Compensation or feedback: Motivating performance in multidimensional tasks

Accounting, Organizations and Society 50 (2016) 27e40

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Compensation or feedback: Motivating performance in multidimensional tasks Margaret H. Christ a, Scott A. Emett b, William B. Tayler c, David A. Wood c, * a

University of Georgia, United States Arizona State University, United States c Brigham Young University, United States b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 October 2013 Received in revised form 17 February 2016 Accepted 21 March 2016

Employees often perform tasks with multiple dimensions. In this study, we examine how employees' performance on multidimensional tasks differs under different control structures. We conduct two experiments in which we manipulate the presence of compensation controls and the presence of feedback controls on multiple task dimensions. Our findings suggest that when employees are compensated on multiple dimensions they commit to multiple goals and divide their attention among those task dimensions. However, when feedback controls are implemented on one task dimension with compensation controls on another dimension, employees can improve performance on individual dimensions as well as their overall task performance. As a result, we find that employee performance on a multidimensional task can be higher when firms compensate employees on one task dimension and provide feedback on the other task dimension than when firms compensate on both task dimensions. This study highlights the benefits of complementing compensation-based controls (i.e., incentive pay) with noncompensation based controls (e.g., feedback), and provides a theoretical basis to help explain the prevalence of this approach in practice. © 2016 Elsevier Ltd. All rights reserved.

JEL Codes: M40 M41 M49 M52 Keywords: Feedback Controls Incentives Compensation Multidimensional tasks

1. Introduction In today's complex business environment, employees often perform tasks that involve multiple performance dimensions. In such settings, employees must decide how to allocate their effort among separate tasks or task dimensions, and firms must decide how to best encourage employees to attend to those tasks or task dimensions. In practice, firms often use a mix of controls, including some which provide compensation for performance and others which provide feedback about performance, to influence employee performance (Simons, 1987). In this paper, we investigate whether using compensation-based controls (e.g., incentive pay) in conjunction with feedback-based controls can improve performance relative to a multidimensional compensation contract (i.e., using only compensation-based controls to influence behavior). Researchers and practitioners have numerous and divergent

* Corresponding author. E-mail address: [email protected] (D.A. Wood). http://dx.doi.org/10.1016/j.aos.2016.03.003 0361-3682/© 2016 Elsevier Ltd. All rights reserved.

views on how compensation-based controls (hereafter, “compensation controls”) and feedback-based controls (hereafter, “feedback controls”) should be used within organizations (Haun, 1955; Merchant, 1985; Merchant & Van der Stede, 2007; Simons, 1987, 1995; Zimmerman, 2011). Academic research has traditionally investigated these two constructs separately or has investigated them simultaneously without considering the individual control components (e.g., incentive pay is often coupled with performance feedback). Indeed, it would be challenging to investigate our research question using field or archival data because compensation and feedback controls are often used together in practice (Ittner, Larcker, & Meyer, 2003; Simons, 1995), and thus it is difficult to differentiate the effects of these two control types in real-world settings. We leverage the experimentalist's comparative advantage and design a setting that tests our theory as well as the separate and joint effects of these two control types. A better understanding of the relationship between these control types yields insights that can be used to improve the design of control systems within organizations. Firms often make trade-offs between contract completeness

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and contract complexity, such that the optimal contract is often incomplete (Choi, Hecht, & Tayler, 2012; Williamson, 1985). Incomplete contracts abound in practice (Williamson, 1985; Holmstrom & Milgrom, 1991; Baker, 1992, 2000, 2002; Ittner, Larcker, & Rajan, 1997; Ittner & Larcker, 1998; Banker, Potter, & Srinivasan, 2000; Bailey, Hecht, & Towry, 2011). The prevalence of incomplete contracts is likely due to managers' understanding of the natural, cognitive limitations of employees (Choi et al. 2012), coupled with managers' inability to precisely measure each important dimension of performance (Holmstrom & Milgrom, 1991). Instead, firms typically use a combination of compensation controls and feedback controls to induce desired employee behavior (Simons, 1987). Prior research shows that controls that are not linked to compensation, such as feedback controls, can improve performance despite the lack of monetary incentives (Christ, Emett, Summers, & Wood, 2012). We investigate whether feedback controls can be used in a multidimensional setting to complement compensation controls and improve performance relative to a purely compensation-based control system. We draw from psychology theory on goal conflict that shows it is difficult for individuals to respond to multiple (conflicting) goals simultaneouslydas employees do in multidimensional tasks (e.g., Kehr, 2003; Kuhl, 1981; Oettingen, Grant, Smith, Skinner, & Gollwitzer, 2006; Morsella, Ben-Zeev, Lanska, & Bargh, 2010; Masicampo & Baumeister, 2011). When individuals experience goal conflict, they divert cognitive capacity from task realization to selferegulatory processes, resulting in an overall loss of productivity. Based on this theory, we predict that when firms implement compensation controls on all task dimensions, employees increase their commitment to multiple goals (Hollenbeck & Klein, 1987; Locke and Latham 2002; Prendergast, 1999), and experience goal conflict as a result. We also predict that when firms implement compensation controls on one task dimension and feedback controls on other task dimensions, employees can concentrate on one focal goal (the task dimension tied to compensation) while relying on the feedback controls to reduce the costs of goal realization on other, non-compensated dimensions. Accordingly, we expect that, relative to a situation in which compensation is tied to all task dimensions, employees subjected to compensation controls on one dimension and feedback controls on other dimensions will perform better on task dimensions separately, as well as exhibit greater overall performance in general. To investigate the influence of compensation controls and feedback controls on employee performance, we conduct two experiments in which participants complete a simplified data-entry task that has two measureable task dimensions. In both experiments we vary the type of control (i.e., feedback vs. compensation) imposed on the task dimensions and compare the performance effects of the controls on each task dimension, as well as performance on the task as a whole. Consistent with theory, we find that employees perform better on individual task dimensions when firms implement a compensation control on one task dimension and a feedback control on the other dimension than when all task dimensions are compensated. Further, overall employee performance on a multidimensional task is higher when employees are subjected to a compensation control on one dimension and a feedback control on the other dimension than when employees are subjected to compensation controls on both task dimensions. Overall, our findings suggest that when compensation controls and feedback controls are used on different task dimensions, employees can rely on the feedback controls to help them achieve an organization's stated objectives, while simultaneously pursuing high performance on the organization's objectives that are reinforced with compensation controls. These results suggest that even

in settings where compensating employees on all task dimensions is possible, contracts that include controls that are not tied to compensation can be more effective than contracts that compensate all dimensions of performance. Our study makes several contributions to research and practice. First, our study extends prior agency research and shows that feedback controls can reduce some of the unwanted employee behavior that occurs when employees are compensated on multiple performance dimensions. Prior agency research indicates that “high-powered” incentives (e.g., compensation) can lead employees to neglect important dimensions of their work (Baker, 1992; Holmstrom & Milgrom, 1991). Our study shows that a combination of compensation and feedback controls can improve employee performance relative to compensation on all dimensions. Second, our study contributes to accounting literature on control system design in multi-dimensional settings. Prior work investigates how compensation controls influence performance on different task dimensions (e.g., see Kachelmeier, Reichert, & Williamson, 2008; Kachelmeier & Williamson, 2010) and on how feedback controls influence performance on different task dimensions (e.g., see Christ et al. 2012; Hannan, McPhee, Newman, & Tafkov, 2013). Our study investigates compensation controls and feedback controls together, and reveals that these two control types can serve as effective complements that together improve operational performance. The results of this study also have important implications for practitioners. When designing control systems, organizations should consider how employees will respond to the entire set of controls to which they are subjected. Our results show that there can be unintended costs when compensation controls are tied to each task dimension, which can hurt employee and organizational performance. Instead, we show that employee performance can be enhanced by using a mix of compensation- and feedback-based controls to motivate performance on multidimensional tasks. These results are particularly important given the prevalence of multidimensional tasks and job responsibilities within organizations. Employees today face increasingly varied and competing tasks, in part because economic downturns have caused companies to downsize their workforce and transition more job responsibilities to remaining employees (Frauenheim, 2011; Frauenheim & Nikravan, 2014). It is critical that organizations understand how to motivate employees in this environment. 2. Theory and hypotheses Management accounting research typically defines controls as processes that help “ensure the proper behaviors of the people in the organization” (Merchant, 1985, 4). While companies employ various controls simultaneously to achieve their objectives, accounting research has traditionally focused on the effectiveness of different types of controls individually without explicitly investigating the joint influence of multiple control types. Prior control research (e.g., Ouchi & Maguire, 1975; Merchant, 1985; Merchant & Van der Stede, 2007) examines whether inputor output-based controls are more effective in various organizational environments.1 Other studies have focused on the effectiveness of various forms of controls over financial reporting (e.g., see Trotman & Wood, 1991; Hansen, 1997; Caplan, 1999; Krishnan, 2005; Doyle, Ge, & McVay, 2007; Ashbaugh-Skaife, Collins, Kinney,

1 For example, research shows that output-based controls are better suited than input-based controls when employees need credible evidence of their performance because the organization is large and decentralized and supervisors lack knowledge of the tasks they perform or technologies they use (Ouchi & Maguire, 1975).

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& LaFond, 2008, 2009; Hammersley, Myers, & Shakespeare, 2008; Hoitash, Hoitash, & Bedard, 2009; Christ et al. 2012). Additionally, research in management accounting focuses on the effectiveness of incentive compensation controls (e.g., see Simons, 1995; Tayler & Bloomfield, 2011; Merchant & Van der Stede, 2007), and investigates the decision-influencing effects of diverse incentives structures (Bonner & Sprinkle, 2002).2 However, there is a paucity of research examining the entire “package” of controls that organizations use to motivate appropriate employee behavior (Malmi & Brown, 2008). Otley (1999) contends that researchers should study multiple control systems at once to understand the relationship between controls and their performance effects.3 In this study, we explore the joint impact of two different control types: compensation controls and feedback controls. We define compensation controls as processes that influence behavior via incentive compensation, but do not provide immediate performance feedback. For example, an office secretary may have his bonus tied to performance. This creates a compensation control that influences performance year-round. At year-end, when the secretary receives the bonus, he implicitly (if not explicitly) receives feedback as well (a feedback control). However, at any given point in time during the year the compensation control works independently of the feedback controldthe secretary's behavior is influenced by the expectation of performance-based pay, absent immediate performance feedback. Prior work has demonstrated that compensation controls influence decision making, even absent performance feedback information (e.g., see Choi, Hecht, & Tayler, 2013). We define feedback controls as processes that influence behavior via performance feedback, but are not explicitly tied to compensation. For example, strategic performance measurement systems within organizations often include multiple measures of performance that are not tied to compensation directly, but that provide immediate performance feedback (e.g., see Kaplan & Norton, 2001). Research from psychology and accounting shows that feedback can enhance employee performance by providing information about employees' achievement of stated goals (Chenhall, 2005). Much of the feedback literature relies on theories of learning (e.g., Hirst & Luckett, 1992) and motivation (e.g., Carver & Scheier, 1981; Latham & Locke, 1991; Wood & Locke, 1990) to explain the positive effect of feedback on performance. For example, Kluger and DeNisi (1996) propose that employees can learn how to best perform a task through performance feedback, generating and testing hypotheses about task realization, adopting task approaches that enhance performance, and abandoning task approaches that deteriorate performance. Prior work has demonstrated that feedback controls influence individual decision making, even absent explicit compensation implications (e.g., see Christ et al. 2012). Academic research on the differential and joint impact of feedback and compensation controls is limited. Often, these two constructs are treated as one, because performance-based compensation can also

2 Merchant and Van der Stede indicate that “Pay for performance is a prominent example of a type of control that can be called results controls because it involves rewarding employees for generating good results” (2007, p 25). Auditing and financial reporting research, however, typically views incentive compensation as a factor that increases the risk of material misstatement, rather than a control (e.g., see Heiman-Hoffman, Morgan, & Patton, 1996; Gul, Chen, & Tsui, 2003). 3 One alternative to using separate controls to influence behavior on multiple performance dimensions is to use a single, aggregate measure of performance that captures behavior from all important performance dimensions. For example, a firm could simply base employee pay entirely on stock value. However, aggregate measures are often noisy and are not controllable by employees (Deason, Hecht, Tayler, & Towry, 2014), and lag significantly behind actual employee actions (Kaplan & Norton, 1992). Thus, in practice, firms often use controls on individual dimensions of performance.

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provide immediate performance feedback (e.g., see Malina & Selto, 2001).4 One notable exception is a study by Ashton (1990), in which participants predict bond ratings based on three financial ratios. Ashton (1990) manipulates the type of performance pressure placed on participants at four levelsd(1) no pressure, (2) financial incentives, (3) performance feedback, or (4) the requirement to justify the decision. He also manipulates the presence or absence of a decision aid that mechanically computes a bond-rating score based on a statistically-derived linear combination of the three ratios. He finds that each type of performance pressure improves performance relative to no pressure when the decision aid is absent, but can decrease performance relative to no pressure when the decision aid is present. Thus, Ashton (1990) investigates the effects of compensation and feedback, separatelydbut not jointlydin a single-dimensional task.5 Because compensation and feedback often operate simultaneously within organizations, we seek to extend prior work by investigating the interactive effect of these two control types in a multidimensional task. Multidimensional tasks require employees to attend to several different tasks as part of their job (e.g., processing transactions and supervising subordinates), or to perform a single task with multiple dimensions (e.g., maximize customer satisfaction while minimizing the cost of sales). Although in our experiments (described subsequently) we operationalize our constructs using a single task with multiple dimensions, our theory and hypotheses (discussed below) should apply to both settings. 2.1. Multidimensional tasks and goal conflict Employees adopt goals related to the tasks they perform (Locke & Latham, 1990). When performing multidimensional tasks employees must balance multiple goals, and these goals often conflict (Baker, 1992; Holmstrom & Milgrom, 1991; Kachelmeier et al. 2008; Kachelmeier & Williamson, 2010; Prendergast, 1999). For example, in manufacturing organizations, employees must balance the goal of producing output quickly with the goal of producing high-quality output. However, high quality output often comes at the cost of quick output (and vice versa). Psychology research suggests that individuals are not good at appropriately dividing attention among multiple goals (Emmons & King, 1988; Emmons, King, & Sheldon, 1993; Kehr, 2003; Krishnan, Luft, & Shields, 2005; Lee, Locke, & Latham, 1989; Locke, Smith, Erez, Chah, & Schaffer, 1994; Payne, Bettman, & Johnson, 1993; Shah & Kruglanski, 2002; Slocum, Cron, & Brown, 2002). In particular, when individuals attempt to balance multiple goals as opposed to concentrating on one focal goal, they divert cognitive capacity from task realization to three additional selferegulatory processes, resulting in an overall decrease in productivity. First, individuals must divert cognitive capacity toward the selection of a suitable strategy from a broader set of task strategies (Kehr, 2003; Kuhl, 1981). Multiple goals create more paths of action, and when faced with several different paths of action, individuals deliberate over the best course of action instead of acting. Second, individuals divert cognitive resources toward monitoring their performance and evaluating their strategies (Kehr, 2003). When pursuing a single focal goal, individuals can monitor performance on that focal goal in a straightforward fashion; however, when

4 Indeed, many incentive pay systems use both compensation and feedback controls simultaneously (e.g., commission-based pay typically reports sales and compensates for increased sales). 5 Though feedback controls and decision aids are similar in that they both influence behavior, decision aids are provided prior to, and are thus independent from, actual decisions; and feedback comes after, and is a function of, actual decisions.

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pursuing multiple goals, individuals must attend to and integrate multiple pieces of information to monitor their performance. This consumes working memory and other executive functions that could instead be used for task realization (Anderson, 2009). Third, individuals divert cognitive capacity toward coping with poor performance on one or more goals. Individuals who pursue multiple goals often struggle to perform adequately on all of their goals, and they typically feel bad when they fail to accomplish their goals (Oettingen et al. 2006).6 People consciously brood over unfulfilled goals (Morsella et al. 2010) and allow their unfulfilled goals to consume the brain's executive functions (Masicampo & Baumeister, 2011). Thus, when individuals pursue multiple goals as opposed to a single focal goal, they typically perform worse on tasks. Because goal conflict can impair task performance, employees often perform better on multidimensional tasks when they prioritize one goal (or task dimension) over other goals (or task dimensions) (Payne et al. 1993, p.10; Hogarth, 1987).7 Prioritizing one goal over others reduces goal conflict by (1) narrowing the set of task strategies that employees must choose from, (2) reducing the number of dimensions for which employees must monitor and evaluate performance, and (3) reducing the importance of unfulfilled goals to the employee. In other words, individuals can reduce goal conflict by establishing a focal goal and demoting all other goals as subordinate to the focal goal.

2.2. Compensation controls in multidimensional tasks Compensation controls are a powerful tool for directing employees' attention to various tasks. Incentive pay appeals to employees' self-interest and directs employees to expend more effort on the dimensions of their task or job that are rewarded and less effort on the dimensions of their task or job that are not rewarded (or that are rewarded to a lesser degree), all else equal (Holmstrom & Milgrom, 1991; Prendergast, 1999, 8).8 Thus, we first make the straightforward prediction that in a multidimensional task where a single task dimension is compensated, employees will allocate more effort to another task dimension if that dimension is also compensated than if it is not compensated. For example, assume management wants to produce a high quantity of high-quality products. When the firm implements a compensation control on both quantity and quality, employees will produce higher quality output than when the firm implements a compensation control on quantity but no compensation control on quality. Specifically, we predict that: H1a. In a multidimensional task where a compensation control is implemented on a given task dimension, individuals will perform

6 Ashton (1990, p.150e151) makes a similar argument in a single-dimensional task setting. Relying on the Yerkes and Dodson (1908) inverted-U relation, he argues that incentives, feedback, and justification, when paired with decision aids that imply high performance standards, may actually harm decision-making by causing workers to feel anxious. 7 Payne et al. (1993) refer to strategies that do not allow individuals to make trade-offs among alternatives as “non-compensatory strategies.” In the decisionmaking literature, this is often used to describe individuals' prioritization of certain characteristics when making a choice between alternatives. For example, when choosing between two jobs, an individual may have a preference for location and no other attribute of the job can compensate for inadequate location. 8 Economic theory predicts that when responding to monetary incentives in a multidimensional task, individuals divide effort among task dimensions in complex ways. For example, Holmstrom and Milgrom (1991, 29) assume that employees have a concave wage function (implying diminishing monetary returns to effort on each task dimension) and a convex personal cost function (implying an increasing personal cost to effort in each task dimension). Ceteris paribus, economic theory predicts that individuals will shift more effort to a given task dimension when incentives for that task dimension are higher relative to other dimensions.

better on a separate task dimension when a compensation control is implemented on that dimension than if a compensation control is not implemented on that dimension.9 The primary objective of this study is to determine whether a combination of compensation controls and feedback controls can improve employee performance relative to a setting with compensation controls on all dimensions. To that end, we next consider whether implementing compensation controls on multiple task dimensions (as considered in H1a) affects performance in ways consistent with employees committing to multiple goals and dividing attention among the multiple dimensions of the task. As discussed above, goal conflict arises when employees commit to multiple, competing goals. Because compensation on multiple task dimensions will cause employees to increase their commitment to multiple goals (Hollenbeck & Klein, 1987; Locke and Latham 2002; Prendergast, 1999), multidimensional compensation will likely lead to goal conflict. Accordingly, performance will be lower on any given dimension than it would be if attention were dedicated entirely to a single dimension. For example, if management implements a compensation control on quality only, employees will likely make quality their focal goal and produce higher quality output than if management implements compensation controls on both quantity and quality. Thus, in conjunction with H1a, we predict that: H1b. In a multidimensional task, a compensation control on one task dimension will increase performance on that dimension more if other dimensions are not subject to compensation controls than if other dimensions are subject to compensation controls.

2.3. Comparing compensation controls and feedback controls Together, H1a and H1b predict that while compensation controls can improve performance, implementing compensation controls on multiple dimensions of a task will cause employees to commit to multiple goals and divide their attention among multiple task dimensions as a result. In this section, we discuss whether firms are better off using compensation controls on all task dimensions, or if they should consider using feedback controls in conjunction with compensation controls in multidimensional tasks. Drawing on prior research (Christ et al. 2012), we expect that feedback controls can improve performance on a given dimension, even when other dimensions are compensated. That is, although employees may focus primarily on the compensated task dimension, they will perform better on the non-compensated task dimension if it is subject to a feedback control than if there is no control imposed. Goal conflict theory further suggests that compensation and feedback controls will affect employee performance in different ways when implemented in multidimensional tasks. As previously described, compensation controls provide explicit information to the employee about where they should focus their attention. Thus, a compensated dimension is most likely to be the employee's focal goal. However, when multiple task dimensions are compensated, employees will likely experience heightened goal conflict, which can impair performance. Feedback controls, on the other hand, are less likely to result in

9 Although our theory and hypotheses speak to tasks that are “multidimensional,” our tests of these hypotheses focus on the most basic of these settings using a two-dimensional task.

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the same level of goal conflict. There are two primary differences between compensation controls and feedback controls that lead to different predictions about how they will affect employee performance when used as part of a portfolio of controls in a multidimensional setting. First, because feedback controls are not linked to employees' compensation, individuals are less likely to abandon their focal, compensated, task dimension in the presence of a feedback control on a separate dimension (Locke and Latham 2002). Although feedback controls are likely to shift some attention away from the compensated task dimension toward the dimension with the feedback control, employees will likely continue to prioritize the compensated performance dimension. Thus, in a setting where organizations implement a compensation control on one dimension and feedback controls on other dimensions, employees can pursue a simple task strategy that does not divert cognitive capacity from task realization to strategy selection or to effective coping mechanisms (the first and third forms of goal conflict discussed above). Second, feedback controls can reduce monitoring costs on nonfocal task dimensions by providing performance-relevant information employees would otherwise expend cognitive effort pursuing. That is, feedback controls can help employees overcome the second form of goal conflict discussed above by automating performance monitoring and performance assessment. Thus, feedback controls simplify employees' efforts to monitor and refine their task approach. This suggests that when organizations implement compensation controls on one task dimension and feedback controls on other dimensions, employees do not experience goal conflict to the same degree as when firms implement compensation controls on all dimensions. Thus, based on goal conflict theory, we predict that employee performance will be better (on each dimension) if firms employ a mix of compensation and feedback controls rather than implement compensation controls on all task dimensions. Returning to our quantity-quality example, this theory predicts that implementing compensation controls on quantity and feedback controls on quality will result in greater performance on both quantity and quality than if the firm employed compensation controls on both quantity and quality. We formally state this prediction in the following two hypotheses: H2a. In a multidimensional task where a compensation control is implemented on a given task dimension, performance in the other dimension will be higher if the other dimension is subject to a feedback control rather than a compensation control. H2b. In a multidimensional task where a compensation control is implemented on a given task dimension, performance in that dimension will be higher if the other dimension is subject to a feedback control rather than a compensation control. Importantly, Holmstrom and Milgrom (1991) argue that if an incentive compensation system weights multiple task dimensions equally, employees will not always allocate their effort equally among all dimensions. All else equal, employees will allocate more of their effort toward task dimensions that they perceive to be less “noisy,” more easily measured, or more easily maximized. Accordingly, if employees focus their available attention primarily on performance in one of the multiple dimensions subject to controls in a given setting, we may only find support for either H2a or H2b. In other words, though implementing compensation controls on multiple dimensions may hurt performance more than using a mix of compensation controls and feedback controls, the diminished performance may not be seen in all task dimensions. However, evidence supporting either H2a or H2b (or both) suggests that feedback controls can be used to avoid the goal conflict

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that arises when compensation controls are implemented on multiple task dimensions.10 2.4. Overall performance in multidimensional tasks So far our hypotheses relate to employee performance on each task dimension separately. We now consider the implications of our prior hypotheses on overall performance (i.e., both task dimensions together). We predict in H2a and H2b that employees will perform better on each dimension, separately, when firms implement a combination of compensation and feedback controls than when firms implement compensation controls on all task dimensions. As discussed earlier, combining feedback and compensation controls allows employees to pursue a focal goal (i.e., the compensated dimension) while relying on a feedback control to reduce monitoring costs for the secondary goal. Thus, the theory underlying H2 suggests that overall performance should be higher when firms implement both compensation and feedback controls on task dimensions than when firms implement compensation controls on all task dimensions. (See the Appendix for a brief mathematical summary of this prediction). We state this prediction as follows: H3. In a multidimensional task where one task dimension is compensated, overall performance will be higher when a feedback control is implemented on the other dimension than if a compensation control is implemented on the other dimension.

3. Experiment 1 3.1. Experimental task and manipulations To test our predictions, we use two simplified data-entry experiments in which we manipulate, between participants, whether two data-entry task dimensions are subject to compensation controls, feedback controls, or neither.11 We use the task dimensions (1) data-entry speed and (2) data-entry accuracy to examine the effects of compensation and feedback controls.12 In Experiment 1, we use an incomplete, 2
3 (task dimension
control type) between-subject design to examine our hypotheses. Specifically, we manipulate the controls on speed by either having compensation controls or no controls. We manipulate controls on accuracy by having compensation controls, feedback controls, or no controls.13 In all conditions, we compare individuals' performance on the accuracy and speed dimensions to individualspecific, data-entry pre-test results, allowing us to measure performance improvement following implementation of the compensation and feedback control manipulations. One hundred twenty-five undergraduate business students

10 Importantly, our theory does not suggest that using feedback controls, coupled with compensation controls, will always result in better performance than using compensation controls on multiple task dimensions. Rather, the theory suggests that using feedback controls with compensation controls can result in better performance than using compensation controls across multiple dimensions. We view this investigation as a first step in understanding the joint impact of feedback controls and compensation controls. Many questions remain (e.g., What factors influence the relative tradeoff between feedback controls and compensation controls?). 11 The data-entry task is adapted from the task used by Christ et al. (2012). 12 The specific task dimensions we use in our study, as well as the specific form of the controls, may influence the generalizability of some of our findings. We discuss this possibility further in Section V. 13 We do not need all nine cells of the full 3
3, between-participant design to test our hypotheses. We provide a summary of a 3
3 full factorial design, as well as the implications for our hypothesis tests in the Appendix.

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participated in Experiment 1.14 Participants completed seven phases of the experiment: (1) an introduction, (2) demographic questions, (3) a pre-test data-entry task, (4) further instructionsdin which we introduce the specific manipulations, (5) a comprehension test, (6) the main data-entry task, and (7) a posteexperiment questionnaire. We describe each phase of the experiment below. 3.1.1. Introduction Participants read the following as part of the introduction to the study:

2.

3.

“For today's study you will perform a data-entry task (i.e., a typing tutor type task). We want you to type as quickly and as accurately as possible.” (emphasis as in the original) Following previous studies that examine similar constructs (e.g., see Shalley, 1991; Kachelmeier et al. 2008), we described both goals (data-entry accuracy and speed) so that participants in all conditions would be operating under the same explicit goalsdto type quickly and accurately. Participants then read specific instructions on how to complete the task, including an example of how the data-entry task worked. 3.1.2. Demographic questions After participants finished reading the introduction, they completed a questionnaire providing demographic information. As part of this set of questions, we asked participants to provide their year in school, gender, GPA, and information relating to their perceived data-entry speed and experience.15 3.1.3. Pre-test data-entry task Next, participants began the pre-test data-entry task, which consisted of the same 1011 characters (12 lines of data on 3 screens) for all participants.16 We did not implement feedback controls on any of the characters, nor did we implement compensation controls (participants were all paid a flat rate during this pre-test task). Performance on this task allowed us to establish a baseline performance level specific to each participant absent controls. 3.1.4. Further instructions After finishing the pre-test task, participants read an additional set of instructions. In these instructions, we reminded participants of their goal to type quickly and accurately (“We want you to type as fast and as accurately as possible”). Participants were then given additional instructions based on their randomly-assigned experimental condition (i.e., the manipulations), as follows: 1. Speed Compensation condition: A compensation control was implemented on speed. Participants were told, “You will be compensated on these screens based on how quickly you type the words on the screen. Therefore, the faster you type, the more money you will earn” (emphasis as in original). No control was

14 We used student participants for our task, as opposed to more experienced professionals, because our data-entry task (discussed below) is well-suited to the abilities of a typical undergraduate student. As such, we expect results stemming from this study will be generalizable to multiple settings where individuals perform multidimensional tasks for which they have been hired. 15 Random assignment of participants to conditions appeared to be successful as the demographic variables do not differ significantly between conditions (all pvalues > 0.10). Furthermore, none of the demographic variables were statistically related to our measures of speed, accuracy, or overall performance. 16 For both pre-testing and testing, the material to be typed came from a news article about Barry Lunt the founder of a start-up technology company (Millenniata, 2011).

4.

5.

imposed on the accuracy dimension of the data-entry task in this condition.17 Accuracy Compensation condition: A compensation control was implemented on accuracy. Participants were told, “You will be compensated on these screens based on how accurately you type the words on the screen. Therefore, the more accurate your output, the more money you will earn” (emphasis as in original). No control was imposed on the speed dimension of the dataentry task in this condition. Speed Compensation/Accuracy Compensation condition: A compensation control was implemented on speed and on accuracy. Participants were told, “You will be compensated on these screens based on how quickly and accurately you type the words on the screen. Therefore, the faster you type, and the more accurate your output, the more money you will earn” (emphasis as in original). Accuracy Feedback condition: A feedback control was implemented on accuracy. Participants were told “On the next screens there are controls on some (but not all) of the letters such that if you do not enter the correct letter and there is a control on that letter the missed letter will be highlighted.” No control was imposed on the speed dimension of the data-entry task in this condition. Speed Compensation/Accuracy Feedback condition: A compensation control was implemented on speed and a feedback control was implemented on accuracy. Participants were told, “You will be compensated on these screens based on how quickly you type the words on the screen. Therefore, the faster you type, the more money you will earn” (emphasis as in original). Participants were also told, “On the next screens there are controls on some (but not all) of the letters such that if you do not enter the correct letter and there is a control on that letter the missed letter will be highlighted.”

3.1.5. Comprehension test After reading the instructions, participants answered a series of comprehension questions to determine whether they understood the manipulations. If participants answered any questions incorrectly, the relevant instructions were repeated and the participant was required to answer the questions correctly before beginning the main data-entry task of the experiment. 3.1.6. Main data-entry task Following the comprehension test, participants completed the main data-entry task, consisting of 3209 characters (38 lines, 8 screens of data). For participants assigned to conditions with feedback controls, we assigned these feedback controls randomly to approximately 20 percent of the characters.18 We imposed

17 Incentive compensation was based on pre-testing such that the scale was designed to create a uniform distribution of payouts. Consistent with several studies in experimental economics (e.g., see Bloomfield, O'Hara, & Saar, 2009a and Bloomfield, Tayler, & Zhou, 2009b), we did not inform participants of the exact conversion rate between their actions and their payouts; but we did provide the range and expected average payoff that would be earned ($5 to $15 with an average payout of $10). We note that this design choice mirrors many settings in practice, where vague compensation structures are common (Gibbs, Merchant, Van der Stede, & Vargus, 2003; Ittner et al. 2003; Rajan & Reichelstein, 2006). 18 We imposed feedback controls on 20 percent of the characters to mirror internal control environments in practice, where controls are not imposed on every facet of employees' tasks. Although the control is not in force for all characters, it still reduces the participants' monitoring costs related to their accuracy goal. When the control is (imperfectly) present, participants need not look back at what they have typed to get an indication of their accuracy, which allows them to focus on looking forward at what they need to type next.

M.H. Christ et al. / Accounting, Organizations and Society 50 (2016) 27e40

feedback controls on the same characters in all conditions subject to feedback controls. 3.1.7. Posteexperiment questionnaire After completing the main data-entry task, participants completed a posteexperiment questionnaire and received their compensation according to the compensation scheme for their experimental condition.

33

participants were not aware of this overall performance measure and the (somewhat arbitrary) equal weighting of normalized measures.21 Our purpose in analyzing this measure is not to suggest how participants should have maximized “overall performance,” but rather, to allow for an ex post investigation of overall performance using a reasonable proxy for performance across both task dimension.22 We summarize our experimental design and primary dependent variables in Fig. 1.

3.2. Measures 3.3. Results To test our hypotheses, we develop measures of data-entry accuracy and data-entry speed. To observe differences in data-entry accuracy across conditions, we allow participants only a single opportunity to enter the correct character (i.e., participants cannot backspace).19 To measure data-entry accuracy, we calculate the ratio of errors to total characters in the main data-entry task and subtract it from the participant's ratio of errors to total characters in the pre-test task.20 If participants have a positive value for this measure of accuracy, it suggests the participant improved their data-entry accuracy in the main data-entry task relative to the pretest task. This measure is formally computed as follows:

Accuracy ¼

Total errors made in pretest task Total charactersðpretestÞ 

Total errors made in test task Total charactersðtestÞ

To measure data-entry speed, we first time, to the thousandth of a second, how long it takes participants to type each character. We then calculate each participant's average time per character in the main data-entry task and subtract it from their time per character in the pre-test task. As with our measure of accuracy, a positive value for this metric suggests an improvement in performance on the main data-entry task relative to the pre-test task. The measure is formally stated below:

P Speed ¼

Time spent entering each character in pretest task Total charactersðpretestÞ P Time spent entering each character in test task  Total charactersðtestÞ

To measure overall performance, we first conduct a z-transformation of the accuracy (speed) measures by subtracting from the measure the average accuracy (speed) of all participants, then dividing by the accuracy (speed) standard error of all participants. This normalization of the accuracy and speed scores removes distributional differences in the accuracy and speed measures, allowing us to average the two measures to yield a single overall performance measure that equally weights both performance dimensions. We add one to this average of the z-transformed accuracy and speed measures so that the overall performance score is non-negative. Higher values for the overall performance score signify better overall performance. Importantly, though we encouraged participants to “type as quickly and as accurately as possible,” emphasizing the importance of both speed and accuracy,

19 Because this was a unique typing task (e.g., no backspacing, participants used tab to go to next line and manually click to enter first line, etc.), we omit from analyses the first screen on which participants typed in the pre-test task. This allowed the participants to get used to the unique typing environment and not create an artificially low pre-test score. 20 Subtracting the pre-test score from the main data-entry task score, in addition to randomization, helps remove any differences caused by factors other than our manipulations (e.g., differences in intrinsic typing ability).

On average, participants in Experiment 1 completed the task in 25.7 min. Participants estimated their own data-entry speed (in words per minute) before and after the experiment. Average responses were 62.6 and 54.2 words per minute, respectively. Sixtyeight percent of the participants were male. An ANOVA comparison of pre-test accuracy and speed shows that there are no significant differences between conditions (p > 0.10), suggesting randomization was successful. To make sure our manipulations of compensation and feedback controls influence behavior, we test to see if the data-entry speed and accuracy measures are significantly different from zero in settings with compensation or feedback controls on these dimensions. Because our goal is to test whether our manipulation worked (i.e., the controls influenced behavior), we examine each control only in settings where there is only one control. Recall that the data-entry speed and accuracy measures show the change in speed and accuracy from the pre-test task to the main data-entry task, not an absolute baseline level of speed and accuracy. Thus, a positive outcome that is different from zero indicates that an individual improved on that dimension from the pre-test task to the main data-entry task. As shown in Panel A of Table 1, a feedback control on accuracy results in significantly greater data-entry accuracy than in the pretest task (0.110, p < 0.01). Further, having a compensation control on accuracy results in significantly greater data-entry accuracy than in the pre-test task (0.101, p < 0.01). As shown in Panel B of Table 1, a compensation control on speed results in significantly faster dataentry speeds than in the pre-test task (0.014, p < 0.01). These results suggest that our compensation and feedback controls, in isolation, are behaving as we would expect as they are resulting in greater performance in the controlled dimension.23 To test our hypotheses, we conduct planned contrasts of the five

21 Notably, this measure of overall performance is decidedly ad hoc. We provided no instruction to participants on the relative importance of speed vs. accuracy; thus, we cannot be sure how participants viewed the importance of these dimensions. As such, our choice to equally weight both (normalized) performance metrics provides one measure of performance, but not necessarily the measure of performance that the “company” cared about, or that the participants chose to maximize. 22 In the presence of an “overall” measure of performance, one might question why any other measure would matter. Though organizations may seek to maximize their “overall” performance, measurement of that construct can be extremely difficult. Take for example a university that seeks to produce scholarly research and provide a world-class education to its students. Administrators may tell faculty applying for promotion and tenure that applicants will be judged based on their “overall performance” (including research success and teaching effectiveness). However, administrators are unlikely to use a single metric to proxy for the goal of maximizing “overall performance” when evaluating applicants, because no one metric will perfectly capture this elusive construct. 23 Accuracy (speed) did not improve from the pre-test to the main data-entry task for participants who were not subject to controls on accuracy (speed) (p > 0.10). Even so, the possible influence of learning effects would not alter the inferences we make in this study, as tests of our hypotheses are based on differential improvement between conditions, as explained subsequently.

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M.H. Christ et al. / Accounting, Organizations and Society 50 (2016) 27e40

Fig. 1. Experiment conditions and dependent variables.

Table 1 Speed and Accuracy improvements from pre-test to main data-entry task.

M.H. Christ et al. / Accounting, Organizations and Society 50 (2016) 27e40

35

Table 2 Effects of control manipulations on Accuracy performance. Panel A: Descriptive statistics related to Accuracy: Mean [Stdev] Experiment condition

Number of Participants Pre-Test Accuracy - Main-Test Accuracy ¼ Accuracy

Speed compensation

Accuracy compensation

Speed compensation/Accuracy compensation

Accuracy feedback

Speed compensation/Accuracy feedback

25

26

26

23

25

0.281 [0.196] 0.277 [0.183] 0.004 [0.126]

0.259 [0.215] 0.158 [0.144] 0.101 [0.107]

0.191 [0.159] 0.152 [0.120] 0.039 [0.095]

0.174 [0.207] 0.064 [0.048] 0.110 [0.166]

0.204 [0.220] 0.062 [0.040] 0.142 [0.198]

Panel B: Planned Comparison Tests of Hypotheses (Based on Accuracy) H1a: Speed Compensation/Accuracy Compensation > Speed Compensation: F ¼ 0.82, p ¼ 0.184 H1b: Accuracy Compensation > Speed Compensation/Accuracy Compensation: F ¼ 2.54, p ¼ 0.057 H2a: Speed Compensation/Accuracy Feedback > Speed Compensation/Accuracy Compensation: F ¼ 6.89, p ¼ 0.005 See Fig. 1 for variable definitions. Panel A contains descriptive statistics for accuracy. The “Accuracy” measure in the final row of Panel A is the percent improvement in accuracy. In other words, Accuracy of 0.142 for participants in the Speed Compensation/Accuracy Feedback condition indicates that participants typed 14.2% more controlled characters correctly in the Main Test than they did in the Pre Test. Panel B contains tests of our hypotheses using accuracy. All reported p-values are one-tailed when testing directional hypotheses.

Table 3 Effects of control manipulations on speed performance. Panel A: Descriptive statistics related to speed: Mean [Stdev] Description

Number of Participants Pre-Test Speed - Main-Test Speed ¼ Speed Approximate Improvement in Words Per Minute

Experiment condition Speed compensation

Accuracy compensation

Speed compensation/Accuracy compensation

Accuracy feedback

Speed compensation/Accuracy feedback

25 0.26 [0.048] 0.246 [0.043] 0.014 [0.026] 4.20

26 0.246 [0.088] 0.252 [0.086] 0.006 [0.018] 1.80

26 0.255 [0.065] 0.251 [0.060] 0.004 [0.017] 1.20

23 0.226 [0.067] 0.227 [0.065] 0.000 [0.006] 0.00

25 0.233 [0.077] 0.232 [0.076] 0.001 [0.011] 0.30

Panel B: Planned Comparison Tests of Hypotheses (Based on Speed) H1a: Speed and Accuracy Compensation > Accuracy Compensation: F ¼ 4.26, p ¼ 0.021 H1b: Speed Compensation > Speed Compensation/Accuracy Compensation: F ¼ 4.70, p ¼ 0.016 H2b: Speed Compensation/Accuracy Feedback > Speed Compensation/Accuracy Compensation: F ¼ 0.33, p ¼ 0.567 See Fig. 1 for variable definitions. Panel A contains descriptive statistics for speed, including a benchmark of words per minute in order to better comprehend the size of effects for differences in data-entry speed at the character level. The approximate improvement in words per minute is computed by assuming that each word is five characters and then multiplying speed * 5 * 60. Panel B contains tests of our hypotheses using speed. All reported p-values are one-tailed when testing directional hypotheses.

different conditions in our study. We present formal statistical tests in Table 2 (accuracy) and Table 3 (speed). Panel A of each table contains descriptive statistics for the relevant dependent measure. Panel B of each table contains the pertinent tests of our hypotheses for the relevant dependent variable. For the data-entry accuracy results (Table 2), the accuracy measure indicates the percent decrease in errors per controlled character in each condition. For the data-entry speed results (Table 3), we provide a calculation of approximate improvement in words per minute to help demonstrate the size of effects for differences in data-entry speed measured at the character level. Note that we can test the majority of our hypotheses using the accuracy dependent measure. Though the nature of our experiment design (incomplete 2
3 instead of a full factorial 3
3 design) does not allow us to test of all of our hypotheses using both the accuracy and speed dependent measures, where possible, we retest our hypotheses using the speed dependent measure as well. 3.3.1. Tests of hypothesis 1 In H1a, we predict that in a multidimensional task where a compensation control is implemented on a given task dimension, individuals will perform better on a separate task dimension when a compensation control is implemented on that dimension than if a compensation control is not implemented on that dimension. We

test this prediction using each dependent variable (i.e., speed and accuracy) separately. First, for data-entry accuracy, we examine whether the Speed Compensation/Accuracy Compensation condition is greater than the Speed Compensation condition. Second, for dataentry speed, we examine whether the Speed Compensation/Accuracy Compensation condition is greater than the Accuracy Compensation condition. The results of the test of H1a using the accuracy dependent measure are presented in Panel A and Panel B of Table 2. Accuracy is 0.039 in the Speed Compensation/Accuracy Compensation condition, and is 0.004 in the Speed Compensation condition. Though directionally consistent with H1a, the difference is not significant (p ¼ 0.184). The results of the test of H1a using the speed dependent measure are presented in Panel A and Panel B of Table 3. Speed is 0.004 in the Speed Compensation/Accuracy Compensation, and is 0.006 in the Accuracy Compensation. The difference is significant (p ¼ 0.021), and is directionally consistent with H1a. Thus, the results generally support H1a, suggesting that when a given task dimension is compensated, individuals will perform better on a separate task dimension when that dimension is also compensated than if it is not compensated. H1b states that a compensation control on one task dimension will increase performance on that dimension more if other dimensions are not subject to compensation controls than if other

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dimensions are subject to compensation controls. In other words, we predict that compensation controls on multiple dimensions will cause individuals to divide their attention among those compensated dimensions. To test this hypothesis, we perform two tests. First, we conduct a contrast comparison to test whether participants have higher data-entry accuracy when compensated solely on accuracy (the Accuracy Compensation condition) than when compensated based on speed and accuracy (the Speed Compensation/Accuracy Compensation condition). Second, we conduct a contrast comparison to see whether participants have higher dataentry speed when compensated based solely on speed (the Speed Compensation condition) than when they are compensated based on both speed and accuracy (the Speed Compensation/Accuracy Compensation condition). The results of the test of H1b using the accuracy dependent measure are presented in Panel A and Panel B of Table 2. Accuracy is 0.101 in the Accuracy Compensation condition, and is 0.039 in the Speed Compensation/Accuracy Compensation condition. The difference is significant (p ¼ 0.057), and is directionally consistent with H1b. The results of the test of H1b using the speed dependent measure are presented in Panel A and Panel B of Table 3. Speed is 0.014 in the Speed Compensation condition, and is 0.004 in the Speed Compensation/Accuracy Compensation condition. The difference is significant (p ¼ 0.016), and is directionally consistent with H1b. Thus, our evidence supports H1b, suggesting that a compensation control on only a single dimension will result in greater performance than if compensation controls are used on both dimensions. 3.3.2. Tests of hypothesis 2 H2a and H2b predict performance differences between a control configuration where two dimensions are subjected to compensation controls and a control configuration where one dimension has a compensation control and a second dimension has a feedback control. Specifically, H2a states that when one dimension is compensated, performance on the other dimension will be better if the other dimension is subject to a feedback control rather than a compensation control. We test this hypothesis by examining whether accuracy is higher in the Speed Compensation/Accuracy Feedback condition than in the Speed Compensation/Accuracy Compensation condition. The results of the test of H2a are presented in Panel A and Panel B of Table 2. Accuracy is 0.142 in the Speed Compensation/Accuracy Feedback condition, and is 0.039 in the Speed Compensation/Accuracy Compensation condition. The difference is significant (p ¼ 0.005), and is directionally consistent with H2a. Thus, the results support H2a, suggesting that when one dimension is compensated, performance on the other dimension will be better if the other dimension is subject to a feedback control rather than a compensation control. H2b states that when one dimension is compensated, performance on that dimension will be better if the other dimension is subject to a feedback control rather than a compensation control. To test H2b, we test whether speed is higher in the Speed Compensation/Accuracy Feedback condition than in the Speed Compensation/Accuracy Compensation condition. The results of the test of H2b are presented in Panel A and Panel B of Table 3. Speed is 0.001 in the Speed Compensation/Accuracy Feedback condition, and is 0.004 in the Speed Compensation/Accuracy Compensation condition. The difference is not significant (p ¼ 0.567). Thus, the results do not support H2b. However, as discussed previously, finding support for only one of our H2 predictions is not entirely surprising. That is, consistent with the arguments from Holmstrom and Milgrom (1991), when cognitive capacity was freed up by the use of a feedback control, participants may have allocated these additional resources to the task

dimensions with the most favorable measurement characteristics (e.g., less “noisy,” more easily measured, or more easily maximized). In our experiment, participants appear to have predominantly allocated the added capacity from feedback controls to improving their performance on the accuracy dimension (providing support for H2a) rather than the speed dimension (providing no support for H2b). Thus, results for H2a and H2b suggest that participants may have perceived accuracy to have a clearer performance benchmark (100 percent accurate) relative to speed (there is no “perfect” speed, or even a suggested speed target).24 We further explore this possibility in Experiment 2 in which we implement a feedback control on speed as opposed to accuracy. 3.3.3. Tests of hypothesis 3 H3 makes a prediction about overall performance derived from performance predictions made in H2a and H2b (see the Appendix for additional discussion). Specifically, in our setting, H3 predicts that overall performance will be greater in the Speed Compensation/ Accuracy Feedback condition than in the Speed Compensation/Accuracy Compensation condition. The results of the test of H3 are presented in Panel A and Panel B of Table 4. Overall performance is 1.174 in the Speed Compensation/Accuracy Feedback condition, and is 0.904 in the Speed Compensation/Accuracy Compensation condition. The difference is marginally significant (p ¼ 0.071), and is directionally consistent with H3. Thus, H3 is supported, suggesting that overall performance is greater when one task dimension is subject to a compensation control and another task dimension is subject to a feedback control than with both task dimensions are subject to compensation controls.25 3.3.4. Addressing an alternative explanation A possible alternative explanation for our H2 and H3 findings is that the feedback control on accuracy in our setting provides individuals stronger motivation to type accurately than does compensating individuals on accuracy. In other words, according to this argument, our result occurs because the feedback control on accuracy is “stronger” than the compensation control on accuracy. To rule out this alternative explanation, we compare the data-entry accuracy of participants in the Accuracy Feedback condition with those in the Accuracy Compensation condition. From Table 2, average accuracy is 0.110 in the Accuracy Feedback condition, and is 0.101 in the Accuracy Compensation condition. The difference is not significant (p > 0.10). Thus, when we compare feedback controls on accuracy and compensation controls on accuracy in isolation in our task, we find that both types of controls produce similar performance in participants, providing additional support for H2 and H3 findings.

24

We thank an anonymous reviewer for making this point. Average overall performance is also higher in the Speed Compensation/Accuracy Feedback condition than in the Speed Compensation setting and Accuracy Feedback setting; however, the difference is not significant (both p > 0.10). These non-significant differences are consistent with the goal conflict theory discussed in Section 2, as we expect little goal conflict in either the Speed Compensation setting or in the Accuracy Feedback setting. These non-significant differences are also likely highly dependent on how “overall” performance is defined. Our ad hoc calculation of “overall” performance is a simple average of the two (normalized) performance measures. Thus, in our setting, overall performance can potentially be improved by maximizing a single task dimension and ignoring the other task dimension. This gives rise to the potential in our setting for higher overall performance with a single controlled dimension than with multiple controlled dimensions. However, if a firm requires a certain minimal level of performance on multiple dimensions (as is often the case), maximizing performance on one dimension (e.g., quantity) and ignoring another dimension (e.g., quality) may not maximize the desired “overall” performance (e.g., “long term sales”). 25

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Table 4 Effects of control manipulations on overall performance. Panel A: Descriptive statistics related to overall performance: Mean [Stdev] Experiment condition

Number of Participants Accuracy Speed Overall Performance

Speed compensation

Accuracy compensation

Speed compensation/Accuracy compensation

Accuracy feedback

Speed compensation/Accuracy feedback

25

26

26

23

25

0.004 [0.126] 0.014 [0.026] 1.067 [0.743]

0.101 [0.107] 0.006 [0.018] 0.845 [0.615]

0.039 [0.095] 0.004 [0.017] 0.904 [0.671]

0.110 [0.166] 0.000 [0.006] 1.023 [0.638]

0.142 [0.198] 0.001 [0.011] 1.174 [0.657]

Panel B: Planned Comparison Tests of Hypotheses (Based on Overall Performance) H3: Speed Compensation/Accuracy Feedback > Speed Compensation/Accuracy Compensation: F ¼ 2.18, p ¼ 0.071. See Fig. 1 for variable definitions. Panel A contains descriptive statistics for accuracy. Panel B contains a test of our H3 using overall performance. All reported p-values are onetailed when testing directional hypotheses.

4. Experiment 2 4.1. Experimental task and manipulations Our theory and the results of Experiment 1 show that individuals perform better on multi-dimensional tasks when there is a compensation control imposed on one task dimension and a feedback control on the other task dimension (relative to a compensation control on all dimensions). However, as described in our hypothesis development and in the H2 results, it is likely that the nature of the task dimensions is an important factor in determining how the performance improvements manifest. The results from H2 and H3 show that when feedback controls are implemented on one task dimension and compensation controls on another dimension, employees can improve performance on individual dimensions as well as their overall task performance, relative to when firms compensate employees on both task dimensions. However, participants' speed did not improve when feedback controls were implemented on one dimension with compensation controls on the other dimension, relative to when compensation controls were implemented on both dimensions (i.e., we observed no differences in performance for the speed dimension). In our study, accuracy and speed are different in an important way that may shed light on why employees improved in the accuracy dimension under feedback controls, but not in the speed dimension. Typing accuracy for a given character is a dichotomous variable, whereas typing speed for a given character is a continuous variable. Dichotomous variables generally create clear normative benchmarks (i.e., you either typed the character accurately or not), whereas continuous variables do not (e.g., it's unclear whether 0.1 s per character is “fast enough.”). We conducted a second experiment to further explore our theory when the task dimension that is subjected to feedback controls does not have a clear normative benchmark. Eighty-seven undergraduate students participated in this experiment. The task, procedures, and measures for this additional experiment are identical to those in Experiment 1, except that this experiment employs a 1
2 between-subjects design with the following conditions: 1. Accuracy Compensation/Speed Compensation condition: A compensation control was implemented on speed and on accuracy. Participants were told, “You will be compensated on these screens based on how quickly and accurately you type the words on the screen. Therefore, the faster you type, and the more accurate your output, the more money you will earn” (emphasis as in original).

2. Accuracy Compensation/Speed Feedback condition: A compensation control was implemented on accuracy and a feedback control was implemented on speed. Participants were told, “You will be compensated on these screens based on how accurately you type the words on the screen. Therefore, the more accurate your output, the more money you will earn” (emphasis as in original). Participants were also told, “On the next screens there are controls on some (but not all) of the letters such that if you type the letter slower than the average speed you typed in the pre-test task there is a control on that letter so that the letter will turn red. For example, if you type slower than the average on the letter 'd' the letter 'd' will turn red.” Thus, this additional experiment allows us to test the theory underlying H2 and H3 more completely by imposing feedback controls on a different task dimension. We report untabulated results for this experiment below. 4.2. Results 4.2.1. Tests of hypothesis 2 In Experiment 2, H2a predicts that speed will be higher in the in the Accuracy Compensation/Speed Feedback condition than in the Accuracy Compensation/Speed Compensation condition. Speed is 0.003 in the Accuracy Compensation/Speed Feedback condition, and is 0.007 in the Accuracy Compensation/Speed Compensation condition. The difference in speed is not significant (p ¼ 0.362). This result is consistent with results from Experiment 1, but inconsistent with our prediction. H2b predicts that accuracy will be higher in the Accuracy Compensation/Speed Feedback condition than in the Accuracy Compensation/Speed Compensation condition. Accuracy is 0.040 in the Accuracy Compensation/Speed Feedback condition, and is 0.005 in the Accuracy Compensation/Speed Compensation condition. This pattern of results is consistent with our prediction for H2b (and with results from Experiment 1), and the difference is marginally significant (p ¼ 0.07). 4.2.2. Tests of hypothesis 3 H3 predicts that overall performance will be greater in the Accuracy Compensation/Speed Feedback condition than in the Accuracy Compensation/Speed Compensation condition. Overall performance is 1.03 in the Accuracy Compensation/Speed Feedback condition, and is 0.97 in the Accuracy Compensation/Speed Compensation condition. This pattern of results is consistent with our prediction for H3, but the difference is not significant (p ¼ 0.346). Together, results from our second experiment suggest that, relative to implementing compensation controls on speed and

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accuracy, implementing a compensation control on accuracy and a feedback control on speed allows participants to improve accuracy without hurting speed, consistent with results from Experiment 1. 4.2.3. Discussion of results from experiment 1 and experiment 2 When we compare results from Experiment 2 with those from Experiment 1, important insights emerge about how the combination of feedback and compensation controls improves performance. In particular, in both experiments, we observe statistically significant performance improvement in the accuracy dimension when one dimension has a compensation control and the other dimension has a feedback control. However, speed performance is the same regardless of the control combination imposed. This evidence helps confirm our earlier conjecture that participants in our study likely focused extra cognitive capacity (provided by the use of a feedback control) on the accuracy dimension because there was a clearer performance benchmark on that dimension (measureable “success” was more achievable). In other words, in both experiments, when feedback controls increased cognitive capacity for goal realization, participants allocated that added capacity to improving the accuracy dimension, rather than the speed dimension. Second, although results for Experiment 2 are generally consistent with predictions made in H2 and H3, they are statistically weaker than those in Experiment 1. In particular, we find only marginally significant support for H3 in Experiment 2, and no statistical support for H3 in Experiment 2 (though directional results are consistent with H3). This pattern of results suggests that the feedback control over accuracy in Experiment 1 was more effective than the feedback control over speed in Experiment 2 at increasing cognitive capacity for goal realization. Although we attempted to create a feedback control for speed in Experiment 2 that closely mirrored the feedback control for accuracy in Experiment 1, natural differences between the speed and accuracy performance dimensions produced natural differences between the feedback controls over speed and accuracy in our experiments.26 We believe a potential direction for future research would be to examine attributes of performance dimensions that make feedback controls on those dimensions more or less effective. 5. Conclusions This study provides important insights regarding the complementary influence of compensation controls and feedback controls. Consistent with our predictions, we find that when firms use compensation controls on multiple task dimensions, employees divide their attention and effort between task dimensions, resulting in lower performance. However, we find evidence suggesting that when firms use a compensation control on only one dimension of a task, and use a feedback control on other task dimensions, employees are able to improve their performance on the task dimension with the feedback and improve their overall performance. We further find evidence that, in multidimensional task settings where firms plan to use a combination of feedback and compensation controls, firms may reap the greatest benefit from implementation of feedback on the task dimensions with clear performance benchmarks. Importantly, our findings demonstrate that contracts that compensate all dimensions of performance should not be

26 For example, the feedback control over accuracy in Experiment 1 has a clearer performance benchmark (the letter was typed accurately or not) than does the feedback control over speed in Experiment 2 (comparison to the mean speed in pre-test task).

considered superior to contracts with other types of controls. Indeed, our evidence shows that, in some instances, firms can better achieve their multidimensional goals by using compensation controls on one dimension of a task and implementing feedback controls on other dimensions than they can using compensation controls on all task dimensions. This study is the first to provide empirical evidence to that end, and is an important first step in a continuing investigation of the complementary nature of compensation controls and feedback controls. The conclusions of our study are subject to limitations that provide opportunities for future research. First, we examine a setting in which firms implement either a compensation control or a feedback control on a given task dimension, but do not implement both types of control on a given task dimension. We designed our experiment in this way to disentangle the effect of control types that are often confounded in natural settings, allowing us to test the joint and separate effects of these control types in a multidimensional task. However, in practice, firms could implement compensation controls and feedback controls on a single task dimension. Although goal conflict theory, and the results from this study, suggests that using compensation controls on multiple dimensions would result in lower performance, it is unclear whether feedback controls could mitigate some of that goal conflict if implemented simultaneously on the same dimensions. Future research should examine how compensation controls that provide timely feedback interact with other types of controls to impact employee behavior. Second, we limit the multidimensional task in our study to two dimensions. However, the theory we draw from would predict that results would be stronger with additional task dimensions. We leave empirical tests of this prediction to future research. Third, we examine the constructs in this study using a very simple, data-entry task. In practice, firms likely consider other features of the work setting when deciding how to design effective control systems. While these other features of the work setting could impact effect sizes we observe, they are unlikely to alter the directional effects (Libby, Bloomfield, & Nelson, 2002). That said, the multidimensional task we employ involves dimensions that are not entirely complementary: entering data more rapidly may lead to more errors, and entering data more accurately may reduce speed. Because our theory relates to goal conflict, the degree of complementarity of task dimensions in a given setting will likely influence the joint effectiveness of feedback controls and compensation controls. We leave to future research an investigation of our findings in a setting where task dimensions are more complementary in nature. Fourth, our tests were limited to analyses of output performance measures rather than a focus on input process measures. With the increasing availability of biometric measurement (such as eyetracking devices), valuable process measures are more and more accessible. We leave to future research a closer examination of the processes underlying the effects examined in this study. Appendix Following is a full 3
3 factorial version of our theoretical design, with two generic task dimensions (Dimension 1 and Dimension 2) and three control levels on each dimension (no control, compensation control, and feedback control). Each condition in the design is labeled with a letter (C through K). Cells represented in Experiment 1 are shaded and cells represented in Experiment 2 are tagged with ** (where Task Dimension 1 ¼ Accuracy and Task Dimension 2 ¼ Speed). Cells that can be used to test our hypotheses are labeled with a capital letter. Italicized cells are only needed to provide a secondary test of a hypothesis (assuming that we first test the hypothesis based on Dimension 1).

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39

Task dimension 1

Task Dimension 2

No control

Compensation control

Feedback control

No Control

c

D

E

Feedback Control

F

G**

h

Compensation Control

I

J**

K

Note that the design we selected for Experiments 1 and 2 give us sufficient experimental conditions to fully test our theory, as well as to provide multiple robustness checks. By construction, our design choice also allows for a few additional robustness checks. Based on our predictions in H1eH2, we expect the following inequalities for each dimension (shaded inequalities represent tests we can run given the design of Experiment 1, inequalities tagged with ** represent tests that we can run given the design of Experiment 2).

Task dimension 1

Task dimension 2

H1a

D>J

I>J

H1b

E>K

F>G

H2a

K>J

G > J**

H2b

G > J**

K>J

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