Locke and Latham’s (2002) theory of goal setting states that specific, difficult performance goals lead to higher performance than vague or do-your-best goals (Latham, Mitchell, & Dossett, 1978). At least one thousand studies support this assertion (Mitchell & Daniels, 2003). Most studies focus on performance goals. The present study investigates intra-individual conflict between learning goals and performance goals. Learning goals focus attention on strategy generation rather than performance and have been shown to be effective for complex tasks (Winters and Latham, 1996), Noel and Latham, 2006).
Though specific, difficult performance goals have been found to work in a wide variety of settings, there are boundary conditions. Kanfer and Ackerman (1989) found support for Anderson’s (1974, 1983) assertion that when a person lacks the necessary knowledge or skill to perform the task, a goal focused solely on outcomes diverts limited cognitive resources away from learning how to do the task. In the end, performance suffers.
In the early stages of goal theory development, goal setting studies focused almost exclusively on simple tasks--those that required little if any consideration of strategies (REF). Many business tasks require creative and strategic thinking. Effort alone does not always lead to higher performance. This is especially true in entrepreneurial settings, where ambiguity and uncertainty dominate. New venture creators must often accomplish tasks that are new to them personally or for which there exists no formulaic path to accomplishment.
Winters and Latham (1996) found that on a task where people lack the requisite knowledge or skill to perform effectively, a do-your-best goal leads to performance that is superior to those who have a specific difficult performance goal. However, they also tried assigning a different type of goal--a learning goal. Learning goals focus attention on generating strategies for performing better on a task, thus de-emphasizing performance outcomes. People who were given a specific, difficult learning goal had the highest performance of all. Noel and Latham (2006) also found that setting a specific, difficult learning goal led to higher performance than setting a specific, difficult performance goal on a computerized entrepreneurial simulation.
Intra-individual goal conflict has been shown to have a deleterious effect on performance outcomes (Locke, Smith, Erez, Chah, & Schaffer, 1994). Goal conflict arises when two or more goals are incompatible to some degree. For example, having a quantity and quality goal may involve tradeoffs (Fitts, 1966). Working faster often leads to more mistakes. Resource allocation theory (Kanfer and Ackerman, 1989) explains this as a problem of limited cognitive resources. One cannot simultaneously focus fully on doing a task and thinking about how to do it.
Deployment of limited cognitive resources in the short run is a zero-sum game. They are allocated to one aspect of a task at the expense of others. The tradeoffs are not as straightforward in the long-term. Paradoxically, when limited cognitive resources are directed toward strategy generation, such as listing ten strategies for improving car sales (a learning goal) as opposed to selling 15 cars (a performance goal), the short-term diversion of attention away from performance leads to long-term performance improvements (Seijts & Latham, 2005). If a person already possesses the ability to perform a task, such cognitive effort is neither necessary nor desirable. Attention, effort, and persistence are all that is required (Locke, Shaw, Saari, & Latham, 1981).
Kruglanski et al. (2002) argued that goal conflict is due to structural issues. Some linked goals complement each other while others conflict. Facilitative links, such as a specific, difficult learning goal and a do-your-best performance goal, serve to focus attention appropriately toward strategy generation when a task is complex. When a task is simple for the person performing it, a specific, difficult performance goal works better (Seijts and Latham?). Inhibitory links exist primarily between two or more focal goals, as in the case of a specific, difficult learning goal combined with a specific, difficult performance goal. Since one cannot simultaneously strategize and perform at high levels, this type of goal linkage is counterproductive.
Research supports Kruglanski's framework insofar as facilitative links are concerned (Winters and Latham, xxxx; Noel and Latham, 2006). However, to this author's knowledge, no studies have compared specific, difficult learning goals coupled with specific, difficult performance goals against other goal linkages. The present study examines this condition against both a specific, difficult learning coupled with a do-your-best performance goal and a no-goal condition. Kruglanski's framework suggests that two specific, difficult goals will work against one another, resulting in lower performance. H1: Subjects with a both a specific, difficult learning goal and a specific, difficult performance goal will have lower performance than subjects with a learning goal alone or no goal at all.
In addition to the types of structural links described by Kruglanski (REF), the psychological dimension of goal conflict is central to understanding the differential impact of learning and outcome goals. Resource allocation theory tells us that in general learning and performing are incompatible, but not how this varies at the individual level. The present study also looks at the degree to which the perception of conflict influences performance. The stronger the perception that a learning goal and a performance goal are in conflict, the lower performance will be. H2: Subjects with both a specific, difficult learning goal and a specific, difficult performance goal will experience a higher degree of goal conflict than subjects with either a specific, difficult learning goal only or no goal at all.
The perception of goal conflict may be influenced both by objective goal structure and by individual perceptions that are independent of goal structure. In other words, it is likely to vary across individuals working under the same goal structure. Psychological goal conflict and goal structure should explain more variance than goal structure alone. The more one sees strategy generation as a waste of time or as an unnecessary distraction from performance, the more one is likely to experience this particular kind of goal conflict. The more one sees strategy generation as complementary to performance, the less one is likely to experience psychological goal conflict. H3: Perceived goal conflict explains more variance in performance than goal structure alone.
Subjects were recruited from introductory undergraduate management classes in the business school of a large Midwestern university. Extra credit equivalent to 2% of the course grade was offered to students who spent two hours participating in an experiment located in a campus computer lab. Subjects consisted of 155 females and 198 males.
The BizCafe (REF) business simulation created by Interpretive was used for the task. Subjects signed up for time slots spread throught a two-week period. An experimental assistant supervised groups of approximately 20 people simultaneously in each time slot. Subjects were taught how to run the simulation and were then given an experiment package containing all materials necessary for the experiment. Surveys were conducted a total of three times during the experiment at the end of each simulated "month."
BizCafe simulates a small coffee shop funded with an interest-only loan made available by a former successful entrepreneur. Participants read a short case describing features of the shop such as its size (1000 sq.ft.) and location (near a college campus). Participants were allowed to make two practice decisions in order to become familiar with the mechanics of simulation play.
The BizCafe simulation contains 13 simulated weeks. Each "week," participants make decisions on advertising, staffing, and inventory purchases. Performance is measured as cumulative net income. In general, decisions that make the most efficient use of staff (enough but not too many servers) and coffee purchases (correctly anticipating the amount that will be sold in the upcoming week) are the most effective. After a decision set is finished, participants receive a financial report with details of their performance.
As recommended by Locke and Latham (REF), specific, difficult goals were set at a level attainable by 10% of participants. Through a pilot study, this was determined to be a net income of -$2500 for January, -$1500 for February, and -$1000 for March respectively for the performance goal. The proper learning goal difficulty level was determined to be ten strategies in January, seven in February, and four in March. Experimental instructions were administered at the beginning of each simulated month, as follows. Performance goal coupled with a learning goal: “You have two goals for (January, February, March). The first is to attain a net income of at least (-$2500, -$1500, -$1000). The second is to generate (ten, seven, four) strategies for increasing your net income in (January, February, March). You will be provided space on the survey site to write them down." Learning goal only: As you make your weekly decisions, think of at least (ten, seven, four) strategies for increasing your net income in (January, February, March). You will be provided space on the survey site to write them down." No goal: "You are about to start (January's, February's, March's) decisions.
0 = No Goal
1 = Learning Goal Only
2 = Learning Goal and Performance Goal
Data was collected in two ways. First, reports generated by BizCafe were downloaded to spreadsheets. They included such information as performance and customer satisfaction. Second, an online survey tool was used to gather information from subjects a three points during the experiment.
One outlier was removed from the data. The data point, net income, showed a negative NI nearly double that of the next lowest score. It was assumed to be a data entry error.
In the present case, the multiple trial design makes conventional regression analysis problematic. Data analysis was instead performed with hierarchical linear modeling. HLM allows analysis at the individual (level-1) and the group (level-2) levels simultaneously. In the present case, HLM allows an examination of the relationship between the DV and fixed variables while taking into account the fact that performance for each individual was measured three times.
Hierarchical linear (also called “multilevel”) modeling was conducted using R, an open-source statistical package (R Development Core Team, 2011). HLM in R is conducted by comparing hypothetical models to the null model, which allows both the slope and the intercept of random variables to vary by group.
A step-by-step analysis was then conducted per Bliese (REF). Other models were compared to the null model to see if they fit the data better.
Performance by time.f and condition.f
The present findings suggest that perceived goal conflict may be only weakly tied to goal structure. Goal conflict may have stronger ties to other sources, possibly one or more trait variables. The goal conflict questions in this study were targeted toward respondents' perceptions of the task at hand, so it is impossible to tell whether they inadvertently tapped more enduring individual characteristics. Future research will have to determine whether this is the case.
Some candidates come to mind. First, goal orientation (REF) may explain why some people
Goal orientation may interact with goal structure.
Another possiblity may be proactive personality.
Learning what affects goal conflict is interesting; learning how to influence it is useful.
Perhaps interventions can work in the short term (per task).
One wonders if this could be altered over the long term.