of each of the three determinants on behavior and possible relationships among these determinants.

The critical question regards the nature of the model. What is the mechanism that produces behavior? Ultimately, this question can only be answered empirically but if empirical evidence is gathered prematurely – before a full theoretical analysis – it may produce partial and biased information and thus contribute to confusion. For this reason, at this point, theory development is given priority over empirical research. Several model possibilities are discussed below: the additive model, the multiplicative model and a limitation focused model referred to as the ‘Constraining-Factor Model’ (Siemsen et al., 2008). An additive model (B = f(M+C+O)) supposes, for example, that a very low value of Motivation may be compensated by a very high value of Opportunity or Capacity. However, this does not seem to hold up as a general rule. If a person has very little motivation to ruin the garden furniture of the neighbors, the likelihood of this happening does not increase by providing that person with more time to do so. Several authors adopt the notion that the model is multiplicative in nature (see, for example, Knies, 2012; Bell and Kozlowski, 2002; Poiesz, 1999; Cummings and Schwab, 1973). In this case, behavior (B) is taken as the product of the scores that may be attached to motivation (M), ability (A) or Capacity (C) and opportunity (O). In its simplest form: B = M x A/C x O (A/C stands for A or C)

The Triad model

(Note, parenthetically, that in mathematical terms the model to be tested would be presented as: B = w0 + w1M + w2O + w3A + w4MxO + w5MxA + w6OxA + w7MxOxA where w stands for weight. In this equation, the latter term would be the focus of interest. The other elements are to rule out that this term would incorrectly claim variance that can be attributed to the main effects of the separate determinants and/ or to their first-order interactions). This may be visualized with the help of Figure 16, where the centre is our focal point. MxC Motivation

Capacity

MxCxO

MxO

CxO

Opportunity

Figure 16

Main effects and first- and second-order interaction effects of the three determinants.

It is not clear however whether the multiplicative model explains more variance than the additive model (see Cummings and Schwab, 1973), although Bell and Kozlowski (2002) view the multiplicative model as a ‘truism’ (p. 497). Siemsen et al. (2008) note that the multiplicative nature has not been established rigorously in empirical research and summarize the literature as follows: ‘(…) while there is some theoretical evidence to suggest that motivation, opportunity and ability are complementary in driving behavior, existing empirical evidence from work-performance theories suggests that little

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explanatory power is to be gained by adding interaction terms’ (p. 431). They claim, in other words, that the interaction does not really contribute to the explanation of behavior relative to the other elements in the equation. For this reason they suggest an adapted version of the multiplicative model which they refer to as the ‘Constraining-Factor Model’ or ‘CFM’. (A comparison may be drawn with Goldratt’s theory of constraints (1999)). Metaphorically, Siemsen et al. (2008) take their model as a bottleneck model, meaning that ‘it is the minimum among the three factors (…) that ultimately determines behavior’ (p. 431). They show that the bottleneck interpretation explains approximately six per cent more variance than the traditional multiplicative model, which amounts to an improvement of about 10 per cent. However, before we take this result too literally, some additional observations need to be made. Each of the issues concerned will be discussed in more detail later when we try to construct a more complete behavior theory. In random order: • The CFM differs only in degree from the multiplicative model. In the multiplicative model the determinants with the lowest value clearly also have a constraining effect. This effect becomes even stronger if a value is reduced. A value of 0 (zero) precludes the behavior altogether. In the case of a very low value for either one of the determinants, no difference can be expected between the multiplicative model and the CFM on mathematical grounds. • The CFM is limited with regard to the knowledge structure: it pays attention only to the three determinants and does not address the possibility of dynamic relationships among these determinants. It is a snapshot, not a movie. At a specific moment in time the minimum value may be the only relevant value. However, if there is a possibility of the minimum value being influenced by the other values, the explanatory potential of this value may be limited. The two limitations seem to point to potentially serious omissions. Siemsen et al. (2008), in a reference to Blumberg and Pringle (1982), indicate that they conceptualize the three determinants as ‘correlated but distinct constructs’. But they also state that ‘the precise direction of all causal relationships among MOA is difficult to justify theoretically’ (p. 429). If the interrelationships of the elements are unclear it seems premature and unwarranted to present a conclusion on the nature of the difference between the multiplicative model and their CFM. • A possible model is one where Capacity and Opportunity form mutually complementary determinants. The combination then has a multiplicative relationship with Motivation. This hybrid model can be expressed as B =

The Triad model

f(M x (C+O)) and is similar to the Motivation x Ability model of Petty and Cacioppo (1986) discussed earlier. However, such a model would only be feasible in the rare case where Capacity and Opportunity are perfectly complementary and dynamic effects among the three determinants do not take place. The model is not considered feasible here as it suggests that C does play a role if O is absent (zero value) and vice versa. If the model applies at all, it may be for magicians only. (Of course other models with differentiating determinant weights and nonlinear relationships might in principle be considered. But at this point there is no theoretical reason to do so). P21 Taking the various arguments into consideration, the Triad model assumes a multiplicative relationship between the three factors: Behavior = f (Motivation x Capacity x Opportunity) ‘Behavior’ stands for behavior likelihood if there is one behavior option. P22 It is important that the person experiences a synthesis: the Triad values are perceived to combine in the same place and at the same time. The experience may or may not be conscious. If it is and the Triad values for behavior X are all high, the person experiences a state that in popular language is referred to as ‘flow’. P23 For a combination of reasons, the set of determinants in the Triad model may be visualized as a cube: the three Triad factors are conceptually independent, they refer to conditions that take place simultaneously (they should be judged integrally), the values may be represented as scores on the three continua and the three factors are assumed to be equally important, implying dimensions of equal length. The scores may be plotted on the scales/dimensions that represent the determinants. Again, these range from 0.0 (lowest) to 1.0 (highest). Visualization of Triad conditions would involve drawing a cube. As most people find this difficult and in the process of drawing tend to create all kinds of odd geometric and even non-geometric figures, the cube is reduced to a tetraeder dubbed ‘the Triad Figure’. In a scheme with three perpendic-

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ular axes the corresponding Triad values are connected, forming a volume. Parenthetically, the volume of this tetraeder is always exactly 1/6 of the volume of the corresponding cube. The larger the volume of the tetraeder, the larger the likelihood of behavior X (or other behavior aspects as we will see shortly). The tetraeder that is formed by connecting the maximum scores represents the full behavior potential at a particular moment. See Figure 17 for the Triad Figure. 1.0 M

0

C 1.0

O

1.0

Figure 17

The Triad Figure (the ‘maximum tetraeder’ in dotted lines and the smaller tetraeder formed by the scores M = 0.7; C = 0.7 and O = 0.7).

It is easy to see how the volume would change with changing values and that it would collapse to a two-dimensional space – no volume – if one of the values were 0. Although the figure can only be drawn in a two-dimensional plane it is to be interpreted as three-dimensional. In this plane, the lengths of the dimensions appear different but should be interpreted as being equal in length. Although the Triad model is a qualitative model in nature and avoids the suggestion of exactness by quantification, the Triad values can be multiplied to obtain a crude quantitative impression of the size of the Triad volume. The product of the three values is dubbed the ‘Triad score’. (Note that ‘Triad value’ stands for the M, C or O value). In the example of Figure 17, the Triad score is 0.7 x 0.7 x 0.7 = 0.34. Again, no exactness is implied.

The Triad model

For obvious reasons, the Triad Figure can only be presented in a static form although the volume may be imagined to be as alive as its owner. It is quite dynamic in that it changes all the time. The Triad volume ‘lives’ and in this sense provides a metaphorical representation of behavior. The theoretical propositions that follow later attempt to explain why volume changes take place and how. P24 Because of the previous proposition, there is no reason to accommodate negative values of Motivation, Capacity and Opportunity for the simple reason that a negative likelihood of behavior is not conceivable. As values of Motivation, Capacity and/or Opportunity reduce (implying also a reducing Triad volume), there is a growing likelihood of another behavior option taking over. P25 Behaviors differ with regard to the amount of resources that they require. Therefore, they also differ with regard to their maximum Triad volumes. For example, the maximum Triad volume, as expressed in the Triad Figure, is higher for running a company than for lighting a match. So Triad Figures may be drawn in different sizes. P26 As long as the total stock of resources is not exceeded, behaviors or Triad volumes may be combined, provided that both behaviors have a sufficient Triad score. People can drive a car and talk with passengers at the same time. When the stock is not sufficient to combine behaviors, a person can only alternate them. For example, watching a show on television and having a conversation with family members. It sometimes appears as if these behaviors can be engaged in simultaneously, but in fact they alternate within a short period of time (unless, of course, the conversation does not require many resources: ‘uh-uh’). P27 Obviously, people are not assumed to make actual calculations before engaging in a particular behavior. Neither can we assume the existence of some ‘homunculus’ doing the calculations for us. However, on the basis of nature and/or nurture, people may be assumed to somehow sense the favorableness of a behavior option in which they include assessments of their Motivation, Capacity and Opportunity for a particular behavior. Survival may even be dependent upon quick, intuitive assessments of goal attainment possibilities in

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the context of resource scarcity and situational constraints. (Also Darwins’ (1859) notion of the survival of the fittest may be taken to apply to the species, organism or person that most adequately and promptly reacts to changes in environmental conditions). Because people, both as a species and as individual persons, accumulate extensive experience with balancing goals, resources and contextual conditions over time, it is very likely that many if not most behaviors take place without a deliberate assessment of the three determinants and their relationships. However, this does not prevent us from using the Triad metaphor – the volume – as a simplified approximation of what goes on in the mind just prior to engaging in behavior X. P28 So the Triad model and its values are to be interpreted in a qualitative, not in a strictly quantitative way. The first reason is presented above: we cannot assume people to calculate their behaviour. The second is that quantification may suggest a higher level of precision than the model can justify. If M, C and O values are 0.80, 0.60, and 0.50 respectively, the resultant Triad score is 0.24. The conclusion that this combination is in the lower range (and considerably lower than the average of the three values suggests) is more relevant than its exact value. It is even doubtful whether the second decimal figure should be given any meaning at all. The third, important reason is that a combined score does not provide information as to how the score has been established. A Triad score of 0.24 can be obtained in several different ways. For example, M, C and O values of 0.50, 0.60 and 0.80 have the same outcome as the earlier example. But so does a combination of 0.80, 0.30 and 1.0. Knowing which values belong to what determinants is important for theoretical reasons and for practical objectives as well: what values should be focused on for designing policy measures? The Triad score should only be used to give a crude indication of the behavior likelihood and other aspects to be discussed later. Practical experience reveals that there is a preference to work with Triad Figures instead of with numerical Triad values and scores. This is in line with the emphasis on a qualitative rather than quantitative interpretation. P29 The Triad model applies to all behaviors, whether ‘large’ or ‘small’. So at all behavior levels, we find the same set of generic determi-

The Triad model

nants. In that sense, the set of behavior determinants reminds us of a fractal: a specific pattern that is repeated at each level of the phenomenon. At the level of large scale behaviors, the pattern is the same as when the behavior is chopped up into small portions. P30 If behavior is viewed as a chain of interconnected activities on a time line, the Triad reasoning may be applied to each of the consecutive activities. The Triad result of the previous activity sets the stage for the next activity. For example: ‘To study’ is a verb that is applicable to each of the consecutive years at a university. At the start of the first year, her Motivation, Capacity and Opportunity to study are all sky-high. In the course of the first year, the M and C values remain high, but the O value drops due to various extracurricular activities. This is manifested in disappointing exam grades. This means that she starts the second year with a handicap: a relatively low level of Capacity. An unfavorable value in an early phase of behavior X may affect its likelihood/quality in later phases. Triad analyses may show where the most important barriers are to be found. P31 If there is just one behavior option, the Triad volume stands for behavior likelihood. If there are more options, the behavior with the largest volume ‘wins’. In that case the likelihood of a behavior is determined by its relative volume position. From an economic perspective, the Triad volume may be interpreted as the value of a behavior option. It combines the benefits or outcome (M) and the costs dictated by the combination of personal expenditures (C) and situational resource limitations (O). A higher Triad volume is more attractive than a lower volume and will be preferred if there is the possibility of a choice. The comparison may be expressed in numerical terms. Let’s assume that there are two behavior options. One option has the Triad score of 0.17 and the other option has a score of 0.25. Then the latter is selected, even though the Triad score itself is not high. For an example, we take ‘going on a vacation’ as the criterion behavior. The target person has gone to the same destination for 20 years running and now contemplates two options: to go to the same place again (option 1) or go to another destination (option 2). The M, C and O values of option 1 are 0.7, 1.0 and 1.0 respectively. The values of option 2 are 0.9, 0.8 and 0.8.

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The difference between the Triad scores, which are 0.7 and 0.58, will make the person return to the same destination as in the previous 20 years – even though he actually feels more attracted to the new location. The proposition suggests that people may select the behavior with the most favorable combination of conditions and that the option with the highest Motivation is not necessarily selected. This may explain why many people stick to familiar behavior for a disproportionate amount of time, and why some people continue to do the same work for the same employer even though they are eager to change jobs. A particular behavior option may look different at different moments in time. For example, the costs (C and O) may become more apparent when the goal gets closer. This refers to the classical approach-avoidance conflict (Boyd et al., 2011; Miller, 1944), which suggests that the approach component dominates at a longer period before the goal and that when the goal comes closer in time the avoidance component gains importance. Long before his departure, a person is looking forward to his vacation; a few days before departure he would rather stay home. P32 The multiplicative nature of the model suggests that an increase of a low Triad value is more effective than an increase of a high Triad value even if the amount of change is the same. See also Quinn et al. (2002) who state that the reduction of limiting powers to change is more effective than the strengthening of driving forces. P33 The very switch to another behavior may imply a Motivational and Capacity and/or Opportunity costs. These are subtracted from the volume of the alternative behavior. This implies that if the Triad volumes of the present behaviour and an alternative behaviour are the same, the person is most likely to stick to the former. P34 In principle, ‘to make a Triad assessment’ is a behavior itself, and for that reason is subject to Triad reasoning. This proposition more generally implies that under particular conditions (low Motivation, Capacity and/or Opportunity to decide about behavior) a person may not be able to make an adequate Triad assessment. For example, a person who is not really interested in behavior assessments, who feels incapable of making behavior assessments and who is in a hurry, is unlikely to apply the model.

The Triad model

Making an assessment of Motivation, Capacity and Opportunity tends to be easier for familiar persons engaging in familiar, simple and routine behaviors than for unknown people who engage in new and complex behaviors. Because making Triad assessments require scarce resources, it may be assumed that the number of behavior options that can be compared simultaneously tends to be very limited (note that also ‘making a comparison of behavior options’ is subject to a Triad interpretation). The assumption seems warrented that people compare a maximum of two or three options at any one time – that is to say, without the support of decision tools. The latter would increase the C-value. The comparison suffers if the person is indifferent, has a low C-value and there is limited time to think things over. Before leaving home by car to go to the pub, Bert tells his wife that he will take a taxi home if necessary. Three hours and nine beers later he feels ready to leave. He has two options: to drive his own car or to take a taxi. The consumed alcohol seriously reduces his Capacity to make a comparison between the alternative behaviors. He drives home. P35 When the value of one or more dimensions is fixed at a low level, the only way to generate some additional volume is by raising the value(s) of the other determinant(s), preferably both. Such compensation measures only work under two conditions: 1. The value to be compensated is not very low (as in that case the volume will be negligible whatever the magnitude of the compensation) and the value of the third determinant is sufficiently high as well. For example, an inexperienced apprentice is given a task. Because of his lack of experience (Capacity) he is given extra time to finish it (Opportunity). However, such a compensatory measure only works (only raises the volume sufficiently) under two conditions: the Capacity value to be compensated is not extremely low and the apprentice’s Motivation is sufficiently high. Again, it appears that in reality, compensation measures disproportionally focus on one determinant rather than two, let alone three, which may reduce their effectiveness unnecessarily. P36 As a particular measure may relate to more than one Triad determinant, it is necessary to allow for parallel and conflicting effects. A message, for example, may have an effect upon both Motivation and

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Capacity to process information. In the ideal case, the two effects run parallel and positively, but it is conceivable that there are opposite effects. For example, a strong increase of the M value may be compensated by a fall in the C value. A sender notes that his message does not have the intended effect. The people who receive it do not like it very much. So it is decided to include an element of humor. Humor in a message may increase the Motivation to process the message. However, at the same time, it may reduce the Capacity to process it as attention is disproportionally attracted to the funny element. See Figures 18a and b, which show that, in this particular case, the net effect is even negative. a

b

M

M

C

O

Figure 18

C

O

An example of an unintended effect of humor. Figure 18a presents the Triad conditions for message processing before humor is included. Figure 18b shows the effects thereafter (higher M; lower C). It is assumed that there is no Opportunity problem in examples a and b.

Other examples may be shown where an increase of Opportunity or Capacity leads to a reduction of Motivation. If a person is informed by his superior that the deadline for the completion of his task has been postponed indefinitely, he may actually receive two messages: (1) Take as much time as you want (maximum Opportunity); (2) We do not care about the outcome. The second message is likely to have a negative effect on Motivation and may conflict with the first.

The Triad model

In a last example, students are repeatedly instructed about how they should study to prepare for an exam. Their Capacity increases each time (although with a diminishing net effect) and with each instruction their Motivation decreases. After a particular number of repetitions, the negative effect dominates the positive effect. These examples show that it is very risky to focus upon one of the Triad determinants only. At risk of affecting the motivation of readers to read on, it must once again be stressed that Triad determinants need to be considered integrally, not separately, and not separated over time and space. To realize a desired behavior, its Triad volume should be as high as possible and higher than competing behaviors. Conversely, the undesired behavior may be restricted by lowering the Triad volume. Sometimes, policy measures need to be directed at both the undesired behavior and the desired alternative behavior. In practice we see policymakers experiment, unknowingly, with the different Triad values, resulting in much trial and error. An attempt is being made to reduce the speed of car traffic in a family neighborhood. As many (most?) motorists enjoy speed (and dislike ‘snails’ in front of them) a spontaneous Motivation to slow down is unlikely. The Capacity to do so is also limited: drivers are often unaware of how fast they are going. The Opportunity to reduce speed is limited by the lack of time. The policymakers first place dynamic electronic signs in the streets that signify the speed of each car. These increase Capacity somewhat but leave Motivation and Opportunity unaffected. The measure is unsuccessful. The next step is to ticket speeding motorists: this should motivate them to slow down. However, few are caught and as the tickets arrive weeks later, disconnecting cause and effect, these do not work either. As policymakers do not give up easily, they proceed by laying concrete thresholds in the road that will damage a car at higher speeds. The motorists do not concede either, speed right up to each threshold, jump it, and are on their way again, accelerating firmly. Eventually, the policymakers throw in the towel and advise the parents to watch their kids. Several things seem to go wrong here. First, the measures are taken sequentially. So the effect of one measure has subsided by the time the next measure is implemented. Second, the tickets do not work because the time lapse does not allow motorists to associate them with the act (one of the propositions claims that a person should experience a synthesis between the three

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values). Third, it does not help to ask people to slow down once they are in a hurry. It is more effective if the measures are combined (synergy) and the motorists are shown how they can increase their Opportunity (for example by asking them to leave five minutes earlier next day). Similarly, but in a more positive example, charity campaigns are successful when they manage to interest people, to show what their contribution would mean, and how they can contribute, all at one particular moment in time and in the particular situation that the request to donate is made. Only then does the combination of three high values become possible. Sometimes it may be impossible to bring about a change in behavior by focusing on behavior X only. In that case, both behavior X and the alternative behavior should be the target behaviors. In the case of criminal behavior, for example, it may not be possible to reduce the Triad values sufficiently to obtain a switch to a more constructive behavior. In that case, additional measures should be taken to increase the values of the alternative behavior. The combination of the reduction of one Triad volume and the increase of the other volume should tip the balance. Now that the basics of the model have been identified, it is relevant to assess what behaviors and what behavior aspects it may be applied to. Behavior and behavior aspects P37 Usually behavior (behavior X) is not a goal in itself. Most behaviors are instrumental in reaching a particular outcome. Outcome refers to the particular result that the person him/herself, a policymaker or a manager hopes to achieve, the result to which behavior X should lead. Examples are the number of products sold, the turnover, the number of clicks on a website, financial outcomes, the number of votes, the number of diplomas, etc. Although outcomes will not be the focus of our interest (as behavior is the critical dependent variable) they help in the identification of relevant behavior aspects. One of these aspects, behavior likelihood, has already been mentioned. It relates to new behavior. Four other aspects relate to ongoing behavior. These are behavior intensity, effectiveness, efficiency and persistence. The Triad approach can be applied indiscriminately to each of these aspects.

The Triad model

Thus the notion of Triad volume is important for each of the aspects. See Figure 19. M Behavior Likelihood Intensity Effectiveness C O

Figure 19

Behavior outcome

Efficiency Persistence

A behavior model including the Triad, behavior aspects and outcome.

P38 Behavior likelihood (BL) can be expressed in a figure between 0.0 and 1.0, where 0.0 represents the absence of likelihood and 1.0 represents certainty of occurrence (if one option is considered). Behavior likelihood refers to behavior that has not yet taken place but may be shown in the near future. Behavior intensity (BI) refers to the amount of activity that is involved in the display of the behavior. A high Triad volume is manifested in energetic, dynamic and vibrant behavior. Lethargy, associated with an uninspired, depressed attitude reflects a low volume. That is, if this is the volume of the dominant behavior. Note again, that a low volume behavior is likely to be discarded in favour of a behavior with a higher volume. Behavior effectiveness (BE) is the extent to which the behavior leads to the required outcome, expressed in the degree to which the goal is achieved. A person who is highly motivated, has a high level of Capacity and a high value for Opportunity is assumed to be highly effective. Note that a lack of behavior effectiveness may mean two things: either the volume is low or there is no optimal match between the behavior and the outcome concerned.

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Behavior efficiency (BY): the costs at which the desired outcome is achieved, expressed in actual costs relating to budget. A low volume, however created, results in errors, delays, mistakes, lack of concentration, distraction, et cetera. Behavior persistence (BP) concerns commitment or the duration of the behavior. It can be viewed as the continuation of behavior likelihood. For repetitive or continuous behavior it is important to consider behavior persistence to be able to refer to high quality behavior. A pianist may be very effective and efficient at playing the first note in a Chopin concerto, but if his performance then ceases, his behavior is not deemed to have high quality. At least some continuity or persistence may be required. When no objective norm for persistence is available, expectation may be applied as a standard. Behavior persistence is functional until the intended outcome is achieved. A habit is a manifestation of behavior persistence. Habitual behavior only exists and continues when its Triad volume is high. In the case of habitual or routine behavior, the ‘automatic pilot’ takes over, meaning that less resources (particularly Capacity and Opportunity) are required for behavior to continue. Stated differently, the Triad conditions of routine behavior are favorable relative to the ones of the same behavior when new. This may be compared with a car that, while moving, is shifted to a higher gear. It maintains the same speed but can do so with less energy. When a person learns how to drive a car, this behavior involves a combination of different simultaneous and consecutive actions that all require high Triad volumes. There is little or no room for alternative, competing behaviors like talking over the mobile phone. When driving becomes more routine (when it becomes simpler), it ‘shifts to a higher gear’. The different behaviors merge into one behavior which requires a relatively smaller amount of resources to continue. More resources are available for other behaviors. This explains why people can drive, monitor traffic, talk and check the highway signs all at the same time. Taken together, behavior effectiveness, efficiency and persistence may be taken to represent behavior quality (BQ). The five behavior aspects are hierarchically ranked (see Figure 20):

The Triad model

Behavior Outcome

Behavior Effectivens

Behavior Efficiency

Behavior Intensity

Behavior Persistence

Behavior Likelihood

Figure 20

A hierarchy of behavior aspects.

P39 The behavior aspects are conceptually independent. High behavior likelihood does not imply high effectiveness or high efficiency. Behavior may be highly effective, but at the same time also highly inefficient. Effectiveness and efficiency do not guarantee behavior persistence. High behavior intensity may or may not be associated with effectiveness, efficiency or persistence. P40 The behavior aspects can be interpreted subjectively and/or objectively. The Triad approach assumes that they are first interpreted in a subjective sense. For example, a person may see himself as highly effective, until objective feedback requires him to think otherwise. P41 Over the behavior aspects, the Triad score is a constant (unless spontaneous changes take place, see earlier). To explain this proposition, let’s start with an example. A person’s Triad score for a particular task is 0.32 (M = 0.40; C = 0.80 and O = 1.0). He is doing something else (which has a higher score). His supervisor orders the person to switch to the unpleasant task and finish it. The Triad score for

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behaviour likelihood is thus forced from level to level 1.0. The original Triad score of 0.32 cannot apply any longer to behavior likelihood, so it shifts to another behavior aspect. For example, it is applied to behavior effectiveness, meaning that it suffers: the task is executed haphazardly. Similarly, if effectiveness is also pre-set at level 1.0 (the supervisor not only demands that the task should be finished but adds that it must reach a certain quality level), the low score of 0.32 shifts again – now to behavior efficiency: the person finishes the task and delivers the demanded quality, but a lot goes wrong in the process. He drops and breaks things and he loses time. It is easy to see how behavior persistence would absorb the misery if high efficiency were also to be required. Throughout the process, the respective Triad scores have been forced to a high level, but the original and actual Triad score remains active in the background. For policymakers and managers, the distinction between the various aspects is important. It shows them why a measure may not be as effective as it seems at first sight. The examples may be interpreted negatively and positively. The negative interpretation: people need to be checked in terms of every behavior aspect, as they are likely to sabotage work if they don’t like it. The positive interpretation: it does not make sense to force people to do things for which they do not have sufficient Motivation, Capacity and Opportunity. If you want to have something done, create the right conditions for it. Or select the people who already have sufficiently high Triad values. In the following examples the various behavior aspects (likelihood, effectiveness, efficiency, etc.) will not be distinguished repeatedly. For the sake of simplicity we will only refer to behavior likelihood unless another aspect must necessarily be considered. P42 Sometimes a behavior outcome is pre-set. In that case the person has no choice, the outcome must be produced and the behavior has to adapt to the required result. And so do the Triad volume and the Triad conditions. Here the regular order is reversed: the Triad conditions do not form the conditions for the behavior, but favorable Triad conditions need to be generated for the required behavior. An overweight person has pledged to run three miles in 30 minutes every day for a month. It is a bet with friends. Socially and financially, losing is no option. At every single step of these three miles the alternative of quit-

The Triad model

ting competes with the option of continuing. However, the goal has been set and cannot be evaded. The only option is to try to think of the social pressure (M), to keep up spirits (M) (‘Think positive! Think positive!’), to ignore the pain (M), to control the breathing (C), and to cut corners wherever possible (O). P43 A person is in doubt when the Triad volumes of his/her behavior options are (approximately) equal. Note that options are equal when their volumes are; the respective M, C and O values of the two behaviors may differ. For example, behavior options A and B both have Triad values of 0.24. The respective values of option A are 0.4, 0.7 and 0.9, which produces a Triad score of 0.25. The values of option B are 0.9, 0.6 and 0.5. Here, the Triad score is 0.24. Even though he dislikes the present work, he feels quite capable of doing it and the opportunity (time/location) is near perfect. He judges the alternative work to be much more attractive, but Capacity and Opportunity are perceived as far less favorable. (The small difference between the Triad volumes in this example is diminished by the anticipated switching costs. These negatively affect the volume of the alternative). The size of the Triad volumes determines the level of doubt. The higher the volumes, the higher the experienced doubt. A brief intermezzo So far no empirical backup has been presented for the various propositions, which is unacceptable in conventional psychology. It is considered not done to ‘speculate along’ without providing supporting empirical evidence for each subsequent theoretical step. Also, the references made to the existing literature are very limited in number. Several reasons can be mentioned: First the decision was taken earlier to develop a more elaborate theoretical foundation before collecting data. It would be contradictory to refer to publications that generated findings in the absence of an overall theoretical framework. Second, it seems safe to assume that, among the myriads of publications, a supporting publication can be found for almost every proposition made or to be made. It might possibly be necessary to search in obscure corners of unknown outposts of the discipline, but somewhere, somehow, corroborating articles would be located. Of course, such referen-

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ces would merely provide the illusion of validity. The third reason is that on the basis of an argument that is yet to be discussed, the external validity of a considerable number of findings reported in the literature is questionable. Finally, it does not seem fair to criticize the psychological literature and use it nonetheless for opportunistic reasons. So let’s continue on our chosen course. Affect, satisfaction and wellbeing Affect, the summary term for all types of feelings that people may experience, receives a lot of attention in the academic world. Affect comprises many different psychological concepts like satisfaction, wellbeing, subjective wellbeing, psychological wellbeing, happiness and mood. To some extent, their meanings overlap. The concepts become more important, as the interest of policymakers in outcomes (relative to mere input, throughput or output) is increasing. Outcome refers to the ultimate goal that a policymaker wants to achieve. Often, this goal is to provide some contribution to human wellbeing. In practice, affect can provide important information on the quality of products and services. Customer and employee satisfaction surveys are feedback instruments frequently used by companies, non-profit organizations and governmental institutions. The feedback on how people feel and evaluate gives direction to production and service optimization processes. Apart from this particular function, affect, satisfaction and wellbeing are important topics in their own right. The behavior aspects discussed earlier and behavior-related affect form two sides of the same coin. Note that ‘satisfaction’ is reserved here for positive affect with regard to behavior X, not with regard to an object. P44 Positive outcomes create positive feelings or affect. Over time and through feedback, these become associated with behavior quality and eventually with high Triad volumes. A person learns to appreciate high volumes and to dislike low volumes. It is assumed that there is a positive relationship between volume and affect. Affect may be implicit or explicit. If explicit, a person indicates to be satisfied with the state s/he is in. A high Triad volume is experienced positively for two reasons. The first is that there is a high likelihood of success of behavior X; the second reason is presented by the harmonious, balanced relationship between goal, resour-

The Triad model

ces and context. A low volume implies that the chances of success are slim, that the behavior is short-lived: it is likely to be replaced by an alternative. Also, a low volume may mean that the concerning person perceives the goal, the resources and the circumstances of a particular behaviour to be out of sync. The tension between the three Triad values causes feelings of discomfort or stress. P45 Not only a high volume static state may be assumed to be associated with positive affect. The same applies to dynamic effects that imply volume increases. A person will experience satisfaction if a low Triad volume for an important behaviour changes positively. Growth is associated with positive affect. P46 Triad value differences are associated with stress and discomfort. Larger differences imply more negative affect. A reduction of these differences produces an alleviation of negative affect (relief). Because of the relationship between value differences and volumes, this proposition is closely related to the previous one. P47 Within a particular period, the combination of behaviors may be represented by the verb ‘to live’. If the Triad volumes of the behaviors that are judged as most important are high, the positive affect associated with each of them adds up to the experience of wellbeing. Stated differently: wellbeing is the Triad volume of the verb ‘to live’. It stands for the combined satisfaction of important behaviors like, for example, caring for others, working, producing or creating (new ideas, art) and engaging in leisure activities. Of course, important behaviors are subjectively determined. The academic literature does not present a unanimously accepted definition of wellbeing, in spite of the literally millions (!) of published studies on wellbeing and directly related topics such as psychological wellbeing, life satisfaction and quality of life. Much research is correlational in nature, which complicates the identification of the direction of causality (e.g. does the positive relationship between life satisfaction and optimism (Pinquart et al., 2004) mean that people who are more satisfied with life develop feelings of optimism, is the relationship the other way around, or is the relationship recursive? According to Ryan and Deci (2000), wellbeing is associated with functioning in a positive way as expressed by curiosity, vitality, being intrinsically motivated, being inspired and eager to learn, personal growth,

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developing new skills, accepting responsibilities, meeting challenges and showing exploratory behavior. ‘Illbeing’, on the other hand, involves refusing responsibility, apathy, alienation and irresponsible behavior (Ryan and Deci, 2000). Huppert (2009) notes that happier people tend to be more productive (or are productive people happier?). The various findings show that the relationship between behavior and wellbeing is not a simple one and is probably best interpreted as circular. Dolan et al. (2008) conclude on the basis of an extensive literature review on wellbeing: ‘One very firm conclusion that can be drawn for our review is that the existing evidence base is not quite as strong as some people may have suggested.’ (p. 112). The concept of wellbeing and its equivalents may possibly also suffer from the absence of an overall, integrative theoretical framework of behavior. The same may be said for the field of satisfaction and dissatisfaction. Both areas of research are relatively isolated. The absence of a general theory not only prevents a clear interpretation of findings reported in the literature, but also causes wellbeing research to go on forever without making substantial progress. Indirectly, the literature seems however to support the notion that wellbeing is associated with having personal goals (Kasser and Ryan, 1996), self-efficacy (Pinquart et al., 2004) and background variables (Argyle, 1999). These notions show some correspondence with the three Triad variables. The ‘Flourishing Scale’ – a recent scale for psychological wellbeing (see Diener et al., 2010) – also contains items that may prudently be interpreted as having a relationship with Motivation, Capacity and Opportunity aspects. Several authors suggest that wellbeing is more strongly related to intrinsic factors than to extrinsic factors. For example, Kasser and Ryan (1996) show that specifically intrinsically (as opposed to extrinsically) determined goals have a positive influence on wellbeing. In the same vein, Pinquart et al. (2004) point to a positive relationship between life satisfaction and self-efficacy: the idea of having control over one’s own life. These findings suggest that it may be relevant to take a closer look at the distinction between intrinsic and extrinsic determinants of behavior. Intrinsic and extrinsic aspects Motivation, Capacity and Opportunity are not only determined by the person him/herself. To some extent, their values are determined by external or out-

The Triad model

side sources. This suggests that in order to understand volume changes, we may have to distinguish between person-related and context-related determinants per Triad determinant. More specifically, we will make a distinction between intrinsic and extrinsic Motivation, Capacity and Opportunity. This is not a departure from simplicity, nor does the distinction involve the multiplication of the three Triad determinants by two. We will continue to work with a model of three variables and not with six. However, the distinction will facilitate further theoretical elaboration. P48 Each of the three generic concepts Motivation, Capacity and Opportunity accommodates the basic distinction between intrinsically and extrinsically determined aspects. These may be denoted by the capital letter of the determinant concerned and a subscript ‘i’ or ‘e’, referring to intrinsic and extrinsic aspects, respectively (Mi, Me, Ci, etc.). A distinction frequently made in the literature is that between intrinsic and extrinsic motivation (see, for example, Bénabou and Tirole, 2003; Ryan and Deci, 2000). ‘(…) intrinsic motivation refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation refers to doing something because it leads to a separable outcome’ (p. 55). In other words, intrinsic motivation is related to the interaction of the person and the task; extrinsic motivation is related to the outcome of the task, not to the task itself. A person may be intrinsically motivated to take a long walk three times per week because she likes the exercise in itself; another person may walk only because of doctor’s orders. Here we define intrinsic motivation as a reason for behavior that is perceived by the person as innate, coming from him/herself. In the case of extrinsic motivation the reason for behavior is perceived to come from an outside source. Policy measures are by definition extrinsic in nature, but may be aimed at either extrinsic or intrinsic effects. Extrinsic effects are more superficial, volatile and short term. Intrinsic effects are more fundamental, stable and long term. A camera’s instruction manual is an extrinsic Capacity aspect. Education to professionalize photographic skills is an extrinsic measure aimed at raising intrinsic Capacity. Even though the distinction between the two types of motivation has received considerable attention in the literature, no such distinction has

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been made (as far as the present author is aware) for either Capacity (or Ability) and Opportunity. Yet it seems to make sense to do so. The distinction that comes closest in meaning to that between intrinsic and extrinsic Capacity is the distinction between internal versus external control (Rotter, 1966). Also the concept of self-efficacy (Bandura, 1977) explicitly refers to the notion of intrinsic control. Here we define intrinsic Capacity as the capacity that the person experiences as innate, as belonging to his/her own person, while extrinsic Capacity is the capacity that is perceived to be provided by an external source beyond one’s control. Examples are unrequested help that is provided by others, information that is presented, equipment that is made available, etc. Also the distinction between intrinsic and extrinsic Opportunity has, to the knowledge of the present author, not been made in the literature even though the potential to do so seems theoretically intriguing. In line with the other two distinctions, the present distinction also relates to internally and externally perceived causes. Intrinsic Opportunity is the time, the space and the context that can be claimed and shaped by a person him/herself; extrinsic Opportunity refers to given, externally determined conditions beyond a person’s control. An example of intrinsic Opportunity is the time that a person reserves for travel to a nearby city; traffic jams and weather conditions are examples of extrinsic Opportunity. Extrinsic aspects may serve to complement intrinsic aspects and vice versa. It is important to note that (limitations of) extrinsic aspects may overrule intrinsic aspects. Table 5 provides an overview of examples of the distinctions for a random behavior example: bicycle riding. Table 5

Examples of intrinsic and extrinsic Triad factors. Intrinsic

Extrinsic

Liking the activity

Recognition by others

Capacity

Physical condition

Quality of the material

Opportunity

Self-selected road

Wind direction & velocity

Motivation

Intrinsic aspects represent the personal budgets for Motivation, Capacity and Opportunity of behavior X. They tend to be more stable than extrinsic aspects, particularly when they are associated with personal characteristics that have developed over time like personal values, personality traits, experience, skills and knowledge. (Feeling hungry or feeling tired are examples of short term intrinsic conditions). Because of their relative stability, intrinsic aspects contribute to the continuity of behavior. Purely extrinsically moti-

The Triad model

vated behavior, for example, has a tendency to fade away quickly when the external stimulant is withheld. P49 The distinction between intrinsic and extrinsic is a subjective one. Relative to extrinsic aspects, intrinsic aspects relate more to selfdetermination, satisfaction and wellbeing. Intrinsic Capacity is closely related to self-confidence. Sometimes the dividing line between intrinsic and extrinsic aspects is thin. For example, reading glasses are externally provided instruments and are, in principle, extrinsic in nature. However, in the course of time, the glasses are perceived by the target person as belonging integrally to him/herself – therefore as intrinsic. This is even more so for prostheses like dentures, artificial joints, pacemakers, etc. A professional tennis player is more likely to view the racket as part of his intrinsic Capacity than a six-year old tennis apprentice. If a person perceives him/herself as having become ‘one’ with a tool, the combination may count as intrinsic. A concert pianist does not experience a distinction between himself and his grand piano (unless it is out of tune). The same applies to support provided by another party (institution or person) or instrument. Help may become so interwoven with one’s activities, that it is perceived as part of one’s own Capacity. For example, over time the help provided by the navigator in the car becomes so self-evident that the driver may not even realize that s/ he did not find the desired location him/herself. Thus, extrinsic aspects may eventually end up as intrinsic aspects. Help for behavior X may be viewed as extrinsic Capacity or as extrinsic Opportunity. If expertise is provided as an addition to one’s own expertise in order to complete a task, then we can refer to extrinsic Capacity. If the function of help is to save time (the chauffeur of a CEO, for example), then the help is a case of extrinsic Opportunity. Help may even be an example of extrinsic Motivation: a personal coach stimulates his pupil to the next level of achievement. Apparently, other persons may play a role with regard to each of the Triad determinants. (Intrinsic and extrinsic aspects can be seen as the two sides of a continuous dimension on which more categories or positions can be placed. Levenson (1974), for example, suggested to add a category of ‘powerful others’ to the internal – external control dimension (similar to that relating to intrinsic and extrinsic Capacity). Powerful others may be viewed as the midpoint of

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the scale. However, for simplicity’s sake we stick here to distinguishing between two broad categories per Triad determinant. Theoretical refinements are not the goal of this book and can be made later). P50 When a behavior analyst is in doubt as to whether an aspect is intrinsic or extrinsic in nature, the perception of the target person (s) is relevant. Here, the notion of attribution (see Brehm and Brehm, 1981) returns. It may be important for the understanding of behavior whether the person attributes a particular outcome to extrinsic or intrinsic causes. In the case of development aid, for example, care must be taken to have the recipients attribute the positive outcome to their own (stable) intrinsic efforts in order to stimulate the continuation of constructive behavior. If the outcomes are only attributed to the help provided by others, the withdrawal of such help is likely to lead to feelings of helplessness (Seligman, 1975) and incompetence and, in consequence, to the discontinuation of behavior X. Similarly, a tutor of a student should see to it that the student attributes positive school results to his/her own efforts rather than the support provided by the tutor. P51 Both extrinsic and intrinsic aspects may be expressed in values just like the Triad values. In fact, they may be visualized in the Triad figure (see Figure 21 for an example). 1.0 M

C 1.0

O

1.0

Figure 21

The Triad Figure (the intrinsic tetraeder is white; the extrinsic complement is grey).

The Triad model

The relationship between intrinsic aspects and extrinsic aspects (/measures) can also be visualized with the help of Figure 22.

1.0

0.0 Motivation

Figure 22

Capacity

Opportunity

Visualization of combination of intrinsic (white) and extrinsic (grey) aspects per Triad factor.

In the ideal case, the extrinsic measures fill the gaps between the values of the intrinsic aspects and the maximum value. However, it may suffice to create a total volume that is larger than the volume of the alternative, undesired behavior option. Then the (suboptimal) Triad value is manifested in limited behavior quality. We already noted that policymakers tend to focus primarily upon Motivation. So they are most likely to focus on measures relating to extrinsic Motivation. However, in terms of generated volume, it may be wiser to divide attention over the three factors. Compare Figures 23a and 23b for an example. Note that the sum of the three extrinsic measures in Figure 23a is the same as the sum of the extrinsic Motivation measures in Figure 23b. The respective approximate Triad scores would be 1.0 x 0.15 x 0.40 = 0.06 and

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0.5 x 0.5 x 0.5 = 0.12. In terms of overall effect, the second alternative (23b) is preferable.

Motivation

Figure 23a

Capacity

Opportunity

Visualization of combination of intrinsic and extrinsic aspects per Triad factor, with the focus on Motivation. The white boxes refer to intrinsic aspects; the grey box refers to extrinsic Motivation.

The Triad model

Motivation

Figure 23b

Capacity

Opportunity

Visualization of combination of intrinsic and extrinsic aspects per Triad factor, with attention more evenly divided over the three factors. The white boxes refer to intrinsic aspects and the grey boxes to the extrinsic aspects.

P52 Intrinsic aspects are closer to the ‘self’ than extrinsic aspects. People therefore have a preference for intrinsic aspects dominating extrinsic aspects. In that case they feel autonomous, independent and in control, capable of pursuing their personal goals. Intrinsic aspects are more closely related to satisfaction and wellbeing than extrinsic aspects (compare P16). Balance and compensation effects So far the Triad model may seem quite simple and straightforward. Now we reach a point where further elaboration takes complexity to the next level. Interactions and dynamic effects enter into the equation. Balance effects explain why people who are motivated to engage in a particular behavior often also have the Capacity and Opportunity to do so. At the same time, balance effects elucidate when and why this may not be the case. Balance effects allow for different interpretations of the same behavior X and suggest

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why particular policy measures do or do not make sense. Balance effects may collide with intuition. Although fragmentation is avoided at all costs, further conceptual and theoretical differentiation will take place – within the overall framework. Gradually, traditional psychological concepts will appear on the horizon, not as isolated, stand-alone concepts, but as part of a more general theoretical structure. P53 When Motivation, Capacity and Opportunity reach their maximum values, behavior X is at its best: it is effective and efficient, and it is associated with high satisfaction. Note that because all values are high, there is also a balance between the values: they have approximately the same scores on their respective dimensions. A balance between the Triad values is a positive state in its own right. It may be achieved at all possible volume sizes. For example, if all three values are 0.20, there is a balance, just as there is when the values are all 0.90. By definition, when the volume increases, value differences reduce. Large value differences imply a low volume. Over time, people experience a balanced volume as rewarding, effective and comfortable because it means that goals, resources and circumstances are mutually supportive and consistent. A lack of balance is associated with feelings of discomfort or, in the case of large value differences, stress. P54 Because of its direct relationship with the Triad volume, the balance between the Triad values is also associated with behavior likelihood, quality, intensity and persistence. P55 A person experiences frustration if Motivation is and remains higher than Capacity and/or Opportunity. S/he wants to achieve a particular goal but is restrained by personal characteristics and/or situational limitations. The higher the level of Motivation, and the larger the difference(s) with Capacity and/or Opportunity, the stronger the feeling of frustration. Conversely, when Capacity and/or Opportunity are higher than Motivation, scarce resources that might be available for other, more productive behaviors are spent in vain. They remain unproductive. In the latter case the person may experience boredom, uneasiness, regret or even guilt – of not making more effective use of scarce resources that can better be spent otherwise.

The Triad model

In a state of balance neither one of these negative feelings is experienced. Again, the intensity of the negative affect (stress) is a function of Triad value differences. P56 If the Triad values of two behavior options are balanced, the person will select the option with the highest volume. Vice versa, if the volumes of two behaviors are identical, the behavior with balanced Triad values will be preferred and selected. Examples: A Triad score of 0.21 that is the product of M, C and O values that are all 0.60 is preferred to the identical score that is the result of M = 0.70, C = 0.60 and O = 0.50. This combination is preferred in turn to a combination of scores of 0.70, 0.30 and 1.0. The degree of balance (Behavior Balance or ‘BB’) can be expressed as: BB = ((M-C) + (M-O))/2 The third difference (between C and O) is not included here as it is determined by the other two differences. Also, the difference between C and O is not meaningful in itself. BB ranges from 1.0 (frustration) to -1.0 (boredom). A 0.0 signifies a state of balance. A moderate deviation from a balanced state is associated with a mild discomfort; a large deviation with stress and dissatisfaction. If the balance is low for a combination of personally relevant behaviors, the person experiences a low level of wellbeing. This may be the result of two mutually opposed types of generalized feelings: on the one hand the generalized frustration that one’s ambitions and hopes in life cannot be fulfilled. On the other hand the feeling of depression: the lack of motivation, energy and sense of purpose to exploit life’s full potential as implied by the abundantly available Capacity and Opportunity. In correspondence with the Prospect Theory (Tversky and Kahneman, 1979) we may expect people to put more effort into avoiding a loss (a negative state) rather than avoiding an overabundance of resources. P57 If there is an imbalance, the person will try to create or restore it. If there is a balance between the Triad values, the person will try to preserve it. Restoring the balance can be seen as coping; increasing the Triad volume after balance is restored can be seen as growth.

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People have various possibilities to create, maintain and restore balance and to realize growth. They can increase or decrease their budgets with regard to scarce resources of Motivation (energy), Capacity and Opportunity. Budgets refer to intrinsic Triad aspects. P58 A person will not increase a budget of Motivation, Capacity or Opportunity if that would increase the imbalance. If the foundation is instable, there is no attempt at growth. When the outcome is determined, and the behavior is pre-set, a temporal imbalance is considered acceptable if it helps to raise the Triad volume to the desired level. For example, a person has very little time for a task that must be finished before the deadline. Opportunity has a very low value. The only way to create some volume is to raise Motivation and Capacity to the highest possible levels even though this would lead to a serious imbalance. The price is stress. P59 A person will not opt for a volume change if that would imply disrupting the balance between the three values. This relates to both positive and negative volume changes. So growth that is more than a temporary improvement of the volume is only feasible if the three values increase simultaneously and at the same speed. (This is also important in the case of a negative volume change in balance. A person will not reduce the value of M and leave those of C and O intact). These notions seem practically relevant. Let’s consider two examples: learning and recovery from illness. Behavior X is ‘to study’. A student experiences a balance: The M, C and O values are moderately high and in balance. Unexpectedly, his teacher provides additional learning material. This will not in itself change the behavior of the student. It does not make sense to only increase Capacity without simultaneously stimulating the pupil (M) and providing for an improvement of learning conditions (O), for example by increasing the amount of available time to study. If these measures are implemented at a different pace, either frustration or discomfort will result from the imbalance. In a healthcare example, a recovered patient needs to be reintegrated into the work process after a long period of absence. Doing his original job is behavior X. In this case the person probably experiences

The Triad model

an imbalance: the M and O values are high, but there is a Capacity issue (‘Am I still capable of doing my job as I used to?’). If he is pressured by an overemphasis on Motivation and Opportunity, the three values will be out of tune with each other even more, resulting in a continuation of an imbalance that is not conducive to full and rapid integration. His employer should carefully manage the restoration of a balanced relationship between the three values. P60 Balance effects only apply to behavior options that are under consideration. Balance effects do not occur when the behavior is pre-set. In that case, volume dominates balance. P61 Several types of balance effects can be distinguished, ranging from simple to more complex. Simple balance effects are preferred over more complex effects. 1. Balance maintenance effects involving adaptations within the intrinsic value of a determinant. Here, relatively minor fluctuations are compensated in order to conserve the Triad volume so that a switch to another behavior can be avoided. 2. Balance maintenance effects that involve the compensation of an extrinsic value by an intrinsic value or vice versa. 3. Balance effects that imply the correction of one Triad determinant value by another. These may change the Triad volume and thus may affect behavior likelihood, quality, etc. 4. Balance effects that pertain to the relationship between intrinsic and extrinsic values over the three Triad determinants. 5. Some extraordinary balance effects, to be discussed separately. The five types will be explained below. Examples are provided together with some practical examples and implications. The cause of an imbalance may be either intrinsic or extrinsic in nature. Both intrinsic and extrinsic aspects can restore the balance. However, only intrinsic aspects are under the control of the person. P62 Simple effects involve an adaptation within the same intrinsic aspect that threatens to tip the balance. For example, a cyclist notices that he gets tired (C i ). So he redirects his thoughts to a mental task, thus hoping to distract himself (Ci). These simple effects may be the first to come to mind if compensation is needed.

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P63 Another rather simple effect involves the mutual compensation between intrinsic and extrinsic values of the same determinant. Figure 24 shows how a reduction of extrinsic Capacity is compensated by an increase of intrinsic Capacity.

M

M

Ce

C

C Ce

O

Ci

Figure 24

A compensation effect intended to maintain balance. The dotted line on the left shows the reduction of Ce. This is counteracted, in the figure on the right, by increasing the level of Ci (solid line).

O

Ci

Some examples may be provided for Opportunity and Motivation: suddenly less time (Oe) is available to finish a mentally demanding task,. The person concerned relocates to continue working in a particularly quiet room to (Oi). In another example, a cyclist notices that it is difficult to make enough speed against the strong wind (Oe). He changes position to ride behind the ‘train’ of cyclists in order to benefit from the slipstream (Oi). This does not remain unnoticed by the ever-present members of the press who openly question his physical condition (Me). He compensates for this by working up his intrinsic motivation (Mi). Alternatively, an increase of an extrinsic value may be followed by a reduction of the intrinsic effect of the same Triad value. Example: for a number of years a person served as a volunteer fundraiser. When the management of the organisation concerned decides to give to all volunteers a financial bonus as an incentive, the person stops being actively involved. Apparently (in order to restore balance), extrinsic Motivation may drive out intrinsic Motivation.

The Triad model

P64 Balance effects that imply a compensation effect by a different Triad determinant than the one that causes the imbalance. Johnson is racing around the garden mowing the lawn (behavior X). He wants to finish the chore as quickly as possible. After 20 minutes, he becomes tired. The C value drops to a low level. It is clear in this example that finishing the job is highly dependent upon the value of Opportunity, assuming that motivation remains at a high level. If Opportunity does not provide compensation, it becomes increasingly likely that another behavior will take over and mowing the lawn is postponed. Before this happens, the imbalance that is created between the Triad values causes feelings of discomfort. Two other forms of compensation may be distinguished. These are called here the ‘primary’ and the ‘secondary’ balance effect. Again, both relate to intrinsic aspects. The labels indicate the order in which they take place – a primary balance effect takes place before a secondary balance effect. P65 A primary balance effect occurs when Motivation has a higher value than Capacity and Opportunity and the person makes an attempt at compensation by raising the (intrinsic) values of Capacity and Opportunity. Of course, this is only possible if stocks of the latter two are sufficient. A primary balance effect may be positive or negative in nature. A positive primary balance effect occurs when the C and/or O values increase as a result of a higher M value. The values will not increase beyond the level of the M value. For example, a consumer is very interested in buying the latest mobile phone (high M). The product can be purchased, but the store is far away (O has a low value) and the phone is very expensive (limited Capacity). In order to save some money – to raise C – the consumer decides to cancel his weekly visits to the movie theatre and he raises the O-value by taking a day off so that he can travel to the distant store. A negative primary balance effect takes place when the higher C and/or O values adapt to the lower M value. For example, a supervisor offers an employee a new pc, a time management program and several coaching sessions in order to raise the quality of his job performance. However, this C- and O-offer is ignored due to a lack of motivation.

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See Figures 25a and b for a visualization of a positive and a negative balance effect. M

M

C

O

C

O

Figure 25a

A positive primary balance effect. The figures on the left and right show the situation before and after the balance effect has taken place.

M

M

C

O

Figure 25b

C

O

A negative primary balance effect. The figures on the left and right show the situation before and after the balance effect has taken place.

Positive and negative primary balance effects are achieved through the person’s own manipulation of intrinsic Capacity and Opportunity (although the reason for the high Motivation may be intrinsic and/or extrinsic in nature).

The Triad model

P66 If Motivation has a higher value than Capacity and Opportunity and the values of the latter two are limited to a lower level, frustration is the result. The person wants to engage in behavior X, but cannot do so because of resource constraints. As these cannot be lifted intrinsically, the person has no other option than to negatively adapt the level of Motivation. This prevents him from experiencing prolonged frustration. This effect is referred to as the secondary balance effect. See Figure 26.

M

M

C

O

Figure 26

C

O

A secondary balance effect. The C and O values are blocked.

In Figure 26 the levels of O and C are equal. If they differ, M will adapt to the lower of the two levels (after which a primary balance effect will restore the balance between the three values). For a number of years, Johnson has had one goal: to win an Olympic medal. His Motivation is sky-high and Opportunity is no problem. Johnson spends a great deal of time and effort on training to improve his results, but in the process he suffers many injuries. The high level of Motivation stimulates him to focus on rapid recovery but his body repeatedly refuses to cooperate. Frustration is high. As the Olympics do not wait for him to be optimally fit, soon Opportunity becomes a problem as well. And the next Olympics are a full four years away so that age will present other Capacity issues. The combination of low Capacity and low Opportunity require him to give up Motivation in order to avoid another long period of frustration. He is now a coach.

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The order of primary and secondary balance effects is suggested, implicitly, by the Prospect Theory (Tversky and Kahneman, 1979) referred to earlier. People will try harder to avoid a loss than to achieve a gain (if both are of equal size). This means they will first try to stick to their goal before letting go. In some way, the secondary balance effect can be interpreted as giving up. Secondary balance effects indicate that it may not be a help to stimulate people with the perspective of long term goals that need considerable investment to achieve. Although these goals may be associated with high M values, people are bound to experience insufficiency of Capacity and Opportunity on their way to a far-reaching goal. Here a primary balance effect is likely to be replaced by a secondary balance effect. Motivation is reduced in order to avoid continued frustration. In cases like this it is far more effective break up the long term goal into consecutive short term goals. It is easier to muster the required Capacity and Opportunity for short term ambitions. The same notion applies to raising ambitions that seem beyond reach (‘if you practice well for your piano lessons, you might become a concert pianist when you’re as old as your Daddy’). P67 Primary and secondary balance effects can take place consecutively, within a short period of time. For example, the Triad volume of a particular behavior (e.g., running 10 miles) is favorable: M, C and O values are high. However, at some point the C value drops due to fatigue. As the goal does not change, the M value initially remains the same and so does the O value: the road is excellent and so are the weather conditions. In the case of fatigue, a primary balance effect, if at all possible, is brief and minor. Soon a secondary balance effect is prompted: Motivation drops (‘Why am I doing this??’). The lowered M value then draws the O value in its wake and pulls it down: (intrinsic) Opportunity is reduced. The runner takes a shortcut home. Understanding and adequately managing the balance effects of his pupils may be one of the most important functions of a sports coach. The distinction between primary and secondary balance effects may have important practical implications. For example, it requires a closer look at the nature of low Motivation. If an employee appears unmotivated, the managerial reflex is to kick this person’s behind. The implicit assumption

The Triad model

is that if a problem appears to be a motivational problem it should be treated as such. However, the secondary balance effect shows that a low level of Motivation may have different causes. One is obviously a lack of interest or ambition, but other possible causes are Capacity and/or Opportunity limitations. These may be both intrinsic and extrinsic in nature. Managerially, this opens up many more possibilities than just providing external stimulation of Motivation. The possibility of a secondary balance effect requires a more careful analysis of the nature of the motivational problem and possibly leads to measures that stimulate Capacity and/or Opportunity instead of Motivation alone. Stimulating Motivation would even be counterproductive if a secondary balance effect is the cause of Motivation. P68 In a similar vein, people may not like what they do not understand or do not know. Not because they dislike it as such, but because the secondary balance effect has pulled down the M value. Do people distrust/dislike people from minority groups because they have a fundamental aversion against them (low M) or because they simply do not know these people (low C) or have no contact with them (O)? Note that interaction with minority groups is impeded by both Capacity and Opportunity so that a secondary balance effect is rather probable. If these restrictions are lifted, people from different groups often appear to like each other on an individual basis. Anonymity produces aversion. If you want your enemy to remain your enemy, just don’t meet him. P69 If the secondary balance effect cannot be expressed in active behavior, it is expressed in affect. Downgrading is a popular technique. Peters is a fanatic golf player. He claims that it is the most sophisticated sport in the world and that he, incidentally, is quite a good player. After a dramatic round, in which he even fails to achieve rookie level, he remarks: ‘Well, it’s only a game’. If policy measures do not address the three Triad variables simultaneously, they run the risk of unknowingly confronting balance effects that may amount to an unnecessary reduction of the Triad volume. By addressing the three Triad determinants at the same time, this risk may be reduced or altogether avoided.

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P70 Motivation to engage in a particular behavior cannot be increased by higher C and/or O values. This seems to be a counterintuitive proposition. Yet, if it were not valid, people would be constantly running around, their behavior prompted by everything they can possibly do and for which they have the opportunity. This proposition, or rather the effect that it describes, saves (most) people from sheer madness. This proposition suggests that it does not make sense, from a practical point of view, to take Capacity or Opportunity measures if Motivation is low or unknown. For example, cities build sport facilities in order to support physical exercise by the city’s population, especially the part of the population that ought to take exercise from a normative medical perspective. However, the new facilities will most likely attract those citizens that are already motivated and who are already involved in sports. So providing facilities helps people with sufficient Motivation and Capacity. It has no or only a limited effect on other people – at whom the policy may be targeted. Facilitation should not be confused with stimulation. In the same vein, merely increasing a person’s (perceived) Capacity does not, by itself, increase Motivation. If an employer sends employees to an obligatory training, this in itself will not raise Motivation. If behavior X (taking part in the training) is enforced, the lack of Motivation will be expressed in low behavior quality and persistence (e.g. long coffee breaks). Motivation can be raised by carefully communicating important reasons for the training and/or by making the training itself highly attractive. For measures like these to be really effective and have an effect when and where they ought to, attention to all three Triad variables is a prerequisite. The problem is that most policymakers and managers think in terms of one determinant at a time. When it comes to behavior they are one-dimensional thinkers. That is not wrong; it is painfully insufficient. P71 There is one important exception to the previous proposition stating that, in principle, an increase of Capacity and Opportunity does not lead to an increase of Motivation: only if a low level of Motivation is the result of a secondary balance effect, an increase of Capacity and/ or Opportunity (whichever caused the balance problem) will cause Motivation to bounce back to its original level or to the level allowed by Capacity/Opportunity. In this case, Motivation is not

The Triad model

generated by Capacity or Opportunity. It was already present. Let’s call this the ‘rebound effect’. This proposition often proves confusing. Sometimes it looks as if an increase of Capacity and Opportunity does result in an increase of Motivation, even when no secondary balance effect is at stake. For example, a person is given a chess lesson and begins to enjoy the game. Here, however, the lesson should be interpreted as raising both Capacity and Motivation simultaneously. While learning to play chess, a person suddenly realizes that he likes it. The lesson does not first increase Capacity before Capacity increases Motivation. They are lifted simultaneously. P72 The combination of the various propositions concerning the primary and secondary balance effects, including the present proposition, underscores the point that Motivation does not play a more important role than Capacity and Opportunity, but rather a different role. In fact, it acts like a pulley relative to the other two. P73 Capacity and Opportunity cannot influence each other directly. An indirect influence may occur when a limitation regarding one of these factors affects Motivation that, in turn, affects the other determinant through a primary balance effect. For example, a person finds out that his cooking skills are far below par. Help is not available. The Capacity constraint lowers the M value. The primary balance effect then causes the O value to drop as well. He is rarely to be found in the kitchen. P74 Balance effects that pertain to the relationship between intrinsic and extrinsic values over the three Triad determinants. In the previous options to develop, maintain or restore a balance, intrinsic aspects played the major role. The result is a balance between intrinsic aspects generated by primary and/or secondary balance effects. It may be assumed that extrinsic aspects also play a role in the notion of balance. People seem to prefer having the same relation between intrinsic and extrinsic aspects to the Triad determinants. For example, if intrinsic aspects dominate extrinsic aspects for Motivation, the same domination is preferred for Capacity and Opportunity. In other words, people want to be in control of Capacity and Opportunity themselves to the same extent that

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Motivation is intrinsically determined. Conversely, if they have limited intrinsic Motivation but experience a strong extrinsic Motivation, they prefer (and feel entitled to) relatively high levels of external support (C) and externally provided facilities (O). ‘You really want me to do this? Then you better help and facilitate me’. A combination of low intrinsic Motivation and high intrinsic Capacity/Opportunity would be experienced as a friction. Also a combination of high intrinsic Motivation and low intrinsic Capacity/ Opportunity would be judged as awkward. Being in control over important personal goals is associated with more satisfaction than having no control over important goals, or having control over unimportant goals. Practically, this proposition means that the intuitive assumption that extrinsic support/facilitation is always welcome does not appear valid. Extrinsic support may be declined – or result in dissatisfaction – if people experience it as a lack of recognition of the ambition to achieve goals by themselves. Help can become an overdose. Often, well-to-do parents want to support their children financially. If they do not understand the children’s reluctance to accept their support, they might want to consider the possibility that they have raised their children to be ambitious. More generally, if Motivation and Opportunity for a particular behavior are both high but Capacity is lagging behind, help (extra Capacity) will be gladly accepted. However, if Motivation and/or Opportunity are low, help is likely to be declined, as it would raise Capacity above the level of Motivation/Opportunity, thus creating a state of imbalance. Rejecting help is then preferred to accepting it. Often, depressed people just want to be left alone. If support is merely intended to help save time, the same explanation applies, but then for Opportunity. ‘No thanks, I’m fine’. Finally, some extraordinary balance effects may be noted apart from the two balance effects and the rebound effect: the dissonance reduction effect (Festinger, 1957), the reactance effect (Brehm and Brehm, 1981), the attribution effect (Rotter, 1966), the burnout effect, the compensation effect and the over-compensation effect. P75 Normally, a person engages in behavior X only after it has been established that the M, C and O values are sufficiently high (and

The Triad model

higher than those of the next alternative). Sometimes, however, unusual circumstances may cause a person to become involved in a particular behavior although his Motivation is low (the behavior can only be shown if Capacity and Opportunity are sufficiently high). In this case there is an imbalance between the behavior and the Triad conditions. Then, the only way to restore the balance is to raise, post hoc, intrinsic Motivation (as, obviously, extrinsic Motivation cannot be raised). ‘I have done it so I must have wanted it’. This effect resembles the well-known dissonance reduction effect (Festinger, 1957). For example, Peters attends a formal reception where funds are being raised for a school in Africa. Peters does not intend to make a donation because he never donates to charity. Suddenly, he realizes that he has lost a banknote from his pocket. Only moments later, someone announces enthusiastically that a banknote has been found and that this money was undoubtedly intended for the fund. The anonymous donor is thanked profusely. Peters, smiling sheepishly, has unwillingly shown a behavior for which he had had no intention. He had the Capacity and the Opportunity, but not the Motivation. In order to restore the balance and get rid of the associated discomfort, he convinces himself that a donation is a good thing and that he is proud of himself in having contributed. After all, every child in Africa should be entitled to good education. Peter’s Motivation is raised after the fact. The next day, the sign of the effect changes when Peters is to compete in the 100 metre sprint. He is eager to win and pleased about the prospect of standing on the podium, waving to the admiring audience. However, his Capacity falls short and he finishes last. (Opportunity is practically equal to all runners). After the race, Peters claims he does not mind about not winning. After all, he considered this event a nice little practice for future races. Here, his Motivation is lowered after the fact. P76 If a balance effect is impaired by extrinsic C and/or O determinants, and the balance cannot be corrected by intrinsic C and O values, an attempt is made to (at least) maintain the original Triad volume. This effect is also known as the ‘reactance effect’ (Brehm and Brehm, 1981). It was briefly referred to earlier.

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A first example: A supermarket has a too large stock of a perishable product. Consumers do not seem particularly interested in buying it. A sign is then displayed near the concerning shelf announcing that individual customers are entitled to buy only two items each, maximum. The product is sold out in no time. The suggestion of scarcity (Opportunity) raises attractiveness (Motivation). (See also Worchel (1975) on this scarcity – attraction relationship). In another example, an increase of the M value is an expression of not being prepared to give in after a reduction of Opportunity. An employee is working at his laptop. He wants to finish his task today. Motivation, Capacity and Opportunity are not very high, but sufficiently so and nicely balanced. Then his supervisor comes in, and informs him that there is no more time to continue working today and switches off the lights. No reason is given. The employee views this as a sudden restriction of his Opportunity and a disruption of the balance. It is not possible to compensate for the loss of Opportunity by raising intrinsic Opportunity. The balance is lost and cannot be restored. Instead, the employee’s attention shifts to maintaining the original volume. This is only possible by increasing Motivation (Capacity is unaffected): causing the person to re-value the task so that he is even more eager to finish it than before the incident. See Figure 27. The likelihood of the behavior is sharply reduced, however. Severe frustration is the result. The imbalance and the associated discomfort or irritation is preferred over giving in or giving up easily. Although the explanation provided here fits in a Triad interpretation, the effect referred to is known as a ‘reactance effect’ (see, for example, Brehm and Brehm, 1981).

The Triad model

M

M

C

O

Figure 27

C

O

A reactance effect: the O-value is reduced in the figure on the left, leading to an increase of M in the figure on the right.

The following day the example is reversed in another reactance effect. The employee is completing his work at the end of the week and is ready to start the weekend with his family. There is a nice Triad balance for behavior X: going home. At that moment his supervisor enters the room and tells him that the office will stay open for the employee to do extra work. This time again no reason is given (communication not being one of the supervisor’s strongest points). The Opportunity to work increases, disrupting the balance. As Capacity remains unaffected, the only way to maintain the original volume is to adapt Motivation negatively. The employee’s Motivation to continue working is even lower than if the Opportunity restriction had not been given. This is another example of a reactance effect. See Figure 28. If the supervisor had provided reasons (extrinsic Motivation) why the work had to be finished today, the reactance effect would probably not have occurred (as in that case, there would not have been an imbalance). Apart from providing extrinsic motivation, expectation management can also prevent undesired reactance effects. The employee would be invited to adapt his intrinsic budgets according to the new situation. In both cases, the imbalance causes negative feelings. As the likelihood of the behavior cannot be changed (it is dictated in both cases), the imbalance will be reflected in both dissatisfaction and a lower quality of work.

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M

M

C

O

Figure 28

C

O

A reactance effect: the O value is increased in the figure on the left, leading to a decrease of M in the figure on the right.

A reactance effect and a secondary balance effect seem to have opposite results. According to the reactance effect, M increases if the values of C and O are reduced; the secondary balance effect implies that M decreases if C and O are reduced. The two effects take place under different conditions, however. A reactance effect takes place if the person is determined to achieve a particular outcome or committed to show behavior X. A secondary balance effect occurs when C and O pose limitations that cannot be overcome. A reactance effect and a secondary balance effect may take place in that order. If a person is determined to show a behavior and is prevented from doing so, the first reaction is an attempt to do it anyway: a reactance effect. However, if the hurdles are too high in the form of C and O constraints, there is only one option left: to give up. P77 Balance effects imply a change of intrinsic Triad aspects. They always involve perceptions or subjective interpretations. From here it is a small step to subjective attributions (Rotter, 1966). Attributions refer to the perceived cause of an effect. A tennis player may attribute her success to her personal qualities or to the weakness of the opponent. Personal qualities, in turn, may be attributed to genetic make-up, the amount of training, the quality of the coach or some combination. Attributions may help – or subtly manipulate – balance effects. An example: the tennis player is very eager to win a match. She has trained intensively and the weather is good, so the M, C and O values are all very high. She loses the first game due to consistently bad services. Also the second game is a dramatic love-

The Triad model

game, now because of her bad returns. The outcomes might cause her C value to drop to a low level. It would endanger the overall volume of behavior X: playing to win. An attribution trick can help out here. Instead of putting the entire burden on Capacity, the player divides the problem between Capacity and Opportunity, ‘knowing’ that in this case the resultant volume is larger than when Capacity gets all the blame. So she treats herself in the form of a rather harmless Capacity reason (‘I am still warming up’ and a modest Opportunity reason (‘The sun was shining in my eyes’). That way the player manoeuvres herself out of a situation that might jeopardize her motivation, her self-confidence, and in the end, her opportunity to win. Why do people closely inspect their racket after hitting a ball badly? An important function of a coach is to manage balance effects in relation to attribution effects. P78 The speed at which balance effects take place depends on the degree of imbalance. The larger the differences between the Triad values, the quicker and more intense the effects are. People differ in the speed with which they are willing to allow secondary balance effects to come into play. Highly ambitious and committed persons are slow balancers. The longer the secondary balance effect is delayed, the more tension builds up and the more dramatic the eventual balance effect. P79 Let’s start the following proposition with an example. It concerns Williams, an elderly employee. He has been working for the company for 40 years with full dedication. He is all stability and dependability. Because of his age, it becomes increasingly difficult for him to keep track of the latest professional developments. He cannot keep up anymore with the fast pace of the work. Yet he refuses to relax his dedication and eagerness to do the job perfectly. So Williams has a high level of Motivation to do his work, but his Capacity and Opportunity slowly deteriorate. The difference between Motivation on the one hand and Capacity and Opportunity on the other creates considerable stress and illbeing. After this has gone on for a lengthy period, a minor frustration has a dramatic effect. Suddenly, Williams’ characteristic calm and composure explodes into a loud display of uncontrolled agitation. He returns home and spends the

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following weeks in bed, keeping eyes and curtains closed. Williams is diagnosed as suffering from a ‘burnout’. In this case Motivation, Capacity and Opportunity originally all have high values. Then Capacity and Opportunity values gradually decrease over time. Because primary balance effects are out of the question, a secondary balance effect (reduction of the M value) can be expected. However, in this particular case the person does not give up. For the explanation of what subsequently happens, we can best use the Triad figure. The C value and O value are fixed at lower levels. A secondary balance effect should take place, pulling the M value down to the level of the C and O values, but in this particular case it does not. Let’s refer to the M value that would be the result of a secondary balance effect as M’. Relative to M’, the actual M value is kept at an artificially high level. We can imagine how the distance between M and M’ is stretched like a rubber band. If M matches M’, there is no stress. The more the M value is pulled away from M’, the higher the tension. See Figure 29a. 1.0

M

M’ 0 1.0 C

O

1.0

Figure 29a

The tension created by the absence of a secondary balance effect. M is the actual Motivation; M’ refers to the Motivation level that would have been adopted after a secondary balance effect.

As Capacity and Opportunity gradually continue to decrease, the distance between the M value and M’ increases. Tension builds up over time. At

The Triad model

some point the situation becomes highly unstable, sensitive to minor disturbances. A trivial incident, for example, may suddenly release M and send it plummeting down. The effect is comparable to letting go of the stretched rubber band. But the comparison does not stop here. After its release, the rubber band does not come to a slow, controlled stop but slams into the point where it is fixed. Similarly, the sudden release causes M to crash into the zero point of the Triad figure, where it comes to a sudden and full stop. Along the way it passes the level of M’ and in its wake drags the C and O values with it because of primary balance effects. The whole structure collapses and the Triad figure implodes to the one displayed in Figure 29b. Let’s call this special balance effect the ‘burnout effect’. 1.0

M

M’ 0 1.0 C

O

1.0

Figure 29b

The result of structure collapse created by a delayed secondary balance effect. See solid lines.

The larger the difference between M and M’, the more likely and the stronger the ultimate burnout effect. The new situation, characterized by a very small Triad volume, causes the employee concerned to feel depressed, worthless and incapable of doing just about anything. Because of the high personal relevance of behavior X (doing the job) there is a strong negative impact on wellbeing. So being highly motivated and committed may come at a high price if the person is not sufficiently supported by Capacity and Opportunity. The example and its explanation suggest that there are several ways to deal with a burnout from taking place. The most obvious and important one is

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to prevent a risky situation from developing. However, practical experience shows that, too often, employers look the other way until it is too late. Other possible measures concern the situation when the problem actually developed. These amount to restoring the balance. The first option is to provide extra Capacity and Opportunity support, preferably leading to effects interpreted by the person as intrinsic. The second option is to reduce Motivation to a more realistic level (although short term oriented employers may not feel this is a viable option – but it is). A third option is to find a different type of work for the person within the organization that would allow him to once again experience a favorable combination of Motivation, Capacity and Opportunity. The final, least constructive option is to stop Opportunity altogether: to fire him. What measures are possible after the fact? How can the person be assisted to climb out of the pit? Balance effects suggest that it does not make sense to increase one of the three levels of the Triad values. If the M value is increased, it may be possible only to the level of the C and O values, but these are constrained. Otherwise secondary balance effects set in again. Increasing either one or both of the C and O values by extrinsic measures is futile as primary balance effects will neutralize the increases in no time (note that M is still at a very low level). It is pointless to raise Capacity and Opportunity if they are not backed up by Motivation. Raising all three levels gradually and simultaneously seems to present the only solution. For this, the criterion behavior may have to be adapted. A task which is less demanding in terms of Motivation, Capacity and Opportunity is likely to be more effective than providing all kinds of sequential stimulation and support for the continuation of the original task. The so-called Peter principle (promoting a person to the level at which s/he is incompetent) (Peter and Hull, 1969) provides an example of a case in which balance effects are dealt with unprofessionally. A person’s promotion implies a change of behavior. For the new behavior (new behavior X) new Capacity and Opportunity conditions are also called for that may differ from those for the original behavior. People are often promoted on the basis of their future oriented ambition (M) and history-based Capacity and Opportunity, not realizing that additional support for the Capacity and Opportunity of the new behavior may be required to effectuate the higher ambition.

The Triad model

P80 Over-capacity exists when a person has more Capacity available than is needed for the behavior at hand and primary balance effects cannot reduce the surplus. For example, a highly intelligent person is required to perform a mental task that is far too simple for her. Together, the over-Capacity and the tendency to preserve the Triad volume lead to under-Motivation (feeling bored, disliking the task), to under-Opportunity (speeding up the task) or to a combination of these compensation effects. Similar effects will take place in the case of over-Opportunity if it cannot be reduced by way of a primary balance effect. Over-opportunity leads to boredom (under-Motivation) or to under-Capacity such as lack of focus. If the surplus of Opportunity refers to space, the extra space may cause a person to experience negative affect such as feeling out of place or ‘not at home’, or under-Capacity like being disoriented. P81 A state of over-motivation exists when there is an excess of Motivation beyond the maximum value (which is the highest value that is still functional). Over-motivation can be seen as disproportionate Motivation that, through primary balance effects, raises disproportionate Capacity and Opportunity levels as well. The combination, in turn, translates into disproportionate behavior. Over-motivation may have an intrinsic and/or extrinsic origin. Over-motivation is associated with discomfort or stress. (Another way of putting this is that people seek an optimal level of ‘arousal’ or stimulation (Berlyne, 1969; Steenkamp and Baumgartner, 1992). They avoid over- and understimulation. When behaviors become habitual and routine, understimulation is more likely and seeking variety is the likely result). Below, several versions of the same example are presented to show the difference between high motivation, over-motivation, extreme over-motivation and panic. The former two relate to primary balance effects; the latter two can be seen as producing over-compensation effects. In each version, behavior X is: driving a car. Version 1 Johnson is ready for a next move in his career and applies for a new job. This job would involve more inspiring and more rewarding work than the present one. Johnson sends in an application and hopes for the best. He is invited for an interview. A day before the appointment, he checks the oil

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of his car and plans a quiet route as he definitely does not want to be late. This is an example of a regular primary balance effect: the high M value to drive causes increases of the C and O values. See Figure 30a.

M

M

C

O

Figure 30a

C

O

The primary balance effect (ordinary case).

Version 2 In this version, Johnson desperately wants the job. Avoiding all possible risks of missing the appointment, he has his car checked by the garage a week before the interview and surfs the internet for alternative routes in case of traffic diversions. And on the day of the planned interview he leaves home very early so that, after arriving at his destination, he has to wait in the parking lot for more than an hour. In this case, there is some overmotivation leading to some Capacity and Opportunity overkill. Capacity and Opportunity are stretched to go over their maximum functional value but are still actually harmless. See Figure 30b.

The Triad model

M

M

C

O

Figure 30b

C

O

The primary balance effect in the case of over-motivation.

Version 3 Johnson is very nervous for the interview that might change his life and that of his family in ways that he can only dream of. He made all the preparations of the previous example. Shortly after he leaves home, he gets caught in the worst traffic jam in years. A phone call to report the mishap and the inevitable delay is impossible: his nerves caused him to forgot his cell phone at home. The situation seems hopeless. There is no information on the cause and the expected duration of the congestion. Johnson is extremely stressed. Each time he is allowed to move forward a few yards, he revs the engine of his car and accelerates explosively, only to hit the brakes moments later in order to avoid colliding into the car in front of him. Finally, traffic slowly starts moving again and Johnson, red-faced, desperately tries to make up for the many lost minutes in order to arrive not too late. He ignores speed limits and traffic signs, drives in emergency lanes, honks at slower drivers, looks at his watch every 20 seconds, waves all kinds of fingers and verbally expresses himself in ways that cannot be reported here. In this case of strong over-motivation, the behavior disintegrates into two types of behavior: the original behavior (driving) and additional behaviors that are irrelevant or even counterproductive for reaching the goal. For the original behavior (driving the car) additional primary and balance effects are impossible. And a secondary balance effect (reducing Motivation) is out of the question. As a result, only the Triad volume can be preserved which means that the over-motivation leads to a reduction of Capacity and

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Opportunity. Over-motivation backfires in a reduction of driving quality. Meanwhile, a burnout effect looms in the background. See Figure 31a and b. a

b M* M” M

C* O*

C

C” O”

O

Figure 31a/b Extreme over-motivation causing two effects: relative to the original behavior (MCO; dashed lines), a reduction of behavior quality (M*C*O*; solid lines) and the onset of new, irrelevant behavior (M”C”O”). Version 4 In this last, rather dramatic version traffic has come to a complete stop, seemingly forever. It definitely looks like Johnson is not going to make it in time. When the congestion eventually dissolves, Johnson, who is now completely in panic, starts driving like a maniac, leaving a trail of broken side mirrors, skid marks, ploughed gardens and shocked pedestrians. He is so focused on speed and time that he even misses the correct exit – to the appointment and his new future. Johnson has a burnout, stops his car, gets out and starts walking in the direction of the sunset. The surplus Motivation could no longer be invested in the original behavior so it spills over to goal-irrelevant behaviors. Here it combines with the residual Capacity and Opportunity. If the resulting behaviors acquire a larger Triad volume than the original behavior, driving loses out. Extreme over-motivation (panic) tips the relationship between the two Triad volumes. Unwillingly and unknowingly, Johnson has switched to behavior that actually prevents him from achieving the desired result.

The Triad model

The degree of over-motivation determines the likelihood and intensity of the irrelevant behaviors and the level of stress. In extreme cases, irrelevant or even counterproductive behaviors may include reactions such as senseless screaming, running around without purpose, violence and self-destructive behavior. These behaviors and the associated strong feelings may be accompanied by physiological reactions such as trembling, sweating, uncontrolled motor movements and hyperventilation. This provides the bridge to the final psychological topic to be discussed in this book: emotions. Emotions In our description of the Triad model affect, feelings and emotions have played a modest role so far. A high Triad volume and its balance were associated with satisfaction, and the combined Triad volumes with wellbeing. Conversely, dissatisfaction and illbeing were related to small Triad volumes and imbalance. It was suggested that small deviations from a state of balance were associated with discomfort, and larger deviations with stress. In the present section we focus specifically on emotions. The topic of emotions has a long history in psychology (for an early publication see James, 1884), yet the definition is far from clear. ‘There is no commonly agreed-upon definition of emotion in any or the disciplines that study this phenomenon. This fact leads to endless debates and hampers the cumulative progress of research’ (Mulligan and Scherer, 2012, p. 345). Similarly, ‘scientific study of emotion faces a potentially serious problem: after over a hundred years of psychological study, we lack consensus regarding the very definition of emotion’ (Cunningham and Kirkland, 2012). Note that the word ‘potentially’ is rather surprising in this context considering the long period of research. According to Mulligan and Scherer (2012), ‘much of the confusion in this area is due to the semantic overlap of denotations and connotations of the terms affect, emotion and feeling, and the retrospective adjectives and adverbs, as well as a host of other terms such as preferences, emotional attitudes, moods, affect dispositions, or even interpersonal stances’ (p. 346). A large number of differing lists of possible emotions can be found in the literature. Ledoux (2012) distinguishes between lists based on introspection and lists based on brain research (for the latter he refers among others to Panksepp, 2005). It does not make sense to present an inventory of these

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lists here. Most emotions have words in everyday language to denote them. Plutchik (2002, 1991, 1980) presents a well-known inventory. He distinguishes eight contrasting (positive – negative) emotions: joy versus sadness; anger versus fear; trust versus disgust; and surprise versus anticipation. Ekman (1992) presents another overview with six basic emotions: anger, disgust, fear, happiness, sadness and surprise. Schachter (2011) positions emotions in a matrix formed by two dimensions: pleasant/unpleasant and activation/deactivation. Other authors have produced different overviews, systems or models, between which there seem to be more similarities than differences. It is neither the intention nor the ambition of the present discussion to make a unique contribution to the emotion literature. Its only purpose is to assess whether the Triad approach can be related to (some?) emotions, and if so, how. The lack of a generally accepted definition poses a problem because if we do not know exactly what an emotion is, we also do not know what it is not. This complicates any kind of any theorizing on emotions, but let’s make an initial attempt. According to Roseman (2013), ‘emotions can be understood as a coherent, integrated system of general purpose coping strategies (…) for responding to situations of crisis and opportunity’ (p. 141). Merriam-Webster (2012) presents the following definition of emotion: ‘A conscious mental reaction (as anger or fear) subjectively experienced as strong feeling usually directed toward a specific object and typically accompanied by physiological and behavioral changes in the body’. Mulligan and Scherer (2012) add that ‘Compared with the other affective phenomena, emotions are focused on concrete events, objects, and situations, and last a relatively short time’ (p. 346). In the absence of a unanimously accepted academic definition, we may just as well, like several other authors, adopt that presented by Merriam-Webster (2012), which emphasizes the strength of feeling, the focus on a target (object/person/situation) and physiological/ behavioral changes. Note that the term ‘behavioral changes’ suggests the possibility of relating the Triad approach to emotions (although the definition does not specify whether behavior precedes, accompanies or follows emotions). Here we will explore whether the explanation of strong feelings may be related to the Triad conditions as precursors to behavior. So we will confine ourselves to emotions that are related to behavior. We will discuss the possibility of emotions

The Triad model

only if they can be related to the Triad model. Our focus will be on emotions in general, not on presenting an inventory of more specific types. P82 The relation between affect and emotions is asymmetrical. All emotions belong to affect but not all affect is emotional. Only extreme degrees of affect (strong feelings) are regarded as emotions here. P83 Emotions have a sign and a particular strength. The sign expresses the direction of the change of the Triad volume and the change of the balance in the Triad figure. A positive change of the volume and the balance is associated with positive affect, a negative change with negative affect. The strength of the feelings depends on a number of factors. It is assumed to be positively associated with: • The subjective importance of the goal/ the behavior. • The number of behaviors that the feelings refer to. An emotion is stronger when the Triad volume concerns a more aggregate level of functioning as compared to a single behavior. Feelings that reflect combinations of relevant behaviors (to survive, to do one’s job) are stronger than feelings with regard to simple behaviors like the use of a household product. • The size of the change of the Triad volume. • The suddenness of change. • The direction of change: a negative change produces a more negative evaluation than a positive change, even if the size of the change is the same (see Tversky and Kahneman, 1979). • The centrality of the determinant: feelings are stronger when related to Motivation as compared to Capacity and Opportunity. • The absence of alternative behavior options. The strength of emotions is negatively associated with the period of time after the change. People experience a reduction, over time, in the intensity of affective reactions to both positive and negative stimuli (Lucas, 2007; Angner et al., 2009, Carel, 2009; Diener and Diener, 1996). Often, an emotional reaction to a negative change may be alleviated by providing sufficient information on the reasons why the change has occurred, why the change is as large as it is and why it has taken place abruptly. Information prior to the change is more effective than information after the event. Expectation management provides the right reference point and attributions.

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Information may dampen the Triad conditions under which emotions are likely to develop. P84 The nature of the emotion varies with the nature of the determinant of the volume change. Let’s assume that the initial situation is a balanced state and that, relative to this state, all changes are sudden and substantial, and distort the balance in a positive or negative way. See Table 6. Table 6

The relation between Triad value changes and emotions.

Determinant

Sign of change

Change relative to

Emotion

1. Motivation

Positive

C and O

Excitement

2. Motivation

Negative

C and O

Distress

3. Capacity

Positive

M and O

Joy

4. Capacity

Negative

M and O

Frustration

5. Opportunity

Positive

M and C

Relief

6. Opportunity

Negative

M and C

Grief

Examples for these six possibilities: 1. A person is searching for valuables in an antique shop. He spots a painting which may be an early Van Gogh. 2. During a pleasant conversation (behavior X = to communicate) a longstanding friend makes an insulting remark. 3. For a long time an elderly couple has been saving for a vacation. They win a huge amount of money in the lottery. 4. A soccer team reaches the final of the World Series. One of the players sprains his ankle during training on the day before the match. Behavior X = playing the game. 5. Johnson, a highly motivated employee, discovers that he has two very important appointments on the same date and at the same time. Neither can be cancelled. Still puzzling how to solve this, Johnson receives a phone call: one of the parties has flu and cannot make it to the appointment. 6. Behavior X = to meet the children. The daughter of a poor Dutch family goes to Australia for a vacation, meets her future husband and decides to stay. The parents are informed that they cannot meet her for a long time.

The Triad model

These possibilities and their examples only address a limited range of emotions that may be Triad related. Other changes can be imagined like those involving two determinants (like a sudden and dramatic decrease of the Capacity and Opportunity levels in a high Triad volume situation). The emotion of happiness can be viewed as resulting from a sharp increase in a generalized Triad volume, while sadness is experienced in the opposite case. If an individual is forced to show a behavior with a very low Triad volume, the emotion s/he experiences is disgust. P85 More specific emotions may be identified when refinements are made within the Triad approach. Denton (2006) makes a distinction between externally and internally determined emotions that resembles that between intrinsic and extrinsic aspects. He suggests that love, anger and fear are examples of externally determined emotions, while fatigue, hunger and pain are examples of internally determined emotions. P86 When a sudden and considerable change takes place in the Triad profile involving intrinsic and extrinsic aspects, an emotional reaction may be expected. For example, someone receives substantial help when he himself feels that this help is not needed and is even counterproductive. Or a person is repeatedly urged to work harder, even though the intrinsic Motivation is already sky high. Substantial and sudden extrinsic influences are regarded as intrusions on one’s Triad balance. A person may experience the emotion of fear if intrinsic determinants of an important behavior are suddenly overtaken by constraining extrinsic determinants of Capacity and Opportunity. P87 When the nature of emotions (anger, disgust, etc.) is to be determined, attributions should be taken into consideration. For example, a person is confronted with sudden, sizeable and negative change of the Triad volume of an important behavior X. An elderly lady is reading a book in the living room of her home in the forest. It is way past midnight. Suddenly the lights go out. The Opportunity to read, which was generously present, drops immediately, dramatically and unexpectedly. The nature of the ensuing emotion will differ on the basis of the attribution made. If she attributes the event to an accident (a power failure due to a storm), she merely experiences discomfort, no emotion. If it is attributed to a recurring mechanical

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failure, she is irritated; if she attributes it to a criminal intent of an unknown person sneaking around the house, she experiences fear, and if it is her husband subtly signalling her to go to bed, it may be anger. P88 Emotional reactions may also be expected if a behavior outcome is important and the attribution needs to change substantially. For example, a person is engaged in a particular behavior that produces a very positive important outcome. This person may first attribute the outcome to himself – his intrinsic aspects. Strong positive feelings are the result. When he is forced later to change the attribution to extrinsic Triad aspects, he is disappointed. See the following matrix for an inventory of four attribution possibilities:

Change from intrinsic to extrinsic attribution

Change from extrinsic to intrinsic attribution

Positive behavior

Negative behaviour

outcome

outcome

1

2

3

4

Cell 1:

Negative emotion: disappointment that the person is not responsible him/herself for the positive result.

Cell 2:

Positive emotion: relief that the person him/herself cannot be held accountable for the negative result.

Cell 3:

Positive emotion: pride that the person him/herself is responsible for the positive result.

Cell 4:

Negative emotion: shame that the person him/herself can be held accountable for the negative result.

At first sight, it seems possible to conclude that the Triad model may be related to different types of emotion. As the model is positioned at a highly generic level, the emotional reactions are also of a very generic nature. More specific emotions can be identified if additional concepts, like attribution, are included in the analysis. However, it is important to note that these

The Triad model

concepts are not considered in isolation, but in the context of the more generic Triad behavior determinants. It thus appears that the Triad approach and more specific concepts can mutually complement each other. Individual differences No two persons are alike, so inter-individual differences cannot be ignored in the analysis of human behavior. These differences are responsible for the fact that it is impossible to precisely predict and explain behavior X of person Y at moment Z. People differ from each other but they do so in a more or less consistent way. This is denoted by the concept of personality. A person’s personality is considered a stable structural profile or characteristic. Research has identified hundreds of different personality dimensions. In this area a serious attempt at simplification has been made by reducing the number of highly specific dimensions to a limited set of major ones. The notion of the ‘Big Five’ (e.g. Barrick and Mount, 1991) refers to five major personality dimensions (not to be confused with a similar notion that refers to the large animals of African wildlife). The five dimensions are Extraversion, Emotional stability, Agreeableness, Conscientiousness and Openness to experience. An individual person can be positioned on each of these dimensions. The complete profile represents his/her unique psychological make-up. It describes how the person generally deals with life and with his/her natural, physical and social environment. Personality is a relatively important concept. For example, as compared with demographic and socioeconomic variables, which together explain only about 10 per cent of the variance of psychological wellbeing (Argyle, 1999), personality variables (in this case extraversion and neuroticism) explain about 20 per cent (Abbott et al., 2008). P89 Individual persons may act consistently in relation to particular conditions and changes in the combination of the three Triad values. This is also likely to be apparent in personal preferences for such conditions and changes. Their behavior will differ systematically from that of other persons. Personality variables have already been identified for many of these systematic tendencies. This is not the place to draw up a systematic overview, but rather to show that individual differences are also likely with regard to the Triad determinants. For example, people may consistently differ from other people with regard to: • Their need for a larger Triad volume before engaging in behavior.

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• Their interest in growth (the increase of a generalized Triad volume). This interest may be compared with the Need for Achievement (Atkinson, 1964). • Their adherence to a particular behavior before contemplating a switch to another behavior; ‘behavior loyalty’. • Their dedication to preserving a balance between the Triad values. • Their sensitivity to balance changes. • Their tolerance of Triad imbalances. • Their willingness to set a secondary balance effect in motion in the case of Capacity/Opportunity constraints (willingness to give up). • The strength of their (emotional) reactions to sudden and large Triad value changes. • Their requirements concerning the relation between intrinsic and extrinsic aspects. Some people are more prone to receiving help from others; others have a strong and consistent tendency to act autonomously. • Their tendency to attribute outcomes to intrinsic as compared to extrinsic aspects for each of the three Triad determinants. See also the concept of Locus of Control (Rotter, 1966). The reason for discussing personality variables here was to show that the Triad approach does not appear to conflict with the important area of research on individual differences. However this area too is the victim of fragmentation. The introduction of the Big Five was a major step towards integration. The Triad approach can possibly provide yet another way to prevent detail overkill. It may also show that personality dimensions that have been presented under different labels in the literature are in fact closely related or even overlapping. In addition, the model emphasizes that personality dimensions that relate to either Motivation, Capacity or Opportunity should not be considered in isolation. For example, the explanatory potential of the Big Five may vary substantially over different Triad conditions.

Triad model conclusions There is no logical end to a discussion on the understanding of behavior and there may never be one. The same is true of the Triad model. So this specific point serves as well or badly as any other for arriving at a provisional conclusion of this book. The most important question is whether conclusions can even be formulated at all. Strictly speaking, the answer is negative. What has been presented in the previous chapters is a generic, hypothetical approach in the form of a set of interrelated propositions. However, these propositions present only arguments, not evidence. They are neither verified nor falsified. Propositions are presented on the basis of the correct or incorrect assumption that they have some inherent logic or psycho-logic. Individually, these propositions may be unstable and vulnerable, but taken together they seem to form a stable theoretical structure. We have built a house of cards. Like the individual propositions, the overall structure is also hypothetical. Although informal, ad hoc evidence has been gathered in practical situations, very limited evidence has been generated in rigorous empirical tests, let alone published in academic journals. The decision to develop the theory by checking it informally in practice before subjecting it to formal empirical tests was taken deliberately for reasons discussed earlier. That decision seemed justified after having observed how many formally substantiated findings reported in the literature prove to be stillborn in reality. The development of the Triad approach originated from a growing concern about the progress of the discipline, its societal reputation, its contribution to the understanding of behavior and the increasing distance between academics and practitioners. After a trade-off of possible alternative approaches, the approach selected comprises three main behavior determinants. Their combination has a lengthy but ultra-thin history in the academic literature. The publications concerned were infrequent, scattered over different areas and used different labels for the combination. They pre-

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dominantly referred to a framework for structuring variables rather than an elaborated theoretical model. The present Triad approach is an attempt to show how the framework might be expanded to a theory. It presents a multi-layered perspective on behavior, ranging from crude simplicity to moderate complexity. The level of simplicity/complexity is a matter of choice. The basic, simpler notions of the model may suffice for practitioners; the more complex version may be interesting for academic researchers although for them the theory is far from complete. While at the more generic levels many questions remain to be answered, at more detailed levels the open ends are innumerable. At the end of the day it is an economic question whether the benefits of developing highly detailed and volatile knowledge exceed the societal costs. Lewin (1943) already noted that behavior is the result of the interaction between the person and the situation. Somehow, behavior researchers seem to have interpreted this as indicating that any study on that interaction would contribute to a deeper understanding of behavior. They may have been right and may still be right. However, if we consider the hundreds of thousands of publications, not counting the even higher number of studies that have been conducted, it makes us wonder whether it was worth the costs in terms of energy, time, money and opportunity. Over the years, psychology did expand our knowledge of how the human mind works and has contributed to the solution of societal, organizational and individual problems. But on the whole it seems hardly justifiable to claim that the discipline has been very effective and efficient in doing so. Considerable parts of the total yield coincide with common sense (confirming intuitive knowledge held by naïve psychologists), are troubled by inconsistencies or are so specific that they at least give the impression of being trivial. This provided the motive for trying to discover the basic determinants of basic behaviors under basic conditions – and to identify the DNA of behavior. In the course of their history, human beings have had to struggle to survive, to adapt to their circumstances, to cope with threatening and risky situations, and to try to grow in order to master and improve their living conditions. It was presumed that, if there is any foundation for behavior, it ought to be found in these basic behaviors. Another assumption was that if particular principles govern lower, simpler levels of behavior, there is no a priori reason why these same principles should not be important for the explanation of higher order, mental behaviors. In neurological terms: the basic principles that steer behavior at brain stem level are possibly the same principles that

Triad model conclusions

operate at the level of the limbic system and the cortex. After all, the fact that we can distinguish them anatomically does not necessarily mean that their basic functioning differs as well. In the more sophisticated neurological systems that characterize higher-level organisms, the basic principles may only operate in a more intricate way. This might imply that we, as human beings, behave fundamentally in the same way as our ancestors did far down the path of evolution and even largely in the same way as other, lower positioned organisms do. That different species translate the principles into different concrete acts does not necessarily mean that they behave fundamentally differently. When interacting with their situation, people behave on the basis of their perception, interpretation and evaluation of goals, instruments and external, environmental conditions, whether the behavior is to survive, to cope or to grow. The Triad model converts this notion into the three basic determinants of Motivation, Capacity and Opportunity. Motivation refers to the goals, Capacity refers to the instruments and Opportunity refers to the external conditions. The interpretation of the interplay between these basic variables determines the likelihood, quality, intensity and persistence of behavior. In essence, the Triad model is an economic psychological theory because it concerns the way human beings deal with scarce resources in their facilitating or restraining environments. The Triad model was introduced in Chapters 4 and 5 as a remedy against the undesirable side effects of fragmentation discussed in Chapter 3. The side effects were structured by using the following categories of issues: meta-theoretical issues, theoretical, methodological/analytical issues and application issues. Let’s now look at the extent to which the Triad model might provide a provisional answer to these same issues and possibly suggest additional insights. We will focus on the main arguments. Meta-theoretical issues If the various schools and approaches in psychology were to position themselves vis-à-vis the Triad model, it might stimulate integration and reduce fragmentation. The model itself implicitly stimulates the synthesis of insights from different disciplines, not in the specific way suggested by Wilson (1998) but possibly with the same result. (Wilson introduced the term ‘consilience’: ‘a jumping together of knowledge by the linking of facts and

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fact-based theory across disciplines to create a common groundwork for explanation’ (p. 8)). In general, the Triad approach argues that focusing exclusively on more specific levels of understanding behavior is futile if it is not backed up by a general theoretical framework. So according to the model there is no question of whether such a framework is useful or not; it is essential. Without it, fragments of knowledge end up in a vacuum where they can only hold on to each other. For example, research on attitude-behavior relationships has been going on for a century (see, e.g. Johnson and Boynton, 2009) but still continues to run around in circles. What might possibly persuade attitude researchers to realize that they are hopelessly caught up in a self-constructed labyrinth? Many decades ago, psychology adopted the positivistic paradigm of the natural sciences as the predominant approach. In retrospect, the decision to put an almost exclusive emphasis on this paradigm may have been a tragic one. Academic rigour is not guaranteed by acting rigorously. Reliability and validity cannot be substantiated by figures only. Attempts to aim for detailed precision in psychological research may just not be a feasible and sensible goal, given that behavior is a highly complex, dynamic, untransparent and versatile phenomenon. In the same vein, it is not very effective to study the grand movements of celestial bodies with the help of a microscope. General insights obtained through relatively crude metaphorical representations of behavior seem to reflect a better correspondence between object and method. The notion of the homunculus was discarded long ago, but may experience a rebirth in the form of a humble, highly flexible Triad figure. Theoretical issues It seems possible, without twisting theoretical arms, to bring together different psychological concepts in the Triad theoretical framework. As became apparent in the substance of previous chapters, concepts like reactance, self-confidence, dissonance reduction, balance effects and attributions are in ready accordance with the model. It may be a sign of its integrative potential. The Triad model may furthermore help specify under what general psychological conditions more specific psychological concepts do or do not ‘work’. To refer once more to the attitude concept, this concept focuses only on motivational aspects. Its relationship with behavior may be better understood if Capacity and Opportunity values are taken into account. The

Triad model conclusions

same notion applies to the concept of intention. As indicated earlier, intention explains only roughly 30 per cent of the variance of behavior (Madden et al., 1992), which means that when people indicate that they intend to do something, they are more likely to do something else. This concept relates exclusively to Motivation. Additional information on Capacity and Opportunity to engage in behavior X might help us to understand the conditions under which intentions lead to or do not lead to behavior X. The psychological literature is replete with inconsistencies regarding the effects of single, isolated variables. The Triad model explicitly argues that the impact of such variables is dependent on the specific Motivation, Capacity and Opportunity conditions that form its psychological context. If this context is not known, then how can we know what a concept – that is, any concept – stands for? See also the methodological issues. Balance proved to be an important concept in the theoretical elaboration of the model. It reflects, with regard to a particular behaviour, a harmonious relationship between the person and his/her external environment. A situation characterized by balance provides the optimal use of scarce resources: not too little and not too much relative to the goal to be attained. It is possible that, in the evolution of species, organisms learned that balance provided the best opportunity for survival and coping, and served as a stable basis for growth. The Triad approach distinguishes different aspects of behavior and relates them: likelihood, quality, intensity and persistence. These behavior aspects and affect (satisfaction; wellbeing) are seen as two sides of the same coin. In the Triad approach, a distinction is made between the person’s perceptions and attributions of his/her inner and outer world. In that sense, it may provide a useful framework for (re)structuring and reinterpreting psychological insights. Methodological and analytical issues From a Triad perspective, psychological research is subject to more limitations than is generally presumed. The first limitation is that research significantly reduces the degree of freedom of its participants’ behavior. This artificially increases the likelihood that the behavior of interest to the researcher becomes, indeed, the focal behavior of the participant. The researcher decides on the behavior that experimental subjects or respond-

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ents need to show. Through a carefully designed procedure the participants are manoeuvred into the research trap that, after shutting them in, permits no escape to alternative behaviors. The person is free to select responses, but only within the constraints of the behavior that happens to be the focus of the study. In Triad terms: the volume of the criterion behavior is increased relative to the volumes of behavior alternatives. If we take behavior X to be ‘perform in an experiment’, we can assume that the subject’s Motivation, Capacity and Opportunity values in the procedure differ from those in real life. A subject’s Motivation probably increases by social norms to comply with the researcher’s instructions and the commitment to be a good participant. The interaction in a behavior study is based on an implicit social contract. Capacity may be relatively high in a research situation because of increased mental concentration. Or Capacity may be relatively low, for example when the participant is required to imagine real life situations only on the basis of written text. Finally, the Opportunity to engage in the specified behavior may differ from the Opportunity available in real life circumstances. There may be more or less time, and the room in which the experiment is conducted usually differs from real life conditions (‘Silence! Research in progress’). In survey research the situation is not significantly different. Participants are extra motivated because of being selected to participate, because of the nice interviewer or because of the nice gift. Capacity is artificially increased through questions that indicate precisely what information the person should take into consideration and contemplate. Questionnaire items contain information that respondents might not be able to produce spontaneously. In fact, the material provided may even produce new insights. Opportunity may be increased in two ways: first, the amount of time respondents are given to consider possible responses may be greater than would be the case in reality. Second, theoretically relevant notions are positioned side to side in the questionnaire, suggesting at least some relationships among them. Alternatively, people may take limited time to arrive at complex decisions in the laboratory while, in reality, they might stretch the decision process over a lengthy period. It is easy to see how the argument on methodological limitations can be extended to other methods including qualitative methods. Most of these limitations do not apply to the same extent in the case of unobtrusive measures.

Triad model conclusions

Thus in various research methods the values of Motivation, Capacity and Opportunity to perform as a participant are at serious risk of being either artificially increased or decreased relative to their respective counterparts in reality. Already more than half a century ago Orne (1962) introduced the notion of demand characteristics that might bias research participants’ behavior. This notion receives little explicit attention in the literature despite the possibility that we are dealing with a massive, systematic, fundamental and variable bias throughout the discipline. It may explain a considerable portion of the observed inconsistencies, just like it might explain misleading consistencies. Studies that deal either with Motivation, Capacity or Opportunity usually focus on only one of these factors. The often implicit ceteris paribus clause comfortably assumes that the other two generic determinants make no difference. It is like trying to establish the boiling point of water empirically, while ignoring the possibility that this point may depend on the quantity of water, the amount of heat applied, the altitude and the purity of the water. To their surprise, researchers continue to find different boiling points in different tests. So we end up with a wealth of publications based on data that have been carefully collected in meticulously designed studies, while there is incomplete information on the psychological conditions under which the data were gathered. This generates unanswered questions and inconsistencies that spawns even more research with unanswered questions and inconsistencies. This way, research is a self-propelling and self-maintaining phenomenon that is best compared with a perpetuum mobile. In this neverending circular motion publications invariably end with the claim that ‘more research is needed’, but what for? Ideally, a correlation indicates the existence of a relationship between two phenomena. However, correlations or comparable association measures may also arise from the mere attempt to study the relationship. For example, a researcher is interested in finding out whether a relationship exists between phenomena B and L. She includes written versions of B and L in the research material that is to be evaluated by research participants. These persons may not experience a relationship between B and L in real life, simply because they never give it a thought. In the course of the research process, however, they may realize that there is a relationship because they are required to react to both B and L in the same situation and within a short

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period of time. The concepts of Motivation, Capacity and Opportunity raise fundamental reliability and validity issues. At this point it is relevant to consider the Triad implications for doing Triad research. Here the model seems to bite its own tail. It suggests that traditional methods cannot be used for doing Triad research. If Motivation, Capacity and Opportunity are affected by research settings, then how can Motivation, Capacity and Opportunity be measured in a reliable and particularly valid way? To take a concrete example: if in reality a person decides on a particular behavior impulsively, it is impossible to confront this person with a questionnaire with a number of questions on each of the three determinants. The Motivation, Capacity and Opportunity to think about the behavior might be quite different from the same determinants in reality. Moreover we would need to consider (and correct for) the differential effect of the Motivation, Capacity and Opportunity to complete the questionnaire. From a Triad point of view, traditional empirical research cannot be used to verify Triad propositions. Sometimes (but surprisingly infrequently) credibility concerns are expressed from within the discipline. For example, Kahneman (2012) calls for replications of studies in order to lift the credibility of the field (in this specific case referring to the area of social priming). He does so in an open e-mail to researchers. He proposes a ‘daisy chain of labs A-B-C-D-E-A, where each lab will replicate the study selected by its neighbor: B replicates A, C replicates B etc.’ He shows how the replications should be done technically and advises how to publish all results, whether mutually confirming or not. If Kahneman’s advice were to be followed, it would entail a lot of (extra) work, and the rigour of the ‘daisy chain’ would undoubtedly contribute to the credibility of research outcomes. However, replications do not exclude the risk that built-in biases are replicated at the same time. Replications may be contaminated by disproportionally high, disproportionally low or differing levels of Motivation, Capacity and Opportunity across studies, which might engender either Type I or Type II errors. (A Type 1 error is the suggestion that there is an effect while there is in fact none; a Type 2 error is the suggestion of no effect, while in fact there is one). So while replications may increase research credibility, they do not necessarily increase research outcome validity. In the end, an incredible invalid outcome is preferable over a credible invalid outcome.

Triad model conclusions

Application issues At the very least, it is important to emphasize that behavior is seriously underestimated as a critical determinant of policy effectiveness. Policymakers often ignore the need to identify the desired behavior explicitly. They merely imply it. This rules out a careful analysis of the relevant behavior determinants, and renders policy making a rather sloppy affair. Almost invariably, practitioners judge the Triad model to be highly plausible. When they are first confronted with it, many claim that they already use it, ‘albeit implicitly’. They cite various examples where they use motivational measures, capacity related measures and measures of time and space. However, if their claims are checked, it becomes apparent that the use of the simultaneous combination of determinants is extremely rare, and that if the combination is addressed at all, the three determinants are addressed sequentially rather than simultaneously. Motivation is the most popular determinant by far, followed by Capacity. Opportunity is given the least consideration. What is more, there is a tendency to overestimate the values of the Triad determinants of the critical behavior, leading to overconfidence on the part of the policymakers and underperforming policy measures. The analysis in this book focuses on individual behavior. The Triad model can also be used and has been used to analyse behavior in dyads, for example in conflict situations. The question then is why and how the parties react to each other. If we take ‘to cooperate’ as behavior X, a conflict involves a serious mismatch between the respective Triad values of parties A and B. In fact, the Triad value of one party presents the Opportunity value of the other party. If A wants to cooperate and B does not, A has an Opportunity problem. Balance effects suggest why a reduced level of Motivation of one of the parties may lead to a chain reaction of value reductions for both parties, resulting in a complete lack of contact and communication. The Triad model also suggests that it makes no sense to try to solve a conflict through an approach that would raise particular Triad determinant values more than others. The Triad conditions of both parties have to be lifted, simultaneously and in a balanced fashion. Otherwise, the correction attempt would prove futile. In general, high quality cooperation is difficult to achieve. In the case of cooperation between two parties no less than six (!) conditions have to be met (2x3 Triad values). The Triad model can be applied to group behavior, but we need to realize that the Motivation, Capacity and Opportunity values of the group are

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dependent on the same three values of the behaviors of individual members. In the case of teamwork, different tasks and behaviors of different persons all contribute in a specific way to the overall result. For each behavior a Triad analysis can show what contribution can be expected and where additional support is needed. In teams where members have complementary tasks, a single Triad value of a single behaviour of a single member may ruin the performance of the entire team. The Triad model may also be applied to other ‘behaving’ or ‘moving’ entities. If we view an organization as engaging in strategic movements, the Triad model may represent its mission (Motivation), its core competences (Capacity) and its market conditions (Opportunity). Here we see a parallel, albeit a partial one, with the popular and traditional SWOT analysis (e.g. Learned et al., 1965). This instrument simultaneously considers an organization’s Strengths, Weaknesses, Opportunities and Threats. Note that the combination of strengths and weaknesses is the equivalent of Capacity, while the combination of opportunities and threats can be seen as similar to Opportunity. The basis of the analysis is the so-called confrontation matrix in which strengths and weaknesses are crossed with opportunities and threats. Remarkable combinations in the matrix suggest major strategic issues that can be used for generating strategic options. Although there is a clear correspondence between the Triad model and the SWOT, there is an important difference as well: in a SWOT analysis the motivational component is ignored or undervalued. Motivation may be represented by its mission or vision (intrinsic Motivation?), leadership (extrinsic Motivation?) or stakeholder pressure (extrinsic Motivation). The lack of explicit attention to Motivation seriously restricts the potential role of this key variable in strategy formulation. It seems impossible to make sense of the SWOT elements if there is no indication of the drive and direction of the organization. So users of a SWOT analysis may risk generating strategies that are fundamentally misguided. The objection might be raised that the motivational component is implied in the other SWOT elements. However this would be comparable to suggesting that Motivation is implied by Capacity or Opportunity in the Triad model. In the various propositions and examples it has been argued that Motivation deserves a role of its own. It seems justified to include a distinct motivational component in the SWOT analysis too. A correction can be made by adding a third dimension to the confrontation matrix, distinguishing positive and negative drivers. This way, the 4-cell confrontation matrix is transformed into an 8-cell confrontation cube that

Triad model conclusions

may point at additional or other strategic options than would be suggested in the conventional 4-cell confrontation matrix. Segmentation is important in policy making. It allows for structural differences between relatively homogeneous groups of people. Segmentation provides for differentiation at a higher level of aggregation when it is too expensive to address individual persons. Segmentation is often based on geographic, demographic and/or socio-economic variables; sometimes the basis is formed by psychological or behavior variables. If the policy’s success is highly dependent on the behavior of the target population, it may be worthwhile considering the possibility of a segmentation based on Motivation, Capacity and Opportunity differences with respect to the critical behavior. This avoids people being addressed for the wrong reasons; it helps fine-tune policy measures; and it avoids the risk that a segmentation based on conventional criteria does not match the segmentation that would be required on behavior – that is, psychological – criteria. The Triad model is presented as a three-factor model. At the same time, it was stressed that Motivation, Capacity and Opportunity values tend to change over time and that balance effects may result in changes within the system. This calls for a dynamic approach to behavior. The Triad approach actually involves four dimensions: the three Triad determinants plus the factor time. For practitioners it may be important to monitor the three values over time. Changes in the values often surface in balance effects and in changes in behavior likelihood, quality and persistence. A policy that once was successful may not continue to be so; past success does not imply future success. Additional policy measures may be necessary to retain the original values.

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Overall conclusions The book started by emphasizing the importance of behavior. We looked at different attempts to explain behavior and considered psychology as an academic discipline with responsibility to advance its understanding. We provided an inventory of important theoretical and practical contributions. We discussed the development of the discipline, which led to the conclusion that the contribution to behavior understanding is impressive but shrinking dramatically over time. We accused fragmentation of being the most important contributor to this problem. We suggested that a general theoretical framework might help to compensate for fragmentation, while such a framework is not available. Attempts to present a framework are scarce and scattered throughout the discipline. This called for the development of an approach that might possibly be functional. This approach was not developed completely from scratch, but was built on the basis of earlier publications concerning the importance of three central variables for the explanation of behavior. The model that emerged was dubbed the ‘Triad model’ to emphasize the importance of the three variables, and at the same time to distinguish it from existing three factor approaches. The reasons for the approach were discussed elaborately and will not be repeated here. It was interesting to see how a very simple and straightforward theoretical framework may be used to provide a provisional explanation of very basic behavior. Also, with the help of a general theory, it seems easier and more justified to interpret the behavior effects of individual variables. In this way the Triad approach could accommodate for effects like dissonance reduction, fear of the unknown, resistance to change, learning, attitude-behavior relationships, intention-behavior relationships, satisfaction and wellbeing, emotions, reactance, the role of feedback, prospect theory, etc. It is important to note that the Triad model is intended as a supplement to existing approaches. In no way is it suggested that it might serve as a

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replacement. Highly specific, technical research and aggregate level research should exist side by side and interconnect wherever possible. They can offer each other a lot. The Triad approach suggests that there are structural biases in psychological research and that, in consequence, serious questions ought to be raised with regard to the reliability, validity and generalizability of findings reported in the psychological literature. Even though this is not stated as a conclusion, the suggestion itself is far-reaching and calls for a closer look at the credibility of the Triad approach itself. Academics tend to react to the model in either of two ways: either they embrace its simplicity, its potential for integrating existing knowledge, its suggestion of new insights, its call for the reduction of fragmentation and its ambition to serve both academics and practitioners. Or they discard it at first sight, claiming that the complexity of behavior cannot be captured in three variables only. On the one hand the Triad approach seems promising. On the other hand, several arguments should be mentioned that call for caution and restraint: • The Triad approach seems plausible. To most people it is intuitively appealing and corresponds to common sense. But it is here that a serious danger looms, knowing how easily our own intuition and common sense notions can fool us. The function of psychology is to question what seems self-evident and produce evidence on the counter-intuitive, not just to corroborate what we already know and understand from experience. The aggregate nature of the model may elicit ‘hineininterpretieren’, a phenomenon by which people selectively attend to, interpret or transform information in order to make it fit with their own preconceived notions. Fortunately, that risk is reduced somewhat if people are aware of it. • The Triad model is not supported by evidence in the positivistic tradition. This might be a reason for traditional academics to ignore it altogether. The dominant paradigm requires empirical evidence for every theoretical claim, however small. The approach presented here is a constellation of propositions, no more. It is a house of cards, not bricks. According to present standards it does not qualify as an academic argument. What is more, this new, ‘naked’ theory has the arrogance to comment critically on a longstanding discipline, generously festooned in multiple layers of empirical evidence. But the role of evidence may not be as straightforward as it appears at first sight.

Overall conclusions

•

•

•

•

The strongly evidence oriented natural sciences provide examples of important theories that were introduced and elaborated without direct and immediate evidence. For example, Einstein’s relativity theory, quantum theory and more recently string theory (the ‘Theory of Everything’) might not have been presented if they had been required to be evidence based from the start. Apparently, evidence has a different function at different phases in the development of theory. In the initial phases, evidence may be counterproductive by blocking innovative thinking. In later phases, evidence is essential to prevent theory from pursuing a runaway course on its own. The Triad model is incomplete. The way it has been elaborated in this book was meant to show that it is possible to ‘play’ with a limited number of variables. But the point at which elaboration provides closure cannot be determined. It simply may not be there. In the ideal case, a more elaborated model ties in with already available, more specific psychological insights. The model originated from the idea that simplicity might be the answer to the overwhelming complexity of the discipline. The various propositions showed however that its complexity rapidly increases when it is elaborated further. This is no problem as long as simplicity structures complexity. A highly generic model cannot contain a lot of information. By aggregating over variables, specific information is lost. This is the price to pay for simplicity. An attempt was made to solve this problem by presenting an ‘elastic’, multi-level model that can be stretched depending on the theoretical need of the behavior analyst. The Triad model does not come with a well-established method. It is important not to give in to the methodological reflex to develop psychological scales for Motivation, Capacity and Opportunity, as such scales might be in direct conflict with the very principles of the Triad model. We would need scales not only for Motivation, Capacity and Opportunity concerning behavior X, but also for the Motivation, Capacity and Opportunity to complete these scales, and so on. This calls for the development of a new methodology. Another complication that needs to be dealt with is presented by the idea that Motivation, Capacity and Opportunity should be operationalized idiosyncratically, that is, in direct relation to each particular behavior X. For example, the Capacity to climb the Mount Everest cannot be studied with the same instrument as the Capacity to grow strawberries or to remove an appendix.

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In spite of these cautions, the potential of the approach gives rise to some recommendations. General and Triad model related recommendations are given in the next and final chapter. The recommendations which are directly related to the model are as provisional as the conclusions on which they are based.

Recommendations 1. Stop fragmentation. It transforms the discipline into a bottomless pit for l’art pour l’art publications. It causes further damage to its societal reputation. It wastes intellectual talent and it increases opportunity costs. Fragmentation drives out integration. Without an integrative framework, or without a relentless attempt to develop one, the position and status of psychology as an academic discipline is unclear – and its future position uncertain. 2. Change the publication system to one that provides incentives for substantial contributions to behavior understanding and that discourages perverse publication activities. The current system is like an intellectual straightjacket, forcing people to follow the rules of convention and discouraging them from thinking innovatively. The academic system frustrates academic work. Too often, small, incremental improvements are confused with true disruptive innovations. 3. Reassess the function of empirical evidence at various stages of theory development. Here we argue for a supplementary approach in which the conventional order of psychological inquiry is reversed: first establish an overarching theoretical framework, then assesses its stability on theoretical grounds and finally submits its implications to an empirical test. 4. Use part of the time and effort that is now spent on reviewing submissions for publications (after the fact) on mutual support during theory construction. Academic performance criteria should be adapted accordingly. 5. Critically assess the Triad model’s propositions. Provide positive and/or negative comments. If there are reasons to continue with the model, contribute to its further development. Also consider possibilities for the construction of a new methodological approach.

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6. The Triad model is not introduced as ‘the’ alternative approach. It is to be viewed as an example of an alternative approach. If the model does not meet the requisite criteria, develop another approach in which simplicity is the starting point. 7. If the Triad model is judged acceptable, elaborate, adapt and refine it so that it can accommodate existing psychological theories and concepts. Use the Triad terms and figures consistently in order to avoid unnecessary confusion. Use them also in contacts with practitioners. Experience shows that this increases the Motivation and Capacity of mutual communication. Look for possibilities to provide for the third component – they are literally all over the place. 8. Assess the stability of available theoretical insights under different psychological conditions. This can be done in various replications, each having a different combination of Triad determinant values. Important psychological phenomena may crumble if one of the Triad values is low. Replace the ceteris paribus clause with information on the psychological conditions under which research findings are obtained. Require researchers who wish to use the clause to specify exactly what they hold constant: Motivation, Capacity and/or Opportunity. 9. Develop, in close correspondence with a multi-level theory, a multi-level methodology. Always combine more detailed level research measures with aggregate level measures so that all data can be related to the same general framework. Only in this way can comparisons, critical for preventing fragmentation, be made across studies. And only in this way can more aggregate research coexist with more specific, detail-oriented research. One thing is clear: it makes no sense to study the effect of a variable that operates on its own, whatever the variable and whatever the behavior effect. 10. Develop a method that precludes Motivation, Capacity and Opportunity biases. See unobtrusive measures as providing the key methodological instrument. Supplement such measures with control conditions under which the levels of the M, C and O values may be checked. 11. Develop an assessment instrument that estimates the Triad values in a way that is not in conflict with the model itself. Simplicity, again, should be the guiding principle. 12. Policymakers should explicitly (re)install behavior as a component in policy development. 13. Leadership and empowerment are hot topics in contemporary management. Give these concepts a deeper meaning with the help of the Triad model. For example, a leader is not just a bundle of competences as is

Recommendations

often implied. A leader only leads by way of his/her behavior. Different situations call for different leadership behaviors (see Caris, 2011). These should be specified. For behavior X the three Triad determinants should have high values: s/he needs the drive and ambition (Motivation), the relevant competencies (Capacity) and Opportunity at the same time. Many wannabe leaders possess only one or two of these characteristics or apply them consecutively. This makes them leaders only when the three determinants of ‘leading’ happen to combine accidentally. Behavior X – ‘to lead’ may amount to stimulating (Me), supporting (Ce) and facilitating (Oe) the behavior X of followers to the extent that their intrinsic aspects are not sufficient for this relevant behavior to occur. Empowerment can be interpreted in approximately the same way. It is not simply realized through the transfer of formal responsibility. So specify the relevant behaviors (what should the empowered person DO him/herself autonomously?) and check whether the Triad conditions for these behaviors are met. 14. Practitioners who want to use the Triad model should do so with the help of the Triad figure. Practical experience shows that the model can be explained easily to people of many sociodemographic backgrounds. After all, it concerns these persons’ own behavior. The model has been functional in the interaction within organizations. For example, party A blames party B for lacking Motivation; party B blames party A for not providing sufficient Opportunity. The Triad model may not prevent conflicts, but at least it may help people to understand what the conflict is about. 15. Be reluctant to label an apparent lack of interest, ambition or enthusiasm automatically as a lack of interest, ambition or enthusiasm. Refuse to diagnose behaviors without considering all three Triad determinants and their balance effects. In many cases, the lack of Motivation turns out to be due to a lack of Capacity, a lack of Opportunity or both. As a result of quick and dirty diagnoses, a lot of human potential is lost, for no good reason. 16. Finally, play and practice with the model in a broad variety of situations. Most users can apply the basics but need to familiarize themselves with the more complex propositions. This book started with a number of examples. These originate from practical situations and have turned out to be excellent practice material in executive teaching. Readers are invited to tackle the examples themselves.

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Epilogue

This book is entitled: Redesigning Psychology; in search of the DNA of behavior. The title is ambitious and may seem somewhat arrogant. However, no arrogance is implied. Also in no way are critical comments on the discipline intended to show disrespect for academic researchers or other agents in the academic world. The book is meant as a humble wake-up call suggesting that the discipline is disintegrating and would benefit from a unifying theory, if on time. ‘Redesigning’ refers to a process and, in fact, that’s what it is. The Triad model as presented here is a provisional product. From a purely academic perspective, the model is far from being applicable; from a practical perspective, it is ready to go. The many positive reactions from practitioners stimulate its application. In fact, it is now being applied in many areas. The author experiences the tension between the academic and practical viewpoints. On the one hand, practice does not want to wait; behavior problems are just too large and pressing to wait for a definite academic solution. On the other hand, there is the risk of misguidance if the model is not as plausible as it may seem. The two perspectives form a perfect dilemma, but when sailing between the Scylla of fragmentation and the Charybdis of oversimplification, doing nothing is no option. Therefore, the ultimate recommendation is to apply the Triad model in practice, to be aware of its provisional nature, to scrutinize it academically, to develop it further and to mix it with existing insights. All at the same time. This book came up with the metaphor of a very lively Triad figure. It expands, shrinks, stirs, spins, tilts, pulses, inflates, deflates and collapses. Understanding behavior may boil down to interpreting its choreography.

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Redesigning implies rethinking, restructuring, repositioning and demystifying. Redesigning is an attempt to innovate. Unfortunately, an innovation is often confronted with resistance to change. A secondary balance effect?

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About the author

Theo B.C. Poiesz (1952) is professor of Economic Psychology and ActiZ professor of Health Care Management at the TIAS Business School of the Universities of Tilburg and Eindhoven in the Netherlands. In earlier phases of his career, he was professor of the Psychology of Advertising at Tilburg University and professor of Marketing/Consumer behavior at the University of Maastricht. Theo Poiesz is involved in executive education and serves as a consultant in various areas, dealing with issues relating to employee and customer behavior. One of his main topics is strategy and policy implementation. He is the author of books, book contributions, academic and professional publications.

Reactions, input, comments, experiences and examples with regard to this book and the Triad model are welcome at [email protected].