This chapter seeks to elaborate a theoretical model for applying behavioural insights to changing organisational behaviour, based on lessons from Industrial-Organisational Psychology.
Behavioural Insights and Organisations
Fostering Safety Culture
2. Beyond the individual: why and how behavioural insights can be applied to organisational behaviour
Abstract
The basis for examining the theoretical foundations for the application of BI to organisations for policy makers is two-fold. First, OECD (2017[1]) provides the first ever global collection of case studies on the application of BI to public policy, with over 110 cases across 11 policy domains. The majority of these cases are BI applied to individual behaviour, such as consumer protection/choice, tax compliance, sustainability, and retirement saving. This is perhaps unsurprising, as BI began as a discipline needing to prove its ability to provide a positive return on investment (Halpern, 2015[2]). However, regulatory policy includes policy problems that are often more complex than changing biases that drive errors in individual decision making; rather, they involve interactions between consumers, firms, government and the legal structures that govern the entire relationship. This is especially true for fields such as regulatory policy, where changing firm behaviour is often the primary goal of regulation.
Second, the natural follow up then is to examine how lessons learnt from influencing individual behaviour can be translated to influencing organisational behaviour, or if we need new lessons altogether. Fundamentally, when it comes to influencing behaviour, it is arguably less a matter of influencing whole organisations versus the people inside of them, and more a matter of influencing whole organisations via the people inside of them – and via the organisational policies and procedures set in place by decision makers. In the development of the Attraction-Selection-Attrition framework explaining how organisations evolve toward psychological homogeneity, organisational scholar Benjamin Schneider stated, “The people make the place” (De Cooman et al., 2009[3]); (Schneider, Goldstein and Smith, 1995[4]). Organisations are made up of people. By influencing the right number or types of people and/or by tweaking the right policy levers in organisations, whole organisations can change.
This logic is normal when viewed through the prism of organisational reform. Reviewing, analysing and providing recommendations for organisational reform is the “bread and butter” of the OECD, who engages in reviews of member and non-member countries across all policy areas and often with regards to one government entity, such as regulatory authorities. While not expressly behavioural, a natural component to any review methodology is an analysis of the routines, policies and procedures of the organisation in an attempt to discover possible misaligned incentives or missing policies that are affecting the good governance and performance of the organisation. Inherently, this is partly behavioural. For example, the Performance Assessment Framework for Economic Regulators (PAFER) looks at four dimensions of governance and performance: roles and objectives; inputs; processes; and outputs and outcomes (see (OECD, n.d.[5]). Each dimension looks at both structural barriers and behavioural barriers, such as the need for high quality strategic objectives. This is a necessary structural requirement for any organisation, while the process of developing strategic objectives involves engaging all levels of staff and external stakeholders. The manner and perception of this process inherently creates behavioural effects that may need to be managed and addressed.
For government then, the application of behavioural insights to organisations is often done by influencing specific individuals in those organisations to affect organisation-wide changes or by more directly intervening on organisational routines, policies, and procedures of the organisation. This chapter will provide the theory of how organisational behavioural can be influenced, and what lessons do policymakers need to learn in order to adapt BI to organisational behaviour. It was originally created as a background paper for the OECD-European Commission Seminar on “Behavioural insights and organisational behaviour” held on 10 May 2017 at the OECD Headquarters in Paris, France, financed by the European Commission Directorate-General for Regional and Urban Policy, and summarised in OECD (2018[6]).1
Applying Behavioural Insights to Organisations: Theoretical Foundations
When enough people are nudged toward behavioural change, those new behaviours have the potential to become habit, switching from deliberate choices and actions otherwise known as controlled processing, to less deliberate, less effortful, more habitual actions known as automatic processing. Whether deliberate or effortful, choice or habit, when enough people in a work group or entire organisation behave in a certain way, that behaviour has the potential to become a norm. Norms are rules for expected and accepted behaviour. As humans, violating norms tends to make us uncomfortable. We are likely to conform to the norms of our work group and organisation.
This is especially true of cohesive groups who feel a degree of attraction to their work group. Years of research in psychology support this assertion, which is also likely supported by the reader’s own work experiences. Within an organisation, people conform to norms as seemingly trivial as dress code and where to sit during lunch breaks to those more important such as communication styles and production rates. Thus, nudging a critical mass of people such that the “new” behaviour becomes a norm is a mechanism by which to nudge an entire organisation.
Nudging leaders is another way to nudge entire organisations. Leaders are those with influence. This includes those officially designated as leaders, as well as those with power and influence for other reasons. There are purported to be five bases of power in organisations: expert, referent, reward, coercive, and legitimate (French and Raven, 1959[7]); (Hinkin and Schriesheim, 1989[8]). Expert power arises from relevant knowledge of a subject matter: Someone with a unique and important knowledge base in an organisation possesses a particular type of power that can be influential. Referent power arises from being liked and admired by others. Reward and coercive power are often possessed by people in supervisory positions. These refer to the ability to reward others with bonuses, promotions, etc. and the ability to punish others with disciplinary actions, fines, firing, and the like. Finally, legitimate power is the power inherent in a boss or leader’s job title. People in organisations may possess one, some, many, all, or none of these bases of power and influence.
Nudging supervisors or other powerful or influential people within an organisation can have a multiplying effect such that the behaviours exhibited and endorsed by influential individuals have a better chance of being adopted en masse, nudging a whole organisation in the process. Indeed, charismatic and transformational leaders are believed to possess qualities that inspire followers to behave in desired ways in service of a larger goal. Nudging such leaders can effect largescale behavioural change.
Of course, those in formal leadership roles toward the top of the organisational hierarchy are also in a good position to effect widespread behavioural change by altering organisational policies and procedures. Nudges that help high-level decision makers (leaders, boards, etc.) optimize organisational policy decisions in the face of their own biases and irrationalities can have an effect. Thus, helping decision makers see the connection between policies, procedures, and behaviour on the ground is another way to nudge whole organisations. It has been argued that behavioural nudges should be part of policymakers’ toolkits (Guszcza, 2015[9]). This is just as true for those setting organisational policy as it is for those setting policy in other contexts such as government.
This last point warrants special emphasis. Organisational policies and procedures affect behaviour at work by shaping who is doing the work and how they are doing it. Organisations can be strategically and intentionally nudged by using behavioural science (theories, methods) to inform those policies and procedures. Said another way, behavioural insights can help organisations develop behaviourally informed policies and procedures. Rather than hoping for human behaviour that supports the policies and procedures in place, it is a matter of creating policies and procedures that will encourage the work behaviours that support the broader mission and vision of the organisation at hand.
So, can whole organisations be nudged? The short answer is “yes.” The longer answer is “Yes, but …” The caveat is this: In order to effect widespread organisational change, we need to incorporate and go beyond traditional nudge techniques used in behavioural economics.
In a sense, nudging entire organisations requires both a narrower and broader focus than we typically see in behavioural economics. The focus can be narrower in that it is constrained to work and organisational behaviour in particular, addressing the phenomena of greatest relevance and concern within the working context – worker and organisational wellbeing, work motivation, efficiency, effectiveness, productivity, and so forth. At the same time, the pursuit of organisational nudges requires a widening of the metaphorical aperture. The theoretical and methodological focus needs to encompass, incorporate, and integrate traditional behavioural economics approaches with years of science that has accumulated in other relevant fields, most notably industrial and organisational psychology, discussed next.
Industrial-organisational psychology
Traditionally, there has been a strong reliance on psychology in behavioural insights (BI) interventions and teams. Indeed, psychologist Daniel Kahneman, who won the 2002 Nobel Prize in Economics, is often considered the father of behavioural economics. Historically, insights from social psychology and cognitive neuroscience have been especially prominent in shaping BI theory and applications.
The neighbouring sub-discipline of Industrial-organisational (I-O) psychology becomes especially relevant as we more carefully consider how to use BI to enhance organisational behaviour. I-O psychology is “the scientific study of working and the application of that science to workplace issues facing individuals, teams, and organisations. The scientific method is applied to investigate issues of critical relevance to individuals, businesses, and society” (SIOP, n.d.[10]).
As this definition shows, I-O psychology is clearly at the intersection of BI and organisational behaviour. Accordingly, I-O psychology has a notable influence in this paper. As an applied discipline that closely adheres to a scientist-practitioner model, I-O psychology draws from and contributes to a range of neighbouring sub-disciplines in psychology and business. Academics in this field are commonly housed in business schools if not psychology departments and publish in business, management, psychology, and trade journals. Practitioners play a prominent role in government, military, non-profit, and for-profit entities. Increasingly I-O psychology is being applied directly to development goals and organisations, such as the United Nations.
In light of its increasingly prominent role in the public and private sectors, I-O psychology has recently been ranked among the fastest growing jobs in the U.S. economy by the Bureau of Labor Statistics (2014). All signs suggest that it is growing in other parts of the world as well, with recent years witnessing, for example, the first Ph.D. in I-O psychology awarded within the country of Ghana (Oppong, 2013[11]). Alternate names for I-O psychology in various parts of the world include Work Psychology, Organisational Psychology, Work and Organisational Psychology (WOP), and Business Psychology. In Europe, there is an Erasmus Mundus postgraduate program in Work, Organisational, and Personnel Psychology (WOP-P) devoted to building expertise in WOP-P for an increasingly globalised socio-economic world of work.
Industrial-organisational psychologists’ methodological toolbox is vast, including a mixture of methods to answer questions about work behaviour: lab studies, field research, Randomized Controlled Trials, quasi‑experimental designs, correlational designs, validation studies, and so forth. In addition, a mixture of analytics is also used: ANOVA, regression, multilevel modelling, factor analysis, cluster analysis, and a variety of other techniques. Because of the focus on applying psychological methods and theories to real-world problems, I-O psychology expertise has been included in behavioural insights groups, including on the White House Social and Behavioural Science Team (SBST; (Stillman, 2017[12]).
Methods for applying behavioural insights to organisational behaviour
Define, Diagnose, Design, Test
One important commonality between I-O psychology and behavioural economics is the define-diagnose-design-evaluate approach to implementing and testing behavioural nudges (see Figure 2.1). This entails, first and foremost, carefully defining and agreeing upon the institutional problem or challenge that needs to be addressed. Defining the problem in precise, concrete, behavioural terms can be difficult. It takes time, investigation beneath the surface, an objective consideration of a variety of alternatives.
Figure 2.1. Define, Diagnose, Design, Test Model
Source: Adapted from UNDP (2016[13]).
At times, behavioural scientists solicit subject matter expertise via informational interviews and observations during the problem definition phase of the process. Silva (2017[14]) summarises this process from experts and stakeholders working within and alongside the United Nations. In this example, participants with different perspectives (country offices, agencies, etc.) helped identify “pain points” and opportunities to strengthen innovation at the United Nations.
The second step is to diagnose: What are the barriers or bottlenecks discouraging the desired behaviours? What aspects of the environment might be reinforcing the less desired behaviours? Each stage of the relevant actors’ decision making process is examined here to determine what cognitive biases or other factors might thwart efficiency or effectiveness. In an organisational context, additional questions asked at this stage may entail whether there is a skill deficit, a barrier preventing skills from being utilised on the job, role ambiguity, a communication issue, a motivational problem, or a formal or informal reward structure inadvertently reinforcing less desired behaviours. This paper elaborates on some of the factors that should be considered when diagnosing effectiveness and efficiency problems at work.
The Critical Incidents Technique is a job analysis tool often used by industrial-organisational psychologists, which can be informative during the diagnostic phase of a change initiative. Developed in the 1950’s by John C. Flanagan, this well-established technique entails using a specific interview protocol to collect and document stories of effective and ineffective behaviours at work, related to superior or inferior performance (Flanagan, 1954[15]). This technique can be applied to individual or group behaviour. Through the collection and content analysis of hundreds of “critical incidents,” the organisation can get a good picture of behavioural bottlenecks ripe for intervention. The critical incidents technique has been used to teach best practices in a variety of industries including healthcare, counselling, and customer service (Rademacher, Simpson and Marcdante, 2010[16]).
The third step is to design an intervention to address the challenge at hand using what we know about human behaviour in a work context. There are a variety of nudge-style tools that may inspire relevant interventions at this stage, including simplification, active choice, the use of social norms, and implementation intentions. In addition to nudge-style tools, there are other distinct as well as some overlapping techniques commonly employed by industrial-organisational psychologists during the intervention phase of the process – including goal setting, redesigning work, and tweaking staffing, training, performance management, and reward systems.
The fourth and final step in the process is evaluation. To date, behavioural economists have relied heavily on Randomized Controlled Trials (RCTs) to test interventions. This entails exposing a random subset of the workforce to the intervention in question and comparing their behaviour to that exhibited by their counterparts in a control group that was not exposed to the intervention. There are clear benefits to using an RCT methodology to test an intervention. Internal validity is high. Causal relationships can be conclusively determined.
It should be noted, however, that organisational phenomena do not always lend themselves to the RCT methodology. Institutions interested in using behavioural insights to effect change will restrict what they can learn and change by limiting their interventions to those that can be tested via an RCT. A bigger methodological toolbox – one that includes but is not limited to RCTs – is arguably needed when applying behavioural insights to institutions and organisations. Indeed, Kanfer and Chen (2016[17]) conclude their review of work motivation with a discussion of the need to integrate emerging insights from various sub‑disciplines, emphasising the need for researchers to expand beyond the standard, time-limited, experimental paradigm. They “strongly encourage the use of multi-level, longitudinal field experiments, experience-sampling studies, and intervention studies to allow for the evaluation of motivational and behavioural variability as a function of time, work events, the individual’s history, and the social context of action – determinants of motivation particularly important for managing many modern organisational behaviour problem spaces, such as expatriation, work transitions, employee diversity, multi-team systems, and high-stakes work” (p. 15).
Behavioural design thinking
When crafting behavioural interventions, it is helpful to make use of several frameworks that have been used previously. The following is a sketch of the steps of thinking through how to apply behavioural insights in your work:
Define the Problem: Develop a clear understanding of a particular behaviour that should be increased or decreased. Frame the behaviour as much as possible by specifying who it is done by, where it occurs, and when it is usually done.
Diagnose the Barriers: Look for the barriers that are preventing the desired action or that are preventing a negative action from ceasing. It is often more productive to start by identifying existing barriers before moving to solutions. Types of barriers worth considering are:
1. Awareness: does the person or organisation know about the behaviour? Do they know that it is (un)desirable? Do they know the ramifications?
2. Motivation: what are the things that are making them unmotivated to act?
3. Behaviour: are there issues with the ease or complexity of the action itself that are preventing action even among those who are aware and motivated?
4. Continuation: does the action require continuous follow-up? Is it likely that the follow-up actions are forgotten due to long time lags? Are there motivational hurdles to sustained action?
5. Feedback: is there a lack of feedback such that individuals are not even aware that they completed the action? Is there feedback that might incentivize them to avoid future action or dissuade others from acting?
Design the Intervention: Look for behavioural insights that have been successful in previous cases that can help with addressing the identified barriers. When designing the intervention, it is useful to look for existing systems that can be adapted. In particular, look for existing points of contact with the audience, partners that can implement the desired change, and any data that can help target the intervention in a precise and personalised manner.
Evaluate: Test the effectiveness of the change to add to the knowledge of your organisation and the broader community about what works, when, where, and for whom. The use of behavioural insights is still in its early states and applications that have worked in different contexts with different populations might not work new contexts. Quick, low-cost, and rigorous evaluations can often be done by using existing sources of administrative data and will inform future policy-makers and practitioners.
Organisational behaviour through individuals: 8 things psychology tells us about work and organisational behaviour
Given the relevance of psychology to the world of work, there is value in highlighting key psychological insights that can be applied to those interested in nudging organisational behaviour. While an exhaustive list of relevant psychological theories is beyond the scope of this chapter, the following pages describe some of the more pertinent insights, which may directly or indirectly stimulate solutions to a variety of organisational challenges.
1. Rewards can motivate and backfire
Official and unofficial reward structures are important levers to examine when considering organisational nudges. Years ago, former president of the Academy of Management, Steven Kerr, wrote one of the most widely cited articles in the management sciences, which continues to be discussed and reprinted today given its relevance to modern organisational challenges. It is titled, “On the folly of rewarding A, while hoping for B” (Kerr, 1995[18]).
To motivate behaviour in the desired direction, reward structures need to be aligned with organisational objectives. Kerr provides examples from politics, medicine, the military, universities, sports, government, and business in which the types of behaviour rewarded are those we wish to discourage, while the desired behaviour is not being rewarded at all. For instance, he comments on governments’ distribution of funding, where the allocation of next year’s budget is often a direct function of this year’s expenditures. While the government likely hopes for economy and prudence in spending, it is rewarding just the opposite.
Kerr describes additional examples as well. He comments on the folly of rewarding individual effort when we hope for teamwork. This likely applies across institutions as well as it does within institutions. If success hinges on effective communication and collaboration across institutions but the workers in the institutions in question are responding to their own individual reward structures, effective collaboration is relatively unlikely. Another example from management relates to the leader who wishes to encourage employees to set challenging “stretch” objectives yet evaluates and promotes people on the basis of whether they have “made the numbers” and achieved goals. Such a reward structure encourages people to aim low.
It is not difficult to conceive of examples outside of government as well. For instance, imagine a university or scientific “think tank” hoping scientists will produce innovative research and development. Typically, researchers’ protocols need to be submitted and approved by an institutional review board (IRB) before the research can begin. IRBs that are overly concerned with staying out of legal trouble or avoiding “attacks” from external stakeholders concerned with unprecedented research protocols will tend to be risk averse, rejecting innovative protocols in favour of the status quo, or requiring modifications that not only create administrative roadblocks but would also render the research far less innovative than it could have been. Legal concerns and fear of external stakeholders may also lead to multiple audits or reporting requirements throughout the research process, which can slow down progress and distract the researchers from innovation. Thus, while the institution is hoping for innovation, it is rewarding safe, status-quo projects and time spent fulfilling reporting requirements.
Kerr (1995[18]) points out several causes of the inconsistency between what is hoped for and rewarded, including a fascination with “objective” criteria and an overemphasis on highly visible behaviours. Organisations need criteria by which to judge success, measure and reward performance. Often, this leads to countable metrics (money spent, time spent, forms completed, etc.) that are contaminated by factors outside of worker or project manager’s control and/or deficient in that they are not tapping into important components of effectiveness and work quality. This is often coupled with an undue emphasis on highly visible behaviours. Team building and creativity are examples of behaviours which may not be rewarded because they are difficult to observe and measure, even though they are important.
At a more micro (psychological, behavioural) level of analysis, we can consider how people will respond when organisational reward structures punish hoped-for behaviours. Learnt helplessness is a behavioural insight that likely plays a role. Years of research in psychology demonstrate that people are susceptible to learnt helplessness: We become passive or stop trying after repeated punishments or failed attempts at a task. Unfortunately, this passivity persists even after the environment changes in a way that would make success possible with further attempts. Organisational scholars have long recognised that this phenomenon occurs in our work lives just as it does in our personal lives. Figure 2.2, for example, shows Martinko and Garnder’s (1982[19]) conceptualisation of Organisationally Induced Helplessness – demonstrating how rigid organisational policies and structures, coupled with a perceived lack of control, results in passive, maladaptive behaviour in organisations. A study published in the Journal of Organisational Behaviour demonstrates that this is neither an exclusively Western phenomenon, nor a phenomenon of days gone by. The article, titled “Tired of Innovations? Learned Helplessness and Fatigue in the Context of Continuous Streams of Innovation Implementation” is based on data from 84 managers and 397 employees of Chinese and Korean organisations. Results demonstrate how employee behaviour toward future innovation is shaped by perceptions of helplessness and fatigue resulting from previous unsuccessful innovation attempts (Chung, Choi and Du, 2017[20]).
Figure 2.2. A model of organisationally-induced helplessness
Source: Reprinted from Martinko and Gardner (1982[19]).
In the context of the preceding IRB example, learnt helplessness can set in at several levels. First, it may affect the researcher whose innovative ideas are repeatedly denied or punished through additional paperwork requirements, revisions, justifications, or reporting requirements. Second, it may affect IRB decision makers who themselves have been punished for supporting innovative ideas, either through legal action, threats thereof, unwanted attention from external stakeholders, or heightened workload (justifications, reporting requirements). As new employees come on board, this “It can’t be done” mindset is likely to be transmitted via the organisational socialisation process. Denying innovative proposals is thus likely to become part of the norm to which workers conform.
There is no single, simple solution to this problem. As Figure 3 suggests, work behaviour does not “exist in a vacuum,” so to speak, but is influenced by a variety of factors interacting in concert. Nevertheless, with a diagnosis in place, we can begin to use behavioural insights to address the problem. The first part of the solution is ensuring that organisational policies and procedures allow for, encourage, and reward innovation at varying levels – and revising policies if necessary. In the parlance of Kerr (1995[18]) the organisation(s) hoping for B need to may sure B is being rewarded. This may require helping decision makers balance risk with innovation, and understand the behavioural consequences of going too far in either direction. See Shephard (2017[21]) for a discussion of using behavioural insights to support decision makers in balancing risk and innovation.
Notably, revised policies alone may not be enough. If learnt helplessness has set in, freeing constraints will not change behaviour. Here, it is worth pointing out the motivating influence of self-efficacy and “meaning” in our work. Assuming organisational policies that allow for creativity and innovation are in place, self-efficacy inducing interventions (training, exercises, etc.) may increase confidence in the prospect of carrying out innovation. A transformational leader who helps workers see the meaning in their work can also help. For example, IRB employees may be more receptive to additional workload or reporting requirements triggered by truly innovative ideas if they feel they are contributing to the cause – that is, if they see the connection between these more mundane work-related actions and the more inspirational end goal that the innovation is designed to achieve.
In summary, many times there is an inconsistency between what is hoped for and what is rewarded. Organisations often nudge the workforce in unintended directions via their informal or formal reward structures. Effective solutions to this problem require attention to organisational policies as well as more micro-level psychological consequences of rewards and punishments.
2. There is more to work motivation than monetary rewards and incentives
It is a central theme of economics that incentives promote effort and performance, and there is a lot of evidence that they often do (e.g., (Gibbons, 1997[22]); (Lazear, 2000[23]). In other words, contingent rewards serve as “positive reinforcers” for the desired behaviour. In psychology, their effect is much more controversial” (Benabou and Tirole, 2003[24]) p. 489). Over the last decade or two, economists and psychologists have begun to develop a more common understanding of work motivation. A 2003 article in the Review of Economics Studies, for example, asserts that rewards may be only weak reinforcers in the short term and may have hidden costs, becoming negative reinforcers once they are withdrawn (Benabou and Tirole, 2003[24]). A classic explanation has to do with the “overjustification effect” whereby extrinsic incentives and rewards cause workers to conceptualize their motivation to perform a task in external terms (e.g., for a bonus) rather than seeing the task as intrinsically worthwhile. The result is a so-called “crowding out” of intrinsic motivation by external rewards (Gubler, Larkin and Pierce, 2016[25]).
Self-Determination Theory is a macro theory of human motivation that evolved from research on intrinsic and extrinsic motivations and has been expanded to account for work behaviour. In their review of Self-Determination Theory in the Annual Review of Organisational Psychology and Organisational Behaviour, Deci, Olafsen, and Ryan (2017[26]) elaborate on the theory, focusing on the distinction between autonomous and controlled motivation, and postulating that all employees have three basic psychological needs – for autonomy, competence, and relatedness (i.e., relationships with other people). Autonomous motivation and high performance result from work environments that satisfy these three needs. Therefore, one component of the diagnostic phase of determining how best to nudge an organisation entails considering the degree to which these needs are being met on the job. Their absence renders an organisation increasingly reliant on external rewards, with motivational effects that are often unsustainable.
Dan Ariely and Adam Grant are two contemporary psychologists whose work has effectively spanned the behavioural economics and organisational psychology domains. As Ariely asserts, “When we think about labour, we usually think about motivation and payment as the same thing, but the reality is that we should probably add all kinds of things to it: meaning, creation, challenges, ownership, identity, pride, etc.” (Gross, 2015[27]). Box 2.1 describes five studies conducted by Ariely and Grant, which support this assertion.
Box 2.1. What Motivates Us at Work? More than Money. Example Research Studies by Dan Ariely and Adam Grant
Seeing the fruits of our labor may make us more productive.
The Study: In Man’s search for meaning: The case of Legos, Ariely asked participants to build characters from Lego’s Bionicles series. In both conditions, participants were paid decreasing amounts for each subsequent Bionicle: $3 for the first one, $2.70 for the next one, and so on. But while one group’s creations were stored under the table, to be disassembled at the end of the experiment, the other group’s Bionicles were disassembled as soon as they’d been built. “This was an endless cycle of them building and we destroying in front of their eyes,” Ariely says.
The Results: The first group made 11 Bionicles, on average, while the second group made only seven before they quit.
The Upshot: Even though there wasn’t huge meaning at stake, and even though the first group knew their work would be destroyed at the end of the experiment, seeing the results of their labor for even a short time was enough to dramatically improve performance.
The less appreciated we feel our work is, the more money we want to do it.
The Study: Ariely gave study participants — students at MIT — a piece of paper filled with random letters, and asked them to find pairs of identical letters. Each round, they were offered less money than the previous round. People in the first group wrote their names on their sheets and handed them to the experimenter, who looked it over and said “Uh huh” before putting it in a pile. People in the second group did not write down their names, and the experimenter put their sheets in a pile without looking at them. People in the third group had their work shredded immediately upon completion.
The Results: People whose work was shredded needed twice as much money as those whose work was acknowledged in order to keep doing the task. People in the second group, whose work was saved but ignored, needed almost as much money as those whose work was shredded.
The Upshot: “Ignoring the performance of people is almost as bad as shredding their effort before their eyes,” Ariely says. “The good news is that adding motivation doesn’t seem to be so difficult. The bad news is that eliminating motivation seems to be incredibly easy, and if we don’t think about it carefully, we might overdo it.”
The harder a project is, the prouder we feel of it.
The Study: In another study, Ariely gave origami novices paper and instructions to build a (pretty ugly) form. Those who did the origami project, as well as bystanders, were asked at the end how much they would pay for the product. In a second trial, Ariely hid the instructions from some participants, resulting in a harder process — and an uglier product.
The Results: In the first experiment, the builders paid five times as much as those who just evaluated the product. In the second experiment, the lack of instructions exaggerated this difference: builders valued the ugly-but-difficult products even more highly than the easier, prettier ones, while observers valued them even less.
The Upshot: Our valuation of our own work is directly tied to the effort we have expended. (Plus, we erroneously think that other people will ascribe the same value to our own work as we do.)
Knowing that our work helps others may increase our unconscious motivation.
The Study: As described in a recent New York Times Magazine profile, psychologist Adam Grant led a study at a University of Michigan fundraising call centre in which students who had benefited from the centre’s scholarship fundraising efforts spoke to the callers for 10 minutes.
The Results: A month later, the callers were spending 142% more time on the phone than before, and revenues had increased by 171%, according to the Times. But the callers denied the scholarship students’ visit had impacted them.
The Upshot: “It was almost as if the good feelings had bypassed the callers’ conscious cognitive processes and gone straight to a more subconscious source of motivation,” the Times reports. “They were more driven to succeed, even if they could not pinpoint the trigger for that drive.”
The promise of helping others makes us more likely to follow rules.
The Study: Grant ran another study (also described in the Times profile) in which he put up signs at a hospital’s hand-washing stations, reading either “Hand hygiene prevents you from catching diseases” or “Hand hygiene prevents patients from catching diseases.”
The Results: Doctors and nurses used 45% more soap or hand sanitizer in the stations with signs that mentioned patients.
The Upshot: Helping others through what is called “prosocial behaviour” motivates us.
Source: Excerpts from Gross (2015[27]).
The results and conclusions of experiments such as those conducted by Ariely and Grant are consistent with theories linking job characteristics to work outcomes such as motivation and performance through psychological states such as the experience of meaningfulness at work. Hackman and Oldham’s (1976[28]) Job Characteristics Model specifies the precise nature of these relationships. As shown in Figure 2.3, the Job Characteristics Model asserts that high internal work motivation, effective performance, and other desirable work outcomes such as high satisfaction, low absenteeism, and low turnover will result to the extent that workers experience three critical psychological states: (a) meaningfulness of work; (b) responsibility for the outcomes of work; and (c) knowledge of the actual results of work activities.
The question, then, becomes: Under what conditions will workers experience these desirable psychological states? The model posits that people working in jobs characterised by five core dimensions are most likely to experience the psychological states leading to high motivation and performance. Those five core job characteristics are: (a) skill variety, or the number of different abilities or skills needed to perform the work; (b) task identity, which is the degree to which a job requires completion of an entire function versus just a narrow piece of the larger whole; (c) task significance, which is the degree to which the job impacts others within our outside of the organisation; (d) autonomy, or freedom to choose how to schedule and carry out the work; and (e) feedback, or the degree to which the job itself offers information about the effectiveness of performance.
Consistent with findings from Ariely and Grant, designing or redesigning work to incorporate these five core job characteristics is expected to increase motivation and performance – assuming the person performing the work is reasonably high in “growth need strength” – that is, the desire for personal growth on the job. The model (as well as Ariely and Grant’s research findings) is (are) less likely to hold true for workers with lower growth need strength, highlighting the fact that one size does not necessarily fit all when it comes to motivating or judging high performance. One practical aspect of the Job Characteristics Model is that it has generated a tool (the JDS; Job Diagnostic Survey) help quantify the motivating potential of any given job based on the five core job characteristics. This tool also includes a series of questions measuring workers’ growth needs strength.
Figure 2.3. Job characteristics model
Source: Produced from Hackman and Oldham (1976[28]).
“Physician report cards have been found to promote patient safety because they prompt physicians to compare their professional conduct to that of their peers and trigger such internal noneconomic rewards as professional pride and the pleasure of helping others” notes Guszcza (2015, p. 70[9])) upon summarising a study carried out by health economist Jonathan Kolstad at the Wharton School of Business. Though economists and psychologists sometimes phrase things a little differently, they are often focusing on the same concepts: this workplace intervention in the health sector arguably works by heightening perceptions of “task significance” and “feedback,” in the parlance of the Job Characteristics Model.
At minimum, psychological theories pertaining to intrinsic motivation such as those described above can be helpful in organising when and why different nudge tactics used by behavioural economists work. Better yet, such theories can prompt ideas for new nudges to be tested. “There is nothing so practical as a good theory,” Kurt Lewin (1951[29]) has famously stated. Theories such as those described above and elsewhere in this paper can be useful during both the diagnosis and design phases of an organisational nudge initiative.
This leads to the question: How might the preceding theories be applied to improve the behaviour of organisations? Perhaps the initial, broad question to ask during the diagnostic phase of inquiry is whether intra or inter-institutional barriers are stifling or fostering intrinsic motivation. Traditional application of Job Characteristics Theory entails redesigning (i.e., enriching) jobs to make them more motivating, thereby improving performance. These traditional applications are of limited utility in a complex environment, in which there is limited control over job design and incentives. Nevertheless, the middle section of the Job Characteristics Model is worth reflecting on. To what degree might policies governing the planning, implementation, and monitoring of various programmes be facilitating or thwarting the experience of the three critical psychological states? For example, are those implementing the projects allowed sufficient autonomy to organize the work in a way that makes sense in their context? Are communications in place to ensure workers know throughout the process whether or not they are performing well and meeting expectations? Do those working on a project see the connection between what they are doing and the broader objective (task significance)? Some of these issues may need to be addressed at the unit or workgroup level by a leader, but others could possibly be addressed through a larger scale intervention.
3. Goal-setting is an important motivational tool
Goal setting theory (Locke and Latham, 1990[30]) has perhaps been the most useful theory of motivation for I-O psychologists (Spector, 2012[31]). This theory has been widely used in organisations and well-supported by research. The theory holds that not all goals are equally motivating. In order for goals to improve job performance, four factors must be present. The goals must be a) challenging; b) specific; c) accepted by the worker; and d) accompanied by feedback on progress toward the goals (Locke and Henne, 1986[32]). Goal acceptance and buy-in by workers can be achieved by allowing workers some voice in the goal-setting process, by tailoring goals to workers’ needs and interests, and/or by an effective leader who inspires goal acceptance.
At a basic psychological level, goal setting is believed to exert its motivating effects through a discrepancy reduction process whereby workers seek to reduce undesirable goal-performance discrepancies in order to receive a positive self-evaluation (Nicklin and Williams, 2011[33]). Goal setting theory overlaps with and is supported by techniques familiar to behavioural economists, such as commitment devices and implementation intentions, described in Shephard (2017[21]). Such techniques can help bolster both goal specificity and buy-in via active engagement in the process of translating goals into concrete action steps.
A fundamental decision in any organisation is the extent to which goals should be set at the individual, subgroup, unit, or organisational level. The success of team competitions at work suggest that group goals can indeed work, as competitions themselves are often goal-setting interventions even if not labelled as such. For example, the example from South Africa described in OECD (2017[1]) showed the effectiveness of setting team-based health-related goals (e.g., weight loss) among government co-workers. Interestingly, this intervention included timely feedback via pedometers which showed the number of steps each team took, presumably on a daily basis. Though social loafing in such a context is possible, it is less likely to occur if the motivational elements of goal-setting are in place (e.g., goal acceptance, feedback, etc.). “Few individuals will want to be the one who ruins the performance of their team,” reasons Guszcza (2015[9]), p. 75) after describing a hospital intervention designed to increase handwashing among healthcare workers. Using an electronic soap dispenser equipped with a computer chip that records how often members of different hospital wards wash their hands, the intervention compared each ward’s actual handwashing to standards (i.e., goal) set by the World Health Organisation.
4. Performance management requires good feedback, which necessitates good data
As suggested on the preceding pages (Job Characteristics Theory, Goal Setting Theory) timely feedback is critical to performance management. There are several behavioural barriers to high-quality feedback that can be overcome if understood. These barriers are both cognitive and social in nature. Cognitive barriers refer to the fact that heuristics and other shortcomings in our ability to take in, process, and recall information limit the accuracy of our assessments when judging others’ performance. The halo (or horns) effect occurs when raters’ perceptions of various aspects of performance are unduly influenced by their positive (or negative) perception of one aspect of performance: Raters or judges place too much weight on one aspect of performance and allow it to shape how they view performance quality overall, and on other dimensions. The confirmation bias is the very human tendency to seek out and remember information that confirms a previously held hypothesis or hunch. This causes our theories of other people’s performance to shape how we notice, remember, and judge their performance quality later on. The recency effect is our tendency to place greater weight on recent behaviour when judging someone else’s performance. These are a few examples of the very human limitations in the way we process information, which can stifle accurate performance assessments needed for high-quality feedback and performance management.
Shephard (in progress) provides examples of how behavioural insights can be applied to help overcome these barriers and improve accuracy. Recent advances in computing, including “the internet of things” creates additional, new opportunities to track certain performance metrics digitally. Of course, doing so requires attention to the circumstances under which such tracking does and does not threaten workers’ sense of autonomy and control, as research tells us that motivation and performance can suffer when such needs are threatened. As discussed later, electronic performance monitoring using real-time data may also require solutions from data science, including data visualisation techniques, to help decision makers make sense of the large volume of data that can be produced.
Regardless of whether performance judgments are based on cognitive retrieval, electronic performance monitoring, or some combination thereof, high-quality performance data is just part of the challenge. The other part is communicating feedback to the workforce. To be maximally useful, feedback should be frequent, constructive, specific, and behavioural in nature, helping the workforce see precisely what types of actions should be discontinued, continued, or increased.
One barrier to the delivery of effective performance feedback is the present bias, well-known to behavioural economists. The benefits of providing performance feedback (and developing others, more generally) are seldom immediately realised. Managers taking the time out of their busy schedules to provide feedback rarely see improvements right away, as behavioural change takes time and effort. Couple that with the fact that delivering feedback is often socially uncomfortable for all involved, and the present bias sets in: People will tend to favour gains (more time, interpersonal harmony) now, preferring them to larger payoffs in the future (better-performing employees). Understanding this barrier to feedback delivery and performance management is important to addressing it.
5. Not every performance problem is a motivational problem
The most highly motivated workforce in the world will fail to produce results if they do not have the skills needed for the job. For example, poor administrative capacity could be a result, in part, of a lack of skills.
I-O psychology has a long history of developing and testing interventions to ensure fit between the Knowledge, Skills, Abilities, and Other Characteristics (KSAOs) required of a work assignment and the KSAOs possessed by the staff tasked with completing the assignment. These interventions typically entail tweaks to the organisational hiring process, training, or both.
Either way, the solution commonly entails job analysis early in the process. This approach was recently highlighted by Google in its analysis of best practice case studies describing “real stories of organisations using data to make work better (re:Work, n.d.[34]). The case study in question describes performance problems among JetBlue’s call centre employees. The method used to address this problem included a job analysis which required participation from subject matter experts at the organisation, and which resulted in a delineation of KSAOs important for the job, sorted into those needed at the point of hire versus those that could be learnt through training. JetBlue then developed and tested the introduction of a simulation to screen applicants on the KSAOs needed at the point of hire, which led to positive results; for example, training failure-based attrition fell by 75%, and overall training attrition fell by 25%.
In some organisational contexts, training is a more appropriate point of intervention than hiring to ensure workers have the requisite knowledge and skills. A model for successful employee training programs includes five steps: assess training needs at the organisational, task, and person levels; establish training objectives; develop and test training materials; implement the training program; and evaluate the training program often using an experimental or quasi-experimental design that compares the performance of workers who have and have not undergone the training program. Measuring actual job performance after training is important, as it is possible for skills adequately mastered in training to fail to transfer to the work setting for a variety reasons unrelated to skill acquisition but instead related to the worker and the work environment (Baldwin and Ford, 1988[35]).
Interventions such as job analysis, hiring, and training may admittedly be outside of the control of the complex network of entities involved in the planning, implementation, and monitoring of programmes. However, there is value in thinking through what KSAOs are needed for sufficient administrative capacity to be achieved, and whether additional policies or supports can be put into place to help assure those KSAOs.
A recent case study from the Republic of South Africa is worth highlighting, as it demonstrates applications of I-O psychology in general and job analysis in particular to large-scale national development projects. As part of the country’s long-term national infrastructure plan established in 2012, South Africa identified 18 major technology and infrastructure projects, known as SIPs (Strategic Integrated Projects) that would be prioritised in the days and years to come. The 18 SIPS include projects spanning a wide range of economic sectors and all nine provinces of the country. Efforts to skill South Africans for and through SIPs begged the question: What KSAOs are needed to accomplish these projects? Experts in South Africa carefully identified the occupations needed to accomplish the SIPs, using South Africa’s Organising Framework for Occupations (OFO). From there, two individuals with I-O psychology expertise were enlisted to conduct a proof-of-concept study to identify the KSAOs needed within and across the SIPs – information that can help drive future education and training curricula (Gloss and Foster, 2014[36]). This was accomplished via a crosswalk between South Africa’s OFO and the United States Occupational Information Network (O*NET) – a large database providing job analysis data on more than 900 occupations in the U.S. economy. I-O psychologists were involved in the conceptualisation of O*NET and continue to be involved in populating it, as each occupation is described in detail by hundreds of data points generated by job incumbents, subject matter experts, and job analysts. In the end, a detailed picture of the KSAOs needed to accomplish the 18 SIPs was provided. A limitation of this study was that it relied on U.S. data (KSAOs) instead of job analysis data from South Africa.
6. A positive work cycle can be strategically created by aligning individual and organisational goals
An overarching message pervading many traditional BI interventions is this: Desired decisions and behaviours are much more likely when programs and policies are set up in a way that is consistent with people’s self-interests. Getting people to persistently act against their own near-term interests is difficult to do. This principle is every bit as true at work as it is outside of work. Accordingly, strategically designing work environments and jobs in ways that align individual and organisational goals has the potential to create win-win situations whereby individual and organisational growth occur in tandem, and work motivation is sustained over time.
In 2014, the United Nations Istanbul International Center for the Private Sector in Development enlisted a multicultural team of I-O psychologists to provide input on how principles from the psychology of work could inform the role of the private sector in inclusive development. The resulting model was a Positive Work Cycle, shown in Figure 2.4, and presented in the culminating meeting in Istanbul attended by UN officials and Nobel Prize winning economist Joseph Stiglitz in late 2014.
As shown in Figure 2.4, the Positive Work Cycle requires attention not only to individual workers’ goals, but also to job design, leadership, human resource practices, and the socio-political context in which the work occurs (Bhawuk et al., 2014[37]). It brings together many of the concepts discussed on the preceding pages (self-determination theory, goal setting, feedback, leadership, training, etc.) and illustrates their reciprocal relationship with each other and the broader environment in which work occurs. Bhawuk et al. (2014[37]) scholarships highlights “that poverty can be reduced, or perpetuated and worsened, through cycles of behaviour in the workplace which are greatly influenced by organisational and societal context. In particular, [the authors] highlight a “positive work cycle” that can lead to both greater productivity and well-being. At the heart of this cycle is the setting of challenging and specific goals and the realisation that people are powerfully motivated by the need to fulfil certain fundamental psychological needs. When this positive cycle is broken or reverses due to poor working conditions or unfair working arrangements, or when it does not start due to unemployment, people can be further disempowered, their skills can atrophy, and ultimately, they are likely to remain trapped in poverty. When this cycle is in operation, people are empowered to shape their own destinies and the destinies of their communities and nations” (Bhawuk et al., 2014, p. 64[37]).
An open question is how and whether the Positive Work Cycle may be useful to help understand the skill deficits and to diagnose “pain points” and behavioural levers relevant to government policy and programmes amid the complex system of implementation, management and control at the national and sub-national levels, and even sometimes supra-national levels (for example, the EU).
Figure 2.4. The positive work cycle
Source: Bhawuk et al., (2014[37]).
7. Data drives better decisions at work. Data science can help
Data science and behavioural science can and need to work more closely together to maximize the potential to nudge organisations through behavioural insights. This has started to happen, but the full potential of this integration has yet to be realised.
Psychologist Michal Kosinki recently testified before the United States Equal Employment Opportunity Commission on the implications of “big data” for equal employment opportunity (Kosinski, 2016[38]). In his testimony, Kosinski describes how, if used properly, advances in data science can be used to identify talent and improve person-job fit. Big data can be used for other purposes as well. James Guszcza’s 2015 article describes a variety of ways in which data science and behavioural science can work together to solve the last-mile problem. As Guszcza (2015[9]) points out, “much of what we call ‘big data’ is in fact behavioral data” (p. 73).
Recall the define-diagnose-design-evaluate framework for behavioural nudges shown in Figure 2.1. Big data has the potential to facilitate multiple points in this process. First, as problem sets are being defined, techniques from data science can provide a clearer picture of the organisational “state of play.” By identifying behavioural patterns revealed in big data, organisations will be able to identify areas at risk for poor performance, such as providing early indicator that a project is lagging behind schedule or otherwise off track. This could allow for more laser-like, strategically aimed nudges in areas most in need of attention.
Big data can facilitate the design of behavioural interventions in at least two ways. First, it can enable the application of “smart nudges” that are tailored to the individual or population in question. Rather than using a “one size fits all” approach, the presentation or nature of the nudge could be adapted based on the needs, strengths, motivations, and shortcomings of the worker, unit, or project in question. Second, we have seen that performance feedback is an intervention in and of itself. If organised and presented in a way that makes sense to end users, big data can be an important source of feedback for individual workers as well as organisational decision makers. Early forays into this territory were described earlier – for example, providing people with immediate feedback on their work group’s exercise or handwashing behaviours.
Finally, it should be noted that big data can serve as important criteria when evaluating behavioural interventions. At present, obtaining the data needed to evaluate behavioural interventions can be very challenging, as many behaviours are not readily observed or measured, and organisations do not always have the resources to track employee behaviours in a way that is useful to behavioural scientists. With digital metrics in place, the behavioural scientist may obtain a detailed picture of moment-to-moment work outcomes of interest.
Of course, big data also implies electronic monitoring of sorts. As suggested earlier, implementation of such monitoring needs to be balanced with concerns over privacy and the potential loss of autonomy or control perceived by workers who are tracked. The social facilitation effect well known to psychologists suggests that our performance of difficult tasks deteriorates in the presence of others. Research should examine whether and under what conditions electronic performance monitoring produces similar effects, and investigate ways to ensure big data are collected in a non-threatening, non-invasive way.
8. Small wins are important
As suggested in this chapter, there is no single, simple prescription for how to nudge organisational behaviour. This is especially true for institutions characterised by a complex set of actors working independently and collectively to accomplish innovative development goals. Under such circumstances, it is easy, and very human, to get overwhelmed into inaction given the scale and scope of the challenge at hand. As Weick (1984[39]) pointed out many years ago, “The massive scale on which social problems are conceived precludes innovative action because bounded rationality is exceeded and dysfunctional levels of arousal are induced” (p. 40). Weick relates this to the Yerkes-Dodson law, whereby an inverted-U-shape characterizes the relationship between stress (arousal) and performance: More arousal leads to better performance up to a point, at which the stress begins to have a negative effect. Weick proposes a solution to this problem, noting that “Reformulations of social issues as mere problems allows for a strategy of small wins wherein a series of concrete, complete outcomes of moderate importance build a pattern that attracts allies and deters opponents. The strategy of small wins incorporates sound psychology and is sensitive to the pragmatics of policymaking” (p. 40).
Weick’s advice can be applied internally, to those charged with effecting change through the application of behavioural insights. Maya Shankar (2016[40]) outlined four principles contributing to the success of the White House Social and Behavioural Sciences Team’s approach to changing behaviour in a U.S. policy context. These principles included:
1. Convert interest into impact.
2. Quantify your wins.
3. Celebrate small wins.
4. The importance of generating buy-in.
References
[35] Baldwin, T. and J. Ford (1988), “Transfer of Training: A Review and Directions for Future Research”, Personnel Psychology, Vol. 41/1, pp. 63-105, http://dx.doi.org/10.1111/j.1744-6570.1988.tb00632.x.
[24] Benabou, R. and J. Tirole (2003), “Intrinsic and Extrinsic Motivation”, Review of Economic Studies, Vol. 70, pp. 489-520, http://www.princeton.edu/~rbenabou/papers/RES2003.pdf (accessed on 27 February 2020).
[37] Bhawuk, D. et al. (2014), “Poverty reduction through positive work cycles: Exploring the role of information about work, culture and diversity, and organizational justice.”, in Al-Atiqi, S. (ed.), Barriers to and opportunities for poverty reduction: Prospects for private-sector led interventions, http://www.undp.org/content/dam/istanbul/docs/2014_Barriers_to_and_Prospects_for_Poverty_Reduction.pdf.
[29] Cartwright, D. (ed.) (1951), Field theory in social science: selected theoretical papers, Harper & Row, New York, NY.
[20] Chung, G., J. Choi and J. Du (2017), “Tired of innovations? Learned helplessness and fatigue in the context of continuous streams of innovation implementation”, Journal of Organizational Behavior, Vol. 38/7, pp. 1130-1148, http://dx.doi.org/10.1002/job.2191.
[3] De Cooman, R. et al. (2009), “Person-organization fit: Testing socialization and attraction-selection-attrition hypotheses”, Journal of Vocational Behavior, Vol. 74/1, pp. 102-107, http://dx.doi.org/10.1016/j.jvb.2008.10.010.
[26] Deci, E., A. Olafsen and R. Ryan (2017), “Self-Determination Theory in Work Organizations: The State of a Science”, Annual Review of Organizational Psychology and Organizational Behavior, Vol. 4/1, pp. 19-43, http://dx.doi.org/10.1146/annurev-orgpsych-032516-113108.
[2] Ebury Publishing (ed.) (2015), Inside the Nudge Unit: How Small Changes Can Make a Big Difference, https://www.behavioraleconomics.com/resources/books/inside-the-nudge-unit-how-small-changes-can-make-a-big-difference-david-halpern-2015/.
[15] Flanagan, J. (1954), “The critical incident technique”, Psychological Bulletin, Vol. 51/4.
[7] French, J. and B. Raven (1959), “The Bases of Social Power”, in Cartwright, D. and A. Zander (eds.), Group dynamics, Harper & Row, New York, http://www.communicationcache.com/uploads/1/0/8/8/10887248/the_bases_of_social_power_-_chapter_20_-_1959.pdf (accessed on 26 February 2020).
[22] Gibbons, R. (1997), “Incentives and careers in organizations”, in Kreps, D. and K. Wallis (eds.), Advances in economic theory and econometrics, Cambridge University Press, Cambridge.
[36] Gloss, A. and L. Foster (2014), “Estimating the underlying characteristics of South Africa’s critical/scarce occupations: A proof‐of‐concept study”, in Department of Higher Education and Training (ed.), Skills for and through strategic integrated projects, http://www.wrseta.org.za/downloads/1_Skills%20for%20and%20through%20SIPs.pdf.
[27] Gross, J. (2015), What motivates us at work? More than money, TED, https://ideas.ted.com/what-motivates-us-at-work-7-fascinating-studies-that-give-insights/ (accessed on 27 February 2020).
[25] Gubler, T., I. Larkin and L. Pierce (2016), “Motivational spillovers from awards: Crowding out in a multitasking environment”, Organization Science, Vol. 27/2, pp. 286-303, http://dx.doi.org/10.1287/orsc.2016.1047.
[9] Guszcza, J. (2015), How data science and behavioral economics can work together, Deloitte Insights, Issue 16, https://www2.deloitte.com/us/en/insights/deloitte-review/issue-16/behavioral-economics-predictive-analytics.html (accessed on 26 February 2020).
[28] Hackman, R. and G. Oldham (1976), “Motivation through the Design of Work: Test of a Theory”, Organisational Behavior and Human Performance, Vol. 16, pp. 250-279, http://web.mit.edu/curhan/www/docs/Articles/15341_Readings/Group_Performance/Hackman_et_al_1976_Motivation_thru_the_design_of_work.pdf (accessed on 27 February 2020).
[8] Hinkin, T. and C. Schriesheim (1989), “Development and Application of New Scales to Measure the French and Raven (1959) Bases of Social Power”, Journal of Applied Psychology, Vol. 74/4, pp. 561-567, http://dx.doi.org/10.1037/0021-9010.74.4.561.
[17] Kanfer, R. and G. Chen (2016), “Motivation in organizational behavior: History, advances and prospects”, Organizational Behavior and Human Decision Processes, Vol. 136, pp. 6-19, http://dx.doi.org/10.1016/j.obhdp.2016.06.002.
[18] Kerr, S. (1995), “On the folly of rewarding A, while hoping for B”, The Academy of Management Executive, Vol. 9, https://www.ou.edu/russell/UGcomp/Kerr.pdf (accessed on 26 February 2020).
[38] Kosinski, M. (2016), Written Testimony of Michal Kosinski: Public Meeting on Big Data in the Workplace, United State Equal Employment Opportunity Commission, https://www.eeoc.gov/eeoc/meetings/10-13-16/kosinski.cfm (accessed on 2 March 2020).
[23] Lazear, E. (2000), “Performance pay and productivity”, American Economic Review, Vol. 90/5, pp. 1346-1361, http://dx.doi.org/10.1257/aer.90.5.1346.
[32] Locke, E. and D. Henne (1986), “Work motivation theories”, in Cooper, C. and I. Robertson (eds.), International Review of Industrial and Organizational Psychology, Wiley, New York, NY.
[30] Locke, E. and G. Latham (1990), A theory of goal setting and task performance, Prentice Hall, Englewood Cliffs, NJ.
[19] Martinko, M. and W. Gardner (1982), “Learned Helplessness: An Alternative Explanation for Performance Deficits”, The Academy of Management Review, Vol. 7/2, p. 195, http://dx.doi.org/10.2307/257297.
[33] Nicklin, J. and K. Williams (2011), “Self-Regulation of Goals and Performance: Effects of Discrepancy Feedback, Regulatory Focus, and Self-Efficacy”, Psychology, Vol. 02/03, pp. 187-201, http://dx.doi.org/10.4236/psych.2011.23030.
[6] OECD (2018), Rethinking Regional Development Policy-making, OECD Multi-level Governance Studies, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264293014-en.
[1] OECD (2017), Behavioural Insights and Public Policy: Lessons from Around the World, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264270480-en.
[5] OECD (n.d.), Driving Performance of Regulators, https://www.oecd.org/gov/regulatory-policy/driving-performance-of-regulators.htm (accessed on 26 February 2020).
[11] Oppong, S. (2013), “Industrial and organizational psychology in Ghana”, The Industrial-Organizational Psychologist, Vol. 50/3, pp. 79-83.
[16] Rademacher, R., D. Simpson and K. Marcdante (2010), “Critical incidents as a technique for teaching professionalism”, Medical Teacher, Vol. 32/3, pp. 244-249, http://dx.doi.org/10.3109/01421590903197001.
[34] re:Work (n.d.), Explore Case Studies, https://rework.withgoogle.com/case-studies/ (accessed on 2 March 2020).
[4] Schneider, B., H. Goldstein and D. Smith (1995), “The ASA framework: An update”, Personnel Psychology, Vol. 48, pp. 747-773, http://phd.meghan-smith.com/wp-content/uploads/2015/09/schneiderasa.pdf (accessed on 26 February 2020).
[40] Shankar, M. (2016), Building a behavioral science start-up at the White House, YouTube, https://www.youtube.com/watch?v=L2sHGC1mDl0 (accessed on 2 March 2020).
[21] Shephard, D. (2017), Applying behavioural insights to organisations: Global case studies, OECD and European Commission, Paris, https://www.oecd.org/cfe/regional-policy/Shepard_Applying-Behavioural-Insights-to-Organisations_Case-Studies.pdf (accessed on 26 February 2020).
[14] Silva, M. (2017), Organizational innovation: Complexities of the United Nations as an institution to innovate for development, Working paper.
[10] SIOP (n.d.), Society for Industrial and Organizational Pyschology, https://www.siop.org/ (accessed on 26 February 2020).
[31] Spector, P. (2012), Industrial and organizational psychology : research and practice, John Wiley & Sons, Inc.
[12] Stillman, S. (2017), “Can Behavioral Science Help in Flint? An unusual team of White House scientists works through the final days of the Obama administration”, The New Yorker, https://www.newyorker.com/magazine/2017/01/23/can-behavioral-science-help-in-flint (accessed on 26 February 2020).
[13] UNDP (2016), Behavioural Insights at the United Nations: Achieving Agenda 2030, United Nations Development Programme, New York, https://www.undp.org/content/dam/undp/library/innovation/Behavioral%20Insights%20at%20the%20UN.pdf (accessed on 26 February 2020).
[39] Weick, K. (1984), “Small wins: Redefining the scale of social problems”, American Psychologist, Vol. 39/1, pp. 40-49, http://dx.doi.org/10.1037/0003-066X.39.1.40.
Note
← 1. This seminar is part of a five-part seminar series in the context of an EC-OECD project “Designing better economic development policies for regions and cities”. Other sessions in the series addressed the use of: contracts for flexibility/adaptability, performance indicators, financial instruments, and insights from behavioural science. The outcome of the seminars supports the work of the Regional Development Policy Committee and its mandate to promote the design and implementation of policies that are adapted to the relevant territorial scales or geographies, and that focus on the main factors that sustain the competitive advantages of regions and cities. The seminars also support the Directorate-General for Regional and Urban Policy (DG REGIO) of the European Commission in the preparation of the impact assessment for the post-2020 legislative proposals and to support broader discussion with stakeholders on the future direction of the delivery mechanisms of regional policy. Full details, including original papers and the resulting OECD publication summarising this work can be found at: https://www.oecd.org/governance/rethinking-regional-development-policy-making-9789264293014-en.htm