Recognition Teamwork Quotes

Few teams sometimes fails miserably because team members wish to work in the team but they want to be recognized individualy.

When you’re in the middle and stuck, you need to know when to back out and call for help. If that person is someone you live with, set up your signals as Molly and her husband did. Use expressions or words that clearly signify “I need your help now!” It is imperative that parents of spirited children work together. It is not a sign of failure to let others assist you. It is a recognition and acceptance of your own intensity and limits. Blaming or ridiculing only fuels the intensity levels. Teamwork is essential. You have to talk about how you react when your child is upset. You have to decide how you can help and support each other. By working together, you take the sting out of your child’s strong responses. You create a lifeline that keeps you from falling into the abyss of the red zone. If it seems impossible for you and your partner to work together, seek counseling, and make weekly dates a priority so that you can work together. Researchers at the Gottman Institute have found that children of unhappily married parents are chronically aroused physiologically and it takes them much longer to recover from emotional arousal. Your children need you to work together so that they can stay in the green zone, where they are calm and open to your guidance. If you are a single parent, you might think that you can’t ask someone else for help. Single parents often say, “What if I call and interrupt their meal or family time?” Or, “I don’t want to bother anyone.” But good friends don’t mind being bothered. They appreciate the opportunity to help and the joy of giving. Look for someone you know who likes your child and won’t be critical of him or you. You have to be able to trust that they’ll support you, and then feel free to call. As the parent of a spirited child, you have to know and use your resources well. Step Away from It Of course there are times when your kids are plummeting into the red zone and you are all alone, with no one to help. If you realize you’re going over the edge with them, give yourself permission to step out of the fire. It’s much better to take a breather than to have two bulls charging head to head into each other.

…Our overriding objective is excellence, or more precisely, constant improvement - A superb, constantly improving company in all respects. Conflict in the pursuit of excellence is a terrific thing. There should be no hierarchy based on age or seniority: Power should lie in the reasoning, not the position of the individual. The best ideas win, no matter who they come from. Criticism is an essential ingredient in the improvement process, yet, if handled incorrectly, can be destructive. It should be handled objectively. There should be no hierarchy in the giving or receiving of criticism. Teamwork and spirit are essential, including intolerance of substandard performance. This is referring to two things: First, one’s recognition of the responsibilities one has to help the team achieve it’s common goal, and second, the willingness to help others work within a group toward these common goals. Our fates are intertwined. One should know that others can be relied on to help. As a corollary, substandard performance cannot be tolerated anywhere, because it would hurt everyone. …Long-term relationships are both intrinsically gratifying and efficient, and should be intentionally built.

Good teams require good leaders. Teamwork does not happen in the absence of leadership. We cannot combine a bunch of people and expect them to work together without a clear recognition of elected or appointed leadership. Multiple IT organizations cannot operate as a team unless they report to a single team leader.

Thus, multiple regression requires two important tasks: (1) specification of independent variables and (2) testing of the error term. An important difference between simple regression and multiple regression is the interpretation of the regression coefficients in multiple regression (b1, b2, b3, …) in the preceding multiple regression model. Although multiple regression produces the same basic statistics discussed in Chapter 14 (see Table 14.1), each of the regression coefficients is interpreted as its effect on the dependent variable, controlled for the effects of all of the other independent variables included in the regression. This phrase is used frequently when explaining multiple regression results. In our example, the regression coefficient b1 shows the effect of x1 on y, controlled for all other variables included in the model. Regression coefficient b2 shows the effect of x2 on y, also controlled for all other variables in the model, including x1. Multiple regression is indeed an important and relatively simple way of taking control variables into account (and much easier than the approach shown in Appendix 10.1). Key Point The regression coefficient is the effect on the dependent variable, controlled for all other independent variables in the model. Note also that the model given here is very different from estimating separate simple regression models for each of the independent variables. The regression coefficients in simple regression do not control for other independent variables, because they are not in the model. The word independent also means that each independent variable should be relatively unaffected by other independent variables in the model. To ensure that independent variables are indeed independent, it is useful to think of the distinctively different types (or categories) of factors that affect a dependent variable. This was the approach taken in the preceding example. There is also a statistical reason for ensuring that independent variables are as independent as possible. When two independent variables are highly correlated with each other (r2 > .60), it sometimes becomes statistically impossible to distinguish the effect of each independent variable on the dependent variable, controlled for the other. The variables are statistically too similar to discern disparate effects. This problem is called multicollinearity and is discussed later in this chapter. This problem is avoided by choosing independent variables that are not highly correlated with each other. A WORKING EXAMPLE Previously (see Chapter 14), the management analyst with the Department of Defense found a statistically significant relationship between teamwork and perceived facility productivity (p <.01). The analyst now wishes to examine whether the impact of teamwork on productivity is robust when controlled for other factors that also affect productivity. This interest is heightened by the low R-square (R2 = 0.074) in Table 14.1, suggesting a weak relationship between teamwork and perceived productivity. A multiple regression model is specified to include the effects of other factors that affect perceived productivity. Thinking about other categories of variables that could affect productivity, the analyst hypothesizes the following: (1) the extent to which employees have adequate technical knowledge to do their jobs, (2) perceptions of having adequate authority to do one’s job well (for example, decision-making flexibility), (3) perceptions that rewards and recognition are distributed fairly (always important for motivation), and (4) the number of sick days. Various items from the employee survey are used to measure these concepts (as discussed in the workbook documentation for the Productivity dataset). After including these factors as additional independent variables, the result shown in Table 15.1 is

regression results. Standardized Coefficients The question arises as to which independent variable has the greatest impact on explaining the dependent variable. The slope of the coefficients (b) does not answer this question because each slope is measured in different units (recall from Chapter 14 that b = ∆y/∆x). Comparing different slope coefficients is tantamount to comparing apples and oranges. However, based on the regression coefficient (or slope), it is possible to calculate the standardized coefficient, β (beta). Beta is defined as the change produced in the dependent variable by a unit of change in the independent variable when both variables are measured in terms of standard deviation units. Beta is unit-less and thus allows for comparison of the impact of different independent variables on explaining the dependent variable. Analysts compare the relative values of beta coefficients; beta has no inherent meaning. It is appropriate to compare betas across independent variables in the same regression, not across different regressions. Based on Table 15.1, we conclude that the impact of having adequate authority on explaining productivity is [(0.288 – 0.202)/0.202 =] 42.6 percent greater than teamwork, and about equal to that of knowledge. The impact of having adequate authority is two-and-a-half times greater than that of perceptions of fair rewards and recognition.4 F-Test Table 15.1 also features an analysis of variance (ANOVA) table. The global F-test examines the overall effect of all independent variables jointly on the dependent variable. The null hypothesis is that the overall effect of all independent variables jointly on the dependent variables is statistically insignificant. The alternate hypothesis is that this overall effect is statistically significant. The null hypothesis implies that none of the regression coefficients is statistically significant; the alternate hypothesis implies that at least one of the regression coefficients is statistically significant. The

Teamwork is the key to winning team sports. The best TEAM wins, not the best players. Peer pressure is the best enforcer of the rules. The worry of ‘me’ destroys a team. Success leads to wanting credit and recognition. Worship of stats is a sign of the ‘me’ culture.” Nick Saban, University of Alabama