Effects of role stress on task performance

Role stress are generally conceptualized using two interrelated constructs:

role conflict and role ambiguity (Rizzo et al., 1970; Nygaard & Dahlstrom, 2002). Role conflict occurs when a transplant believes that the expectations and demands of two or more members of his or her role set are incompatible (e.g., role expectations from a vendor manager and a client manager).

Strength of ties between a transplant and client is not unlikely because of previous employment. Role ambiguity relates to the perceived lack of infor-mation a transplant needs to perform his or her role adequately and his or her uncertainty about expectations of different role set members. The business relationship between client and vendor organization is negotiated over some time during the outsourcing project, but transplants experience an overnight change in their role as they are transferred from client to vendor organiza-tion. On large contracts it is not unlikely that transition activities may last from 18 months to more than 2 years (Lacity & Willcocks, 2000a). In this survey data was collected in the transition phase of a selected outsourcing arrangement. Two groups, A and B, of respondents had been transferred from client to vendor 17 months and 7 months prior to the survey.

Conventionally, the linear influence of role stressors on task performance has been examined, suggesting that role stress has significant dysfunctional (negative) effects on task performance. This confirmatory research was look-ing for supportlook-ing evidence from the field of IT outsourclook-ing. The influences of role stress on task performance were hypothesized to be negative for both role conflict and role ambiguity. As expected, the path from role ambiguity to task performance was negative, meaning higher levels of role ambiguity decreased task performance. Contrary to what was hypothesized, the path from role conflict to task performance was positive; meaning, a higher level of role conflict was associated with higher levels of task performance.

Previous studies in other fields have examined relationships between role stress and effectiveness, yet the results do not unequivocally support the hypotheses of a negative relationship (Nygaard & Dahlstrom, 2002). Re-searchers have proposed alternative perspectives to the linear effect of role stress on job performance; the relationship appears to be (1) a variant of the Yerkes-Dodson Law, or (2) a three-phase model known as General Adapta-tion Syndrome, and (3) influenced by a moderator effect. Below, these three alternative models are discussed.

Yerkes-Dodson Law has been used in describing an inverted U-shaped curve relating role stress to task performance (Michaels et al., 1987; Singh, 1998).

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Thus, one might speculate that increased arousal improves performance only up to a certain point, after which further increase in arousal is linked with decrements in performance levels. The positive relationship between role conflict and task performance, found in the survey, may be explained by Yerkes-Dodson Law, because heightened role conflict improved task per-formance. This explanation will apply up to a certain point, at which contin-ued increase in role conflict will reduce the task performance level. Apply-ing Yerkes-Dodson Law as a theoretical reason to explain the relationship found in this research, the conflict variable can be modified via the square term. For example, in a simple regression model, a curvilinear model with one turning point can be modeled with the equation Υ = β0 + β1X1 + β2X12

where β0 is the intercept, X1 is the linear effect of role conflict, and X12 is the curvilinear effect of role conflict. For interpretation purposes, the positive quadratic term indicates a U-shaped upward curve, while a negative coeffi-cient indicates an inverted shaped downward relationship. An inverted U-shaped relationship is supported if (1) the coefficient for a linear term is positive (e.g., β1), (2) the coefficient for a quadratic term is negative (e.g., β2), and (3) both coefficients are significant (Singh, 1998). The signs are reversed for a U-shaped relationship.

Selye (1959) offers a contrasting perspective in his presentation of the Gen-eral Adaptation Syndrome (G-A-S) as a three-phase model of reactions to stressors. The alarm phase is characterized by increasingly lower levels of performance. For example in an IT outsourcing arrangement, where trans-plants immediately after transfer to a vendor organization increase their per-ception of environmental stressors, which lower their task performance. Dur-ing the reactance phase, performance factors increase and resistance to stress increases. Selye (1974) uses the term “eustress” to describe positive conse-quences of stress and “distress” to describe negative conseconse-quences of stress.

Eustress is accompanied by coping behaviors that enable people to overcome stress and accomplish tasks that are considered worthwhile. Beyond some threshold, however, the exhaustion phase is observed, in which reactions are similar to those in the initial phase. The periodicity outlined in G-A-S aug-ments the hypotheses examined in prior research. The linear effects offered in many studies are embedded in the initial and final phases of the function (negative relationship in the alarm phase and the exhaustion phase). The function also incorporates logic from Yerke’s and Dodson’s (1908) research on habit formation to account for nonlinear influences of stress. This rela-tionship is incorporated into G-A-S function by a positive relarela-tionship in the reactance phase. Applying the General Adaptation Syndrome on the rela-tionship, the conflict variable can be modified via the sine function. A curvi-linear model with two turning points can be modeled with the equation Υ = β₀ + β₁X₁ + β₂sine(X₁)where β₀ is the intercept, X₁ is the linear effect of role

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conflict, and sine(X₁) is the curvilinear effect of role conflict. An inverted sine-shaped relationship is supported if (1) the coefficient for a linear term is positive (e.g., β₁), (2) the coefficient for a sine term is negative (e.g., β₂), and (3) both coefficients are significant.

The non-linear relationships discussed above require the creation of an addi-tional variable to represent the changing slope of the relationship over the range of the independent variable. This focuses on the relationship between one single independent variable (role conflict) and the dependent variable task performance. Singh (1998) has investigated the interaction effects of other independent variables and role stressors on job performance. He found that task variety buffers the effect of role conflict on job performance. In his research, salespeople faced fewer dysfunctional consequences of role con-flict when task variety was high. This is termed moderator effect, which occurs when a third independent variable causes the relationship between a dependent and an independent variable to change depending on the value of the moderator variable. The moderator effect is represented in multiple re-gressions by a term quite similar to the polynomials described earlier to rep-resent nonlinear effects. The moderator term is a compound variable formed by multiplying X1 by the moderator X2, which is entered into the regression equation. The moderated relationship was represented as Υ = β0 + β1X1 + β2X2 + β3X1X2where β0 is the intercept, X1 is the linear effect of role con-flict, X2 is the linear effect of client managerial persistent expectations, and X1X2 is the moderator effect of client managerial persistent expectations on role conflict.

To determine whether the alternative models were significant, the analyst first estimated the original (unmoderated) equation and then estimated the moderated relationships. If the change in R² was statistically significant, then a significant effect of the alternative model was present. Appendix G shows regression with task performance, role conflict, and the additional non-linear and interaction variables.

First, The General Adaptation Syndrome was represented by Υ = β₀ + β₁X₁ + β₂sine(X₁) in Model 2. Both coefficients of independent variables were sig-nificant, but their sign was not as expected to represent the alarm phase, reactance phase, and exhaustion phase. The proposed Model 2 suggested increased performance in the alarm phase, decreased in the reactance phase, and again increased performance in the exhaustion phase. And thus we found no support for the General Adaptation Syndrome.

Second, an inverted U-shaped relationship was represented as Υ = β₀ + β₁X₁ + β₂X₁² in Model 3. Both coefficients of dependent variables were signifi-cant, but their sign was the opposite of what could be expected applying

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Yerkes-Dodson Law. The proposed Model 3 suggested an upward U-shaped curve, and thus we found no support for Model 3.

Third, the interaction effect was represented as Υ = β₀ + β₁X₁ + β₂X₂ + β₃X₁X₂ in Model 3. The specific interactional influences appear significant, including the interactive positive influence of role conflict and client mana-gerial persistent expectation (β₃ = 0.129, p < 0.01). In addition, role conflict (β₁ = -0.526, p < 0.01) had the proposed dysfunctional effect. In other words, client managerial persistent expectation appeared to buffer the effects of role conflict on task performance.

Partial plots for the intercorrelational effects of client managerial persistent expectations (PME) and role

conflict on the task performance of transplants

0 1 2 3 4 5 6 7

1 2 3 4 5 6 7

Role conflict

Task performance

Low PME Medium PME High PME

Figure 7.1. Interaction effect of role conflict and client managerial persistent expec-tations on task performance.

As indicated in Figure 7.1, role conflict has a negative effect on task per-formance when client managerial expectations are at a “low” level. Role conflict has a positive relationship with task performance when client mana-gerial persistent expectations are at a “high” level. As such, transplants face fewer dysfunctional consequences of role conflict when client managerial persistent explanation is high. Prior research has not reported such effects.

An explanation of these results was sought, and thus, follow-up interviews with three randomly selected transplants were conducted. Findings from these interviews are reported later in section 7.1.4. Other buffering effects

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were sought both in survey data and case-studies, but no such effects were found.