Identifying antecedents of a manager’s decision to delegate
Master Thesis, MSc Human Resource Management University of Groningen, Faculty of Economics and Business
Abstract
Delegation by managers coincides with benefits for the manager, subordinates, and the firm as a whole. Even though delegation is associated with such advantages, only little research on delegation focuses on the influence of the context in which delegation takes place. This research shed light on the influence of context in this regard, by investigating the influence of the framing of decisions on delegation. In an online experiment with a 2x2 factorial design, which was conducted through MTurk, participants (N = 363) were provided with either gain-framed or loss-framed investments, and they had to choose between making the decision themselves, or to delegate. Participants that were provided with loss-framed investments were found more likely to delegate than participants that were provided with gain-framed decisions. Furthermore, participants with high power were found less likely to delegate. The results of this study provide further proof for the notion that the context in which leadership takes places influences delegation.
Identifying Antecedents of a Manager’s Decision to Delegate
Effective management of the workforce is a way for firms to create sustainable competitive advantages (Pfeffer, 2005). Many researchers have argued that it is not just human capital that creates this advantage, but that it is the ability of managers to manage this human capital that can lead to these benefits (Chan, Shaffer & Snape, 2004; Yukl, 2012). Through managing the workforce, firms can attract, retain, motivate, develop, and use their human capital (Coff, 1997; Kamoche, 1996; Mueller, 1996).
In an aim to discover what makes management effective, Google set up ‘Project oxygen’. As explained in a news article by Schneider (2019), Google’s Project oxygen led to the identification of eight effective managerial habits. One of these habits was the empowerment and motivation of employees. Empowering employees leads to better employee performance and a more committed workforce (Kahreh, Ahmadi & Hashemi, 2011; Fernandez & Moldogaziev, 2013). As empowerment of employees is beneficial to the functioning of companies, both academia and organizations have been preoccupied with studying how one can establish empowerment. One approach that effectively establishes the empowerment of employees is delegation, which is considered as a process in which leaders (i.e. managers) provide their subordinates with decision making authority (Leana, 1987).
managers (Muir, 1995; Riisgaard, Nexøe, Søndergaard & Ledderer, 2016; Leana, 1987). Through all these processes, delegation can lead to increased firm performance.
Following the benefits of delegation, one would expect that research aimed at identifying the determinants of delegation has been widely conducted. However, as was already noted by Bass and Stogdil (1990), only limited research has been conducted on this topic. Little has changed in this regard, as Haselhuhn, Wong, and Ormiston (2017) more recently pointed to the limited research conducted on the identification of the antecedents of delegation. As delegation involves a manager’s decision to provide subordinates with decision-making authority, research on aspects that influence a manager’s decision to delegate could open the black box of the determinants of delegation.
characteristics of managers and/or subordinates (Richardson, Amason, Buchholtz & Gerard, 2002), or on the interaction between these two (Haselhuhn et al., 2017). As characteristics are relatively stable, research on characteristics as determinants of delegation provides little explanation for differences within people regarding their leadership behavior (i.e. delegation) (Dinh & Lord, 2012). Rather than focusing on characteristics that influence delegation, research focused on the influence of the context of leadership could provide us with more insights into the antecedents of delegation. Context refers to the environment in which leadership takes place, and has an important influence on leadership (Liden & Antonakis, 2009). For example, the effectiveness of leadership styles depends on the organizational context to which it is applied (Fiedler, 1967). Based on these findings, it can be expected that delegation is also influenced by its context. For this reason, this research will focus on providing insights regarding the determinants of delegation by investigating contextual factors that may influence a manager’s decision to delegate. By doing so, this research could provide firms with valuable insights regarding contexts that can potentially foster delegation. Consequently, this could then lead to the previously mentioned advantages of delegation.
Specifically, this research will look at whether a manager’s decision to delegate is influenced by the context, by looking at potential differences between situations in which these decisions are either framed as gains or losses. Therefore, the following research question will be investigated:
“Does the framing of decisions influence a manager’s decision to delegate?
will focus on the potential gains. It is expected that framing will have a significant effect on participants’ decision to delegate. In addition, based on the findings that high power makes people more likely to delegate (Haselhuhn et al, 2017), the influence of power on the relationship between framing and delegation will be investigated.
Relevance and Contribution
This research can contribute to the process of opening up the black box of the determinants of delegation. Whereas most research on delegation has focused on the influence of relatively stable characteristics, research on the influence of context can provide us with further insights into the determinants of delegation. In addition, as most research on the influence of context on delegation has looked at its moderating effect (i.e. Walter & Bruch, 2010; Antonakis, Avolio, & Sivasubramaniam, 2003), researchers have expressed the need for research on the direct influence that changes in context have on leadership (Osborn & Marion, 2009; Oc, 2018). By investigating whether the context in which leadership takes place in relation to loss- and gain-framing influences delegation, this research can help fill the existing gap in the literature on determinants of delegation and the direct influence of context in this regard. In addition to the influence of framing on delegation, this research will look at the interaction between context and individual differences, by investigating the influence of people’s sense of power on the relationship between framing and delegation. This can further shed light on factors that influence the decision to delegate.
could thus provide these firms with information regarding the way they should frame this information so managers will be more likely to delegate, which would then lead to positive consequences for the manager, subordinates, and the firm as a whole. In addition, when a manager with a higher sense of power is indeed more likely to delegate, it could be helpful for firms to, for example, keep this influence in mind during the process of hiring a new manager. In the next chapter, the theoretical framework of this study, including the hypotheses that will be investigated in this research, will be presented.
Theoretical framework
Leadership in Context: Prospect Theory and Loss Aversion
avoid losses compared to the importance of acquiring gains. Moreover, people are more reluctant to suffer a loss than that they desire obtaining a gain that is of similar size (Levin, Schneider & Gaeth, 1998). Based on the larger impact that losses have compared to gains, people may perceive losses as being more important, which could result in less delegation for decisions involving losses compared to decisions involving gains.
The Influence of the Framing of Decisions
In addition to the influence that the outcomes of decisions may have on a manager’s decision to delegate, the way that these outcomes are perceived may also influence delegation. Previous research has indicated that the way outcomes and decisions are perceived indeed influence people’s actual decisions. For example, risks are often perceived differently than the actual risk that a situation involves (Bohnenblust and Slovic (1998), and this perception that a person has of the risk involved with the outcomes of their decisions influences their actual decisions and behavior (Siegrist, Gutscher & Earle, 2005)
to the death of 400 people, whereas the other program (the risky option) involved a 1/3 probability that nobody would die, and a 2/3 probability that all 600 people would die. As provided, the problems that were presented to participants were almost identical: the only difference is that problem 1 focused on the lives that would be saved, whereas problem 2 focused on the lives that would be lost. Interestingly, participants were found to prefer the risk averse option (chosen by 72% of the participants) when the problem focused on lives saved (problem 1), whereas they preferred the risky option (chosen by 78%) when the problem focused on lives lost (problem 2). Hence, Tversky and Kahneman established that framing influences participants’ risk preference, even when the actual outcomes are exactly the same. They provided further support for these findings in their research on choices, values, and frames (Tversky & Kahneman, 1983), in which they established that loss-framed decisions led to more risky decisions compared to gain-framed decisions.
researches such as that on mammography screenings (Banks et al.,1995), and on credit card use (Ganzach & Karsahi, 1995), also established that framing focused on negative consequences had a larger impact on people’ behavior compared to framing focused on positive consequences. These findings provide further proof for the notion that loss- and gain- framing influences risk perceptions and decision making.
As previous research has established that the framing of situations and decisions influences decision making, it can be expected that framing regarding losses and gains also influence a manager’s decision to delegate. As previously stated, decisions involving actual losses are likely considered more important than actual gains, and more important decisions are less likely to be delegated by managers. Hence, loss-framed decisions could then also be expected to be delegated less often compared to gain-framed decisions. On the other hand, the findings of Tversky & Kahneman (1983) point to the fact that decisions framed as losses lead to more risky decisions compared to gain-framed decisions. Delegation involves risk, because a subordinate can, for example, make decisions that are not in line with the decisions that the manager would have made (Alter, 2001). Based on these findings, one could expect that loss-framed decisions would actually lead to more delegation, as such framing would make participants more prone to engage in risky decision making. In this case, actual losses may actually also lead to more risky decisions, consequently leading to more delegation. Thus, whereas previous research has established that both actual and framed gains and losses influence decision making, based on which it can be expected that these gains and losses influence delegation, the direction of this effect remains difficult to predict. This research aims to shed light on the influence of actual and framed gains and losses on delegation, by investigating the following competing hypotheses:
Hypothesis 1B: People are more likely to delegate decisions that involve actual losses compared to decisions that involve actual gains.
Hypothesis 2A: Loss-framed decisions are delegated less often than gain-framed decisions.
Hypothesis 2B: Loss-framed decisions are delegated more often than gain-framed decisions.
Power and delegation
The decision to delegate involves a loss of control for managers, as delegation entails a transfer of authority to a subordinate (Hales, 1999; Richardson et al., 2002; Leana, 1987). Furthermore, delegation authorizes an employee to take action without having to refer back to the manager (Rees & Porter, 2015). Based on these findings, Haselhuhn and colleagues (2017) looked at the influence of individual differences on the decision to delegate, by examining the influence that psychological power may have on this decision. Power is defined as a person’s ability to have control over the punishments, rewards, and outcomes of others (Keltner, Gruenfeld & Anderson, 2003). Haselhuhn and colleagues measured psychological power as either feeling powerful or powerless, and found that individuals who felt relatively powerful were more likely to provide their subordinates with decision making authority through delegation, compared to individuals who felt relatively powerless. These findings point to the influence of psychological power on delegation.
risky decisions, it is be expected that these individuals are more likely to delegate decision making authority.
In addition to the influence of power on risk-taking behavior, people are likely to compensate for lower levels of psychological power by desiring more choices (Inesi, Bott, Dubois, Rucker & Galinsky, 2011). The delegation of decision-making authority may leave managers with less choices, as delegation transfers the decision between the available options to their subordinates. This further stresses the effect that psychological power may have on the process of delegation. Based on these findings, the following hypothesis will be investigated. Hypothesis 3: People with a high sense of power are more likely to delegate compared to people with a low sense of power.
Moderating effect of power on the relationship between framing and delegation
(Anderson & Galinsky, 2006), a higher sense of power could be expected to strengthen the effect between loss-framed decisions and delegation. For high power individuals, this high power would strengthen the relationship between loss-framing and delegation, and weaken the relationship between gain-framing and delegation, leading people to engage in more delegation. For low-power individuals, this low power arguably weakens the positive relationship between loss-framed decisions, and strengthens the negative relationship between gain-framing and delegation.
Thus, while previous research gives reason to expect a moderating effect of power on the relationship between framing and delegation, the direction of this effect could go either way. Therefore, the following competing hypotheses will be investigated:
Hypothesis 4A: Psychological power moderates the relationship between loss-framing and delegation, such that for high psychological power the negative relationship between loss-framing and delegation is weakened, leading to more delegation.
Hypothesis 4B: Psychological power moderates the relationship between loss-framing and delegation, such that for high psychological power the positive relationship between loss-framing and delegation is strengthened, leading to more delegation.
Conceptual Framework
The hypotheses of this research are included in the conceptual model as depicted in Figure 1.
Method
Participants and Design
To test the formulated hypotheses, an experimental research was conducted. For this experiment, a survey was constructed using Qualtrics software, Version (2020) of Qualtrics (Qualtrics, Provo, UT). The survey was distributed via Amazon Mechanical Turk (MTurk). The participants received money for their participation. A total of 363 people took part in the experiment. The study was set up as a 2x2 factorial design, with framing (loss and gain framing) and power (low and high power) as independent variables. The participants were randomly divided over the four different conditions. Prior to data analysis, 211 participants were excluded because they failed to pass the attention checks or they did not follow the instructions. These exclusions resulted in a dataset consisting of 152 eligible participants (58.6% male, 41.4% female). The age of the participants ranged from 22 to 65 (M = 36.83, SD =9.57). Table 1 provides further information about the participants.
Table 1. Demographics.
Measures and Manipulations
Investment game. To investigate delegation behavior, the participants took part in an
online investment game. In this game, participants were manager of a company, where they supervised an employee. As previous research has indicated that personalization can enhance
Demographic Percentage
Level of Education Highschool Diploma 8%
Some College Credit 15%
Bachelor’s Degree or Higher 77%
Employment Status Employed 88.2%
Unemployed 11.8%
Sector of Employment Manufacturing 13.2%
study’s response rates through involvement (Dillman, 1984), participants could choose their own name for their company. As manager of their company, they were asked to make sixteen investment decisions. These investments involved 8 opportunities including actual gains, and 8 decisions including actual losses. More information regarding the investments and examples can be found in Table 2 in the section ‘Loss and gain frame’. The participants were given an investment capital of €100,000, and they were told that their goal was to hold the largest amount of money with their company by the end of the experiment. As monetary incentives beneficially influence participation in experiments (Beattie & Loomes, 1997), participants received a base payout of $1, and an additional $.10 for every €5,000 experimental currency that their company held over €60,000 by the end of the experiment. This amount was the participant’s final score for the experiment (M = €83,552.12, SD = €8,061.22, min. = €64,072, max. = €120,500). Furthermore, they were told that three participants with the largest amount of money would be rewarded with an extra $20, as previous research has indicated that lottery incentives positively influence response rates and provide participants with an extra incentive to perform as best as they can (Laguilles, Williams & Saunders, 2011).
Delegation. For each investment decision, it was registered whether participants chose
benefits that coincide with delegation due to time-saving as a result of this delegation. After data collection, the proportion of delegated investment decisions was calculated for each participant to investigate their delegation behavior. The average proportion of delegation was .55 (SD = .36).
Loss and gain frame. To investigate the influence that framing has on people's decision
to delegate, participants were divided over a loss-frame and a gain-frame condition. Participants within the loss frame condition (N = 73) were provided with the following information: “It is not going so well with your company, and the company is struggling to survive. As manager you have to make a number of investment decisions, to hopefully save the company.”. For the gain-frame condition (N = 79), participants were told: “It is going very well with your company and, therefore, the company is looking for opportunities to expand. As the manager you will have to make a number of investment decisions about these expansion opportunities.”.
Table 2. Overview framing.
Table 3. Overview investment decisions.
Power manipulation. The participant's level of psychological power was influenced
via a power manipulation, which involved writing a narrative essay. This particular power
Loss-framed decisions Gain-framed decisions Actual Loss This investment involves a potential loss of €6000.
Decision A will lead to a loss for the company of €4000. Decision B involves a chance of 30% that the company will lose nothing, and a chance of 70% that the company will lose the €6000
This investment involves a potential loss of €6000. Decision A will lead to saving €2000. Decision B involves a chance of 30% that the company will save €6000 and a 70% chance that the firm won't be able to save the €6000.
Actual Gain With this investment, the company has the opportunity to gain €8000. Decision A will lead to losing €6000 of the potential gain that can be made with this
investment. Decision B involves a chance of 20% that the company will lose nothing of the potential gain, and a probability of 80% that the company will lose the opportunity to gain the mentioned €8000
With this investment, the company has the opportunity to gain €8000. Decision A will lead to gaining €2000 of the potential gain that can be made with this investment. Decision B involves a chance of 20% that the company will gain €8000, and a chance of 80% that the company will not gain €8000
manipulation was based on the power manipulation used by Galinsky, Gruenfeld, and Magee (2003), and was chosen because this manipulation has been found to create similar effects as power manipulations in which role-based manipulations of power were used (Anderson & Galinsky, 2006; Galinsky et al., 2003). Furthermore, this power manipulation was suitable for the online nature of the data collection of this study.
Within the low-power conditions (N = 78), participants were asked to recall and describe an experience during which someone else had power over them, which was explained as a situation in which someone had control over their ability to get something they wanted, or was in a position to evaluate them. Within the high-power conditions (N = 74), on the other hand, participants were asked to recall and describe an experience during which they had power over another individual or individuals. The participants were asked to describe how they experienced this situation, how this situation affected them, and how it influenced their behavior. After pilot testing, a minimum amount of characters of 400 was installed that was required for answering this question, to make sure that the manipulation would have an effect on the participants’ level of psychological power.
To be able to check whether the power manipulation indeed led to different levels of psychological power within the two power conditions, participants were presented a power check after they had finished all investment decision. This power check consisted of a slider, on which they were asked to indicate how powerful they felt on a level from 0 to 100. Control variables. Previous research has indicated that various other variables might
influence a managers’ decision to delegate. For this reason, the following control variables were included in this research: gender, age, desire for control, extraversion, openness to experience, and regulatory focus. All reversed items were recoded. The control variables were measured as follows:
delegate compared to men. Therefore, participants were asked to indicate whether they are male or female. Gender was measured by asking participants to indicate whether they were male or female. Contrary to previous research findings, women were not found to be significantly more likely to delegate (r = -.01, p = .91).
Age. Based on the research of Oshagbemi (2004) on the influence of age on leadership style and behavior, it is expected that age doesn’t influence delegation behavior. However, previous research has indicated that risk-taking decreases with age (Deakin, Aitken, Robbins & Sahakian, 2004). As delegation involves risk, older participants may be less likely to delegate. Therefore, this research controlled for differences in age to ensure that potential differences in the amount of delegation between the loss- and gain-frame condition cannot be explained by age differences. Age was measured by asking participants to answer the question “What is your age?”. Age was not found to significantly correlate with the proportion of delegated investment decisions (r = .09, p = .28).
Desirability of control. As previously stated, it can be argued that delegation coincides
with a loss of control (Hales, 1999; Richardson et al, 2002; Leana, 1987). Therefore, differences between the two conditions in the participants’ desirability of control could influence delegation, whereby people with a higher desirability of control would be less likely to delegate. Therefore, this research controlled for participants desirability of control.
Extraversion and openness to experience. Hassan, Asad and Hoshino (2016) identified that high scores on the Big Five personality traits extraversion and openness to experience are related to the participative leadership style. Since delegation is an aspect of the participative leadership style, this research controlled for participants’ scores on extraversion and openness to experience to ensure that differences in the amount of delegation between the two conditions can’t be explained by differences in scores on these personality traits.
To measure participants' scores on extraversion and openness to experience, they were asked to fill out the items of the shortened Big Five Inventory (Gerlitz & Schupp, 2005) that are related to these personality traits. Both extraversion (α = .77, M = 4.50, SD = .96) and openness to experience (α = .78, M = 5.46, SD = 1.15) were measured with three items on a seven-point scale (Appendix B). Examples of these items are "I see myself as someone who is talkative" to measure extraversion, and "I see myself as someone who is original, comes up with new ideas" to measure openness to experience. No significant correlation was found between delegation and extraversion (r = .01, p = .93) and between delegation and openness to experience (r = .05, p = .54).
of the General Regulatory Focus Measure (Lockwood, Jordan & Kunda, 2002) to ensure that differences in the amount of delegation between the loss- and gain-frame condition can’t be explained by differences in scores on regulatory focus. The General Regulatory Focus Measure measures people's promotion focus scores and prevention focus score. This scale consists of 18 items (Appendix C), of which 9 items measure participant's prevention focus (α = .89, M = 3.99, SD = 1.34) and 9 items measure participant's promotion focus (α = .88 M = 5.20, SD = 1.01). It was decided to measure these items on a seven-point scale, instead of on a nine-point scale, because previous research has indicated that seven-point scales are more accurate compared to other Likert scales (Diefenbach, Weinstein & O’Reilly, 1993). This adjustment in the measurement scale of the General Regulatory Focus Measure is in line with the research of, for example, Zhao and Namasivayam (2012).
An example of an item measuring people's promotion focus is "I frequently imagine how I will achieve my hopes and aspirations", an example of an item measuring prevention focus is "I often imagine myself experiencing bad things that I fear might happen to me". For data analysis, all reversed items were recoded. No significant correlation was found between delegation and prevention focus (r = -.04, p = .67) and between delegation and promotion focus (r = -.00, p = .99).
Attention Checks and Control Questions. Several attention checks and control
were excluded from the dataset when they did not provide serious answers to the questions regarding the power manipulation. In addition, participants were asked to write down the word “investment” as an attention check.
Procedure
Participants received the survey via a Qualtrics link. After giving their consent, they were asked about their demographics, which can be found in Table 1. After this step, the participants were presented the Desirability of Control Scale (Burger & Cooper, 1979), the shortened Big Five Inventory (Gerlitz & Schupp, 2005), and the General Regulatory Focus Measure (Lockwood et al., 2002), to measure their scores on these control variables. These scales and the questions included in the scales were presented to the participants in randomized order, to ensure that an order effect (Perreault, 1975) would not influence their answers on these scales or further throughout the experiment.
After filling out the scales that measured the included control variables, the participants were first told that they were an employee at a company which held the name that was chosen by the previous participant. The participants were presented with the investment decisions that the previous participant had delegated, and they were asked to decide for these investments. The outcomes of the delegated investment decisions were added to the outcomes of the investment decisions of the previous participant, to calculate the final amount of money that this company held.
Results
Descriptive Statistics
Table 4. Descriptive Statistics
Notes. N = 152. * p < .05 ** p < .01
Variable Frequency Mean SD 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
1. Age 36.83 9.57 -
2. Female .41 .49 .14 -
3. Loss Frame .52 .50 .04 .02 -
4. High power condition .49 .50 .02 -.05 .04 -
Influence of Decision Previous Participants
Participants that took part in this study were first asked to conduct the investment decisions delegated by the previous participant. While the participants were told that every company is different, meaning that the choices of the previous participant were not necessarily ideal for their company as well, it could still be argued that, because of this set-up, the proportion of delegated investment decisions of the previous participant would influence the next participant’s decisions to delegate, which would interfere with investigating the influence of framing on delegation. A non-significant positive relationship was found between the proportion delegated investment decisions of the previous participant and the proportion of delegated investment decisions (r(151) = .14, p = .09). Therefore, decisions of the previous participants were not found to significantly influence participants decisions. However, it was decided to include the proportion of delegation of the previous participant as a control variable, as it is likely that this proportion somewhat influenced participant’s decision to delegate.
Hypotheses Testing
Hypothesis 1: Delegating Potential Gains and Losses. To test hypothesis 1A and 1B,
delegation for actual gains and actual losses (F(1, 143) = .37, p = .54).
Hypothesis 2: The Effect of Framing on Delegation. To test the main effect of
framing, whether loss-framing has a significant effect on the decision to delegate (Hypothesis 2a and 2b), a regression analysis was conducted. As can be seen in Table 2, the analysis was both conducted with (model 1b) and without (model 1a) including the covariates. With this analysis, it was established that the model without covariates was significant (F (1,150) = 5.71, p < .05), whereby 3.7% of the variance in proportion of delegated investment decisions was explained by the variables included in the regression analysis. Furthermore, the results of this analysis showed that framing is a significant determinant of the proportion of delegated investment decisions (b = .14, t(150) = 2.39, p < .05), whereby participants that were provided loss-framed decisions were more likely to delegate (b = .14, p < .05). These findings provide support for the hypothesis that loss-framed decisions are delegated more often than gain-framed decisions (Hypothesis 2b).
Table 2. Regression Analyses Main Effects Framing and Power on Delegation
Notes. N = 152. Variables were standardized. Standard Errors between parentheses. * p < .05 ** p < .01 *** p < .001
Hypothesis 3: The Main Effect of Power on Delegation.
To check whether the power manipulation led to significant differences in how powerful participants felt within the two power conditions, an independent sample T-test was conducted. With this independent sample T-test, participants in the low-power condition were indeed found to have, on average, a lower score on the power check (M = 64.14, SD = 32.20) compared to the participants in the high-power condition (M = 69.27, SD = 23.93). However, this difference was not found to be significant: t (141.97) = -1.12, p = .27, meaning that the power manipulation did not lead to significant differences within the level of power between the two power conditions.
To investigate hypothesis 3, two regression analyses were conducted (2a and 2b). The results can be found in Table 2. With this analysis, it was established that model 2a was not significant (F (1,150) = .10, p = .75), whereby 0 percent of the variance in the proportion of delegated investment decisions was explained by the variables included in the regression
Predictor Model 1a Model 1b Model 2a Model 2b
Constant .48*** (.04) .28 * (.14) .54*** (.04) .31 * (.14) Loss Frame .14* (.06) .14* (.06) - - High Power - - .02 (.06) .03 (.06) Female - -.02 (.06) - -.02 (.06) Age - .00 (.00) - .00 (.00) Extraversion - .00 (.04) - .00 (.04) Openness to Experience - .02 (.04) - .03 (.04) Promotion Focus - .01 (.04) - .00 (.04) Prevention Focus - -.00 (.03) - .01 (.04) Desirability of Control - -.02 (.04) - -.02 (.04)
Delegation previous participant - .15 (.09) - .15 (.09)
R .19 .26 .03 .18
analysis. Furthermore, the results of this analysis showed that the power manipulation is no significant determinant of the proportion of delegated investment decisions (b = .02, t(150) = .32, p = .75). Based on these findings, no support was found for the hypothesis that participants with high psychological power would be more likely to delegate compared to participants with low psychological power (Hypothesis 3).
The model in which the covariates were included was also not found to be significant (F (9,142) = .53, p = .85), whereby 3.2% of the variance in proportion of delegated investment decisions was explained by the variables included in the analysis. None of the covariates was found to be a significant determinant of delegation.
Hypothesis 4: The Moderating Effect of Power on the Relationship Between
Framing and Delegation. To investigate the hypothesized moderating effect of power, a
delegation (b = .05, t(140) = .45, p = .65). As provided in Table 4, none of the covariates was found to be a significant determinant of delegation.
Table 4. Regression Analysis Moderating Effect of Power on Delegation
Notes. N = 152. Variables were standardized. Standard Errors between parentheses. * p < .05 ** p < .01 *** p < .001
Further Investigation of the Effect of Power on Delegation and the Relationship
Between Framing and Delegation. As stated, it was established that the power condition did
not have a significant effect on delegation and the relationship between framing and delegation. However, scores on the power manipulation check were found to significantly correlate with the proportion of delegated investment decisions (r = -.22, p < .01). Participants’ scores on the power manipulation check could provide us with insights into the influence of individual differences in participants’ sense of power on delegation. However, it should be noted that the power check was measured by the end of the experiment, which makes it difficult to determine whether participants level of power influenced their delegation behavior, or whether their delegation behavior influenced their sense of power. A regression analysis was conducted to
Predictor Model 3a Model 3b
Constant .49*** (.06) .28 (.14)
Loss Frame .10 (.08) .12 (.09)
High Power Condition -.02 (.08) .00 (09)
Loss Frame x High Power Condition .07 (.12) .05 (.12)
Female - -.02 (.06) Age - .00 (.00) Extraversion - .00 (.04) Openness to Experience - .02 (.04) Promotion Focus - -.00 (.04) Prevention Focus - .01 (.04) Desirability of Control - -.02 (.04)
Delegation previous participant - .15 (.09)
R .20 .26
investigate the relationship between scores on the power check and delegation. The results of this analysis can be found in Table 5.
Table 5. Regression Analysis Main Effect and Moderating Effect Power Check
Notes. N = 152. Variables were standardized. Standard Errors between parentheses. * p < .05 ** p < .01 *** p < .001
It was established that the model without covariates was significant (F (1,150) = 7.88, p < .01), whereby 5% of the variance in the proportion of delegated investment decisions was explained by the variables included in the regression analysis. Furthermore, the results of this analysis showed that scores on the power check are a significant determinant of the proportion of delegated investment decisions (b = -.08, t(150) = -2.81, p < .01), meaning that participants with a high sense of power were less likely to delegate. These findings thus provide no support for Hypothesis 3.
The model in which the covariates were included was not found to be significant (F (9,142) = 1.64, p = .11), whereby 9.4% of the variance in proportion of delegated investment decisions was explained by the variables included in the analysis. As provided in Table 5, none
Predictor Model 4a Model 4b Model 5a Model 5b
Constant .55*** (.03) .36 ** (.14) .50*** (.04) .33* (.13)
Loss Frame - - .12* (.06) .12* (.06)
High Power Check -.08** (.03) -.11** (.03) -.15*** (.04) -.18*** (.05)
Loss Frame x High Power Check - - .14* (.06) .15* (.06)
Female - -.06 (.06) - -.05 (.06) Age - .00 (.00) - .00 (.00) Extraversion - .02 (.04) - .01 (.04) Openness to Experience - .01 (.04) - -.01 (.03) Promotion Focus - .04 (.04) - .06 (.04) Prevention Focus - .01 (.03) - -.00 (.03) Desirability of Control - -.01 (.04) - -.00 (.04)
Delegation previous participant - .16 (.09) - .17* (.09)
R .22 .31 .33 .40
of the covariates was found to be a significant determinant of delegation. When controlling for the included covariates, scores on the power check were still a significant determinant of the proportion of delegated investment decisions (b = -.11, t(142) = -3.15, p < .01).
To investigate the moderating effect of scores on the power check on the relationship between framing and delegation, another regression analysis using the Process macro Model 1 extension by Hayes (2017) was conducted. The results of this analysis can also be found in Table 5. With this regression analysis, it was found that the model without covariates (Model 5a) was significant (F(3,148) = 6.09, p < .01), whereby 11% of the variance in proportion of delegated investment decisions was explained by the variables included in the regression analysis. The interaction effect of framing and scores on the power check on the proportion of delegated investment decisions was found to be significant (b = .14, t(148) = 2.39, p < .05). High scores on the power manipulation check were found to weaken the effect between loss-framed decisions and delegation, meaning that participants in the loss frame were less likely to delegate when they had high scores on the power check. Therefore, these findings were contrary to Hypothesis 4a and 4b.
The model with covariates was also found to be significant (F(11,140) = 2.40, p < .01), whereby 16% of the variance in proportion of delegated investment decisions was explained by the variables included in the regression analysis. The interaction effect of framing and scores on the power check on the proportion of delegated investment decisions was significant (b = .15, t(140) = 2.52, p < .05), meaning that participants in the loss frame were less likely to delegate when they had high scores on the power check.
the proportion of delegated investment decisions is strongest when people have a high sense of power. Figure 2 provides a visual representation of the relationship between framing and delegation for scores on the power check.
Table 7. Conditional Effects of Loss Framing on Delegation.
Figure 2. Visual representation of the relationship between framing and delegation for scores on the power check.
Further Examination of the Dataset
Framing and the decision to delegate all – or none- of the investment decisions. Within this study, participants were presented with 16 investment decisions. To investigate whether the framing of these investment decisions had a significant effect on the amount of people that either chose to delegate all investment decisions (N = 30) or chose to make all decisions themselves (N = 19), a Chi-Square test was conducted. The other participants were not included in this analysis. The results of this test can be found in Table 8.
Conditional effects Effect SE 95% CI
Low power (-1 SD) -.03 .08 [-.19; .14]
Moderate power (M) .12 .06 [.00; .23]
Table 8. Frequencies Chi-Square Test
The analysis resulted in a Chi square value of 1.61 that was not significant (p = .20). Based on these findings, it can be concluded that framing did not have a significant effect on participants’ decision to either delegate all investment decisions or to make all decisions themselves.
Examination of separate investment decisions. To further examine whether framing
influenced participants’ decision to delegate for the separate investment decisions, a Repeated Measures ANOVA was conducted. Within the loss-frame condition, the proportion of delegation was found to significantly differ across the 16 investment decisions (F(10, 724) = 2.56, p = .01). Within the gain frame, the proportion of delegation was also found to differ significantly across the 16 investment decisions (F(10, 817) = 2.57, p = .00). Not only a significant difference was found in delegation between participants in the loss and gain frame, but significant differences were also found within participants investment decisions. For this reason, it was decided to take a closer look at the separate investment decisions. By calculating the frequencies of delegated investment decisions within both the loss- and gain-frame condition for each separate investment decision, it was investigated whether framing significantly influenced a participant’s decision to delegate by conducting a Chi-Square test for each separate investment decision. The results can be found in Table 9.
Delegated none Delegated all
Loss frame 6 15
Gain frame 13 15
Table 9. Frequency table and Pearson Chi-Square values delegated investment decisions
Delegated Loss frame Delegated Gain frame Pearson Chi-Square Maximum loss/gain € Loss/gain sure choice Chance (un)favorable outcome risky choice
Frequency Percentage Frequency Percentage Favorable Unfavorable
Actual Losses Investment 1 53 73.6 43 53.8 6.19* -€6000 -€4000 30% 70%
Investment 2 51 70.8 46 57.5 2.76 -€10,000 -€7500 20% 80% Investment 3 46 63.9 43 53.8 1.53 -€2000 -€1200 30% 70% Investment 4 46 63.9 41 51.3 1.49 -€5000 -€2250 40% 60% Investment 5 42 58.3 36 45 2.68 -€1000 -€500 10% 90% Investment 6 47 65.3 42 52.5 2.46 -€12,000 -€9000 20% 80% Investment 7 40 55.6 36 45 .95 -€8200 -€3200 60% 40% Investment 8 37 51.4 32 40 2.53 -€15,000 -€11,000 50% 50%
Actual Gains Investment 9 50 69.4 37 46.3 6.41** €8000 €2000 20% 80%
Investment 10 45 62.5 35 43.8 5.30* €12,000 €7000 10% 90% Investment 11 41 56.9 39 48.8 .46 €1000 €200 60% 40% Investment 12 40 55.6 31 38.8 4.34* €5000 €4000 20% 80% Investment 13 46 63.9 37 46.3 4.69* €7200 €2000 30% 70% Investment 14 47 65.3 49 61.3 .22 €700 €50 10% 90% Investment 15 36 50 37 46.3 .22 €9000 €4500 50% 50% Investment 16 46 63.9 37 46.3 4.69* €3500 €500 10% 90%
The percentages of delegation were lower within the gain frame for all investment decisions. The investment decision that was most often delegated was investment 1, with a 73.6% delegation within the loss frame, and only 53.8% delegation within the gain frame. The decision that was least often delegated was decision 12, with only 38.8% delegation within the gain frame. 55.6% Of the participants within the loss frame delegated this decision. Framing was found to have a significant effect on these two investments, whereby the loss framed decisions led to significantly more delegation compared to the gain-framed decisions. Out of the separate investment decisions on which framing had a significant effect, one investment decision involved an actual loss (investment decision 1), whereas five involved an actual gain (investment decision 9, 10, 12, 13, and 16). Hence, it was established that framing more often had a significant effect on decisions involving actual gains, compared to decisions involving actual losses.
Discussion
Most research on delegation has focused on the influence of characteristics of managers and/or subordinates, or the interaction between them (Richardson et al., 2002; Haselhuhn et al., 2017). Even though previous research indicated that the context in which leadership takes place vastly influences delegation (Liden & Antonakis, 2009), little research has been conducted on this context and its specific influence on delegation (Chen & Aryee, 2007). Therefore, a black box of the determinants of delegation remains, which this study aimed to open up by looking at the influence of the context on delegation.
Findings
delegation, but no support was found for these expectations. However, framing was found to significantly influence delegation, as loss-framed decisions led to significantly more delegation behavior compared to gain-framed decisions. These findings contrast previous research in which it was established that people are less likely to delegate important decisions (Leana, 1986; Yukl & Fu, 1999). As delegation is said to involve risk (Alter, 2001), these findings provide further proof for Tversky & Kahneman’s findings (1981) that loss-framed decisions lead to more risky decisions. Hence, this study provides further proof for the notion that framing influences delegation. However, whereas Tversky and Kahneman (1981) only looked at the influence of framing on an actual situation that was held constant, this research also looked at the influence of actual losses and gains on delegation, and the interaction between framing and actual losses and gains. It was established that loss-framed decisions more often led to significantly more delegation for investments that involved actual gains, compared to investments that involved actual losses. Thus, framing has more influence on decisions that regard actual gains, pointing to the interaction between framing and the actual situation to which this framing is applied. In addition, this research points out that the loss- or gain-framing of decisions is more important for delegation than the actual losses or gains that these decisions involve. As framing influences people’s perception of a situation, these research findings point out that the perception that people have of the outcomes of their decisions may be crucial in their decision to delegate.
detecting cancer early on) and gain messages focused on the benefits of less risky behavior (preventing cancer) were most effective in convincing women to get tested for cancer. These researches point to the different influence that framing may have on people’s behavior based on the content of the message that is influenced by this framing. In the current research, gain-framing may have effectively influenced participants behavior for investments involving actual losses, whereas such framing was not effective in influencing participants behavior related to actual gains. These findings provide further support for earlier research findings that the effectiveness of framing in promoting certain behavior is contingent on the content of the message provided to participants, which further points to an interaction effect between the actual situation and framing.
As stated, delegation largely reduces the effort and time that the controlling party needs to invest (Fehr et al., 2013). It is possible that participants provided with loss-framed investments considered these decisions as requiring more effort, which may have motivated them to delegate these decisions. In addition, participants may have experienced loss-framed decisions as more difficult, which, based on previous research findings that people are prone to delegate difficult decisions (Steffel & Williams, 2018), may have made these participants prone to delegate. These findings highlight a possible mediating effect of both the effort and difficulty that people ascribe to decisions on the relationship between framing and delegation, which would be interesting to investigate in future research.
have made them experience this impact. The set-up of this study may have made participants who were presented with loss-framed decisions more prone to delegate as a way to avoid experiencing the impact of these loss-framed outcomes. Previous research has indicated that an important motivator to delegate is when the consequences of the outcome of the delegated task are also delegated onto someone else (Bartling & Fischbacher, 2012). In addition, participants may not have believed that their delegated investment decisions were actually going to be conducted by a subordinate. This is supported by concerns of Hauser, Paolacci and Chandler (2018) regarding the use of MTurk as a participant pool, as they noted that experienced MTurk participants may know that deception often takes place, which influences their responses. Hence, participants potentially considered delegating as a way to avoid the impact of loss-framed decisions, as they may have thought that they were deceived into believing that their delegated investment decisions would influence their payment.
The findings that people may have been prone to delegate in the loss-frame condition to avoid the experience of a loss, are in line with prospect theory and the notion that people find it important to avoid experiencing losses (Tversky & Kahneman, 1979). Hence, the opportunity to avoid experiencing losses can be another determinant of delegation. In practice, this could mean that managers who are not confronted with the separate outcomes of their delegated decisions will be more likely to delegate, which is in line with research on mental accounting (Thaler, 1980). This entails the process in which people consider and evaluate their financial transactions, regarding which it was established that people react more positive to incentives when losses are integrated in their overall performance (Barberis & Huang, 2001). This could mean that managers are more positive about the incentives and benefits that coincide with delegation when the losses of their delegated decisions are not presented to them individually, but are integrated in the information about their overall performance.
important is also in line with previous research on regret aversion. According to Zeelenberg, Beattie, Van der Pligt and De Vries (1996), people experience regret when they find out that the alternative decision would have resulted in a better outcome. Regret influences decision making, as people are prone to avoid experiencing feelings of regret (cf. Zeelenberg, Beattie, Van der Pligt & De Vries, 1996). Previous research has indicated that regret aversion is a reason for people not to delegate, as delegation could lead to unfavorable choices made by the subordinate (Fehr et al., 2013). In this study, however, an investment decision made by the participant him/herself that resulted in an unfavorable outcome could lead to feelings of regret, as the other option could have led to larger gains or smaller losses. Regret theory assumes that people keep the emotional consequences of regret in mind when they make a decision (Zeelenberg et al, 1996). For that reason, participants may have chosen to delegate to avoid experiencing regret due to unfavorable outcomes of the investment decisions that they made themselves. As losses have a larger impact on people compared to gains (Kahneman & Tversky, 1979), people may be more motivated to avoid experiencing regret for loss-framed decisions. This could explain why participants in this research were found more likely to delegate loss-framed decisions. Furthermore, these findings support previous research findings that regret aversion influences delegation (Fehr et al, 2013).
Power and delegation. In this study, power was not found to significantly influence
offline research, it may not be as suitable to use as a power manipulation in online research, such as research conducted via MTurk, because the effect may not last long enough throughout the experiment.
Scores on the power manipulation check and delegation. Participants who felt more
powerful (based on their scores on the power manipulation) were found to be less likely to delegate. This was contrary to the expected effect of power on delegation and previous research findings (Haselhuhn et al., 2017). It should, however, be noted that it is difficult to determine the direction, or causality of this relation. Either people with higher scores on the power check were less likely to delegate or, oppositely, people who delegated less felt more powerful. This ambiguity is further elaborated on in the chapter on the limitations of this research.
One reason for the contrasting findings of this research compared to previous research can be linked to the perceptions of risk that are involved with decisions. Previous research has indicated that people with a higher sense of power usually have more optimistic perceptions of the risk involved with decisions, and are, furthermore, more likely to make risky decisions (Anderson & Galinsky, 2006). Moreover, high power often leads people to make overconfident decisions (Fast et al., 2011). It may be the case that participants with high power were more optimistic about the outcomes of their decisions, and, therefore, were less likely to delegate for the reason to avoid experiencing losses. This could explain why people with high power were actually found less likely to delegate.
whereas agentic traits are more often ascribed to men (Schuh, Bark, Van Quaquebeke, Hossiep, Frieg & van Dick, 2014). In relation to delegation, previous research has indicated that delegation is primarily an aspect of communal leadership (Akinola, Martin & Phillips, 2018). One aspect of leadership in which men and women have been found to differ, is in their motivation to possess power, as men are more motivated to possess power compared to women (Schuh et al., 2014). Therefore, power may be one of the factors that influences people’s leadership orientation. Rucker, Galinsky and Dubois (2012) established that high power is likely to evoke an agentic orientation to leadership, whereas low power prompts a communal orientation to leadership. Therefore, it can be argued that participants’ sense of power influenced their leadership orientation in the current research, whereby participants’ higher sense of power prompted an agentic orientation to leadership, which can explain why they were found to delegate less.
that loss-framing makes people prone to take risk, is strongest for people with a high sense of power. This points to the influence of high power on the relationship between framing and risk taking (i.e. delegation), which could be interesting to further investigate in future research.
Practical Implications
The results of this study have practical implications. As delegation can lead to numerous benefits (i.e. Bloom & van Reenen, 2001; Yukl & Fu, 1999; Muir, 1995; Riisgaard et al., 2016; Leana, 1987), firms should present managers with decisions that are loss-framed, as this will lead to more delegation. A situation in which firms could benefit from providing loss-framed information, is when top management provides middle management with, for example, information regarding an investment. Top management is said to depend on middle management for the achievement of organizational goals (Raes, Heijltjes, Glunk & Roe, 2011). Providing middle-management with loss-framed information will likely further help in the achievement of the goals of the organization.
Another finding of this research is that people who feel more powerful are less likely to delegate. Therefore, it could be interesting for firms that are planning on hiring a new manager, to test these potential managers’ sense of power. To benefit from the advantages that coincide with delegation, firms could choose to only hire managers who have a lower sense of power, as these managers would be more likely to delegate.
Limitations and Future Research
A first limitation of the current research stems from the fact that scores on the power manipulation check were used as moderator rather than the power condition. The causality of the relationship between scores on the power check and delegation is difficult to determine, as the power manipulation check was the last question of this research. It may be that participants’ scores on the power manipulation check were influenced by their choices for the investment decisions. In that case, the manipulation check measured how powerful the participants felt as a result of having conducted the investment decisions, rather than how powerful they felt as a result of the power manipulation. This indicates that using scores on the power manipulation check as a power construct would not be ideal, as the relationship between these two constructs would actually be reversed. Furthermore, the power manipulation check as a power construct may have low content validity, as it is difficult to check whether this construct properly measured participants’ sense of power. A good alternative for future research could be to use the Sense of Power scale (Anderson, John & Keltner, 2012), which measures the extent to which people believe that they have influence over other people in their day-to-day lives. This scale would allow future research to properly measure participants scores on psychological power, and to examine the influence of individual differences in people’s sense of power on delegation. In addition, based on the notion that the effect of the power manipulation may have worn off by the end of the experiment, future research could choose to implement multiple power manipulations, for example another one halfway through the experiment, to potentially maintain and strengthen the effect of the power manipulation. It could also be helpful to implement several power manipulation checks throughout the research, to be able to better determine whether the power manipulation influenced participants’ sense of power.
of the outcomes of these decisions. In the current research, delegation was a way for participants to avoid experiencing the impact of losses, especially when they did not believe that their delegated investment decisions would actually be conducted. While previous research has established that delegation involves risk (Alter, 2001), it can actually be argued that the decision not to delegate was the riskier option in the current research. This is due to the fact that the set-up of this study made delegation a way for participants to avoid experiencing actual losses. Hence, it was certain that delegation would not lead to the experience of a loss, whereas choosing to make a decision themselves could lead participants to experience the impact of a loss. As high power has been found to make people more likely to take risk (Anderson & Galinsky, 2006), this could further explain why people who felt more powerful delegated less. It should be noted that in real businesses, delegation does not necessarily imply that managers are not confronted with unfavorable outcomes of the decisions that they chose to delegate. This makes it difficult to know whether the findings of this research regarding delegation would also apply to delegation within actual firms, or to situations in which people know for certain that their delegated investment decisions influence them.
delegated decisions, as this information lowers the perceived risk involved in delegation. This further points to the role that feedback may play in the decision to delegate, which makes it an interesting topic to investigate in future research.
This research established that framing can be an important context variable that directly influences delegation, which provides further support for the need for research on the direct effect of context on leadership (Osborn & Marion, 2009; Oc, 2018). Based on the established effect of framing on delegation, it would be interesting for future research to further investigate this effect on leadership, for example by applying framing to different kinds of tasks, moving away from the use of investment tasks. In addition, it would be interesting to examine potential mediators of the relationship between framing and delegation, by investigating the effect of the effort and/or difficulty that is ascribed to decisions on the relationship between framing and delegation.
Conclusion
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