task complexity and information exchange: the impact ...

TASK COMPLEXITY AND INFORMATION EXCHANGE: THE IMPACT OF NURSES' NETWORKING ACTIVITIES ON ORGANIZATIONAL INFLUENCE DEBORAH WRIGHT BROWN Long Island University — C. W. Post Campus

SOCIOLOGICAL FOCUS Vol. 29 No. 2 May 1996

ALISON M. KONRAD Temple University

We used an information-processing perspective and the reported work activities of practicing nurses to examine the relationships among task complexity, human capital endowments and patterns of networking. In addition, we also examined the impact of all of these factors on nurses'perceptions of influence over hospital policies and procedures. Measures of task complexity, including task instability and decision-making autonomy, were found to be positively related to intraunit and interunit networking, respectively. Contrary to prediction, education and experience were found to be negatively associated with all forms of organizational networking. Our analyses also indicated that decision-making autonomy and intraunit networking were positively related to nurses' perceptions of influence in the organization.

"Once, when I was working on the Labor and Delivery Unit, we were unexpectedly faced with the delivery of Siamese [conjoined] twins joined at the esophagus. Within moments, the flurry of activ­ ity escalated. Physicians, nurses and clinical specialists from all over the hospital were called to the unit in an effort to save the lives of these two infants." A 24-year old nurse's first day on the Labor & Delivery Unit

VJTood communication networks are paramount to the conduct of complex and lifesaving work. The study of networking activities in organizations attracted researchers' attention well over a decade ago (Aldrich 1976; Hall, Clark, Giordano, Johnson and Van Roekel 1977; Freeman 1979; Lincoln and Miller 1979). Interest in networking resurfaced in the mid 1980s (Albrecht 1983; Fombrun 1983; Brass 1985) and again in the 1990s (e.g., Courtright, Fairhurst and Rogers 1989; Nelson 1989; Burkhardt and Brass 1990). We propose to extend this important body of literature by identifying the factors that cause nurses to network with other health care professionals throughout the hospital organization. These factors include task complexity (measured by task instability and decision-making autonomy) and human capital endowments (measured

The authors gratefully acknowledge support of a Temple University Grant-In-Aid of Research. We thank three anonymous Sociological Focus reviewers for their commente on earlier drafts of this manuscript. We would also like to thank Tracy L. Sharkey, MSN, ARNP; Debbie Cage, MSN, ARNP; Kathleen Linnehan, RN, BSN; and Kathy Merz, RNfortheir valuable insights regarding the role of the nursing professional. 107

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by education and experience). In addition, we examine the impact of networking on nurses' influence over hospital policy and procedure. Our research is based on information processing theories of organization. The "gathering, interpreting and synthesis of information" (Tushman and Nadler 1978, p. 614) is critical to effective decision making by health care professionals. Informationprocessing activities are enhanced by frequent interpersonal interaction. Informationprocessing theories suggest that direct interaction among staff is the coordination mechanism of choice in hospitals, due to the fact that health-care professionals must frequently share a large amount of technically complex information (Thompson 1967). Overton, Schneck and Hazlett (1977) argued that nursing units in hospitals dif­ fer in the technical complexity of the work performed. Information processing theories imply that nursing units should differ in the extent of interpersonal information exchange that occurs. Technically complex units should be characterized by higher levels of interpersonal interaction than units conducting less complex work. We pro­ pose to examine this general premise by developing and testing a causal model of the effects of task complexity on nurses' networking behavior. In addition, we examine the effects of networking on nurses' influence over hospital policy and procedure. By examining whether nurses' information-sharing activities are predictable from variation in task complexity between nursing units, we put informationprocessing theories to a stringent test. Data from a single occupation in a single work environment rule out many alternative explanations that might account for differ­ ences in information sharing between organizations using very different technologies. Previous research examining information networks within a single organization either failed to examine the effects of the technical complexity of work (e.g., Tjosvold 1989) or have examined correlational data without the benefit of causal modeling (e.g., Ito and Peterson 1986). INFORMATION-PROCESSING PERSPECTIVE

According to Eisenhardt and Bourgeois, an information-processing perspective emphasizes "open and forthright discussion, with full sharing of information, in set­ tings open to all decision makers" (1988, p. 738). In addition, Galbraith (1974, 1977) argued that the complexity of the task affects the amount of information that must be acquired and disseminated to facilitate task completion. Galbraith (1974, 1977) posited that organizations design work processes to aid individuals in the acquisition of task-related information. When work activities are simple, coordination between units can be accomplished through programming or interaction among supervisors. When tasks are highly complex, coordination mechanisms with a high capacity for information exchange need to be developed (Thompson 1967). Direct contact among professionals is a coordination mechanism that has a very high information-processing capacity. Through direct personal contact, health-care professionals share data regarding patients' conditions, discuss possible interpreta­ tions and coordinate patient-care activities. Rhodes' (1986) documentary, "The Making of the Atomic Bomb," illustrates the importance of information exchange between pro­ fessionals to the conduct of complex work. The ability of U.S. scientists to develop and institute a research agenda that led to the intended outcome was credited "in large measure [to a] system of team work and free interchange of information" (Rhodes

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1986, p. 645). According to Rhodes, the implications of this project on human life necessitated high levels of openness and cooperation among scientists. The sharing of technical information was viewed as the cornerstone for future advancements in soci­ etal well-being. Thus, the successful completion of a complex project could only be facilitated by an open forum of information exchange among professionals. The theory of organizational information-processing argues that task structure and individual attainments are important determinants of the flow of information in organizations (Smith-Grimm, Gannon and Chen 1991). On the basis of this perspec­ tive, we developed hypotheses proposing relationships between task complexity, human capital endowments and networking patterns. Figure 1 summarizes our hypotheses in a model of networking and influence. Directions of the effects predicted by the information-processing model are shown above the arrows. Task complexity (indicated by task instability and decision-making autonomy) and human capital attainments (indicated by education and experience), are depicted as directly affecting networking relationships. Networking relationships are depicted as directly affecting individual perceptions of influence. Task complexity and human capital endowments are also depicted as directly affecting individual perceptions of influence. FIGURE 1 HYPOTHESIZED MODEL OF NETWORKING AND INFLUENCE

Task Complexity: *Task Instability * Decision Making Autonomy Influence Human Capital Endowments: •Education * Experience

Effects on Networking

Task Complexity. Advances in medical research and technology have had pro­ found effects on the delivery of health care services. These advances have resulted in a substantial amount of variation in the complexity of work conducted by nursing pro-

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fessionals. On the one hand, medical "miracles" have substantially improved the Well­ ness and quality of life for patients, therefore minimizing the demands for care placed on the nursing professional. On the other hand, these same miracles have resulted in significant improvements in average life expectancies. Increased life expectancies have resulted in a "new population of chronically ill patients with multiple health problems" (Emmett 1994, p. 51). For many nurses, the complexity of work will be a function of the high levels of unpredictability of the process of care needed by these patients (Overton, Schneck and Hazlett 1977) and the unmediated responsibility for decisions surrounding appropriate care-giving strategies (Emmett 1994). Within this context we apply two important indicators of task complexity, namely, task instability and decision-making autonomy, to examine relationships between the characteristics of nursing tasks and the tendency of nursing professional to network in an effort to facilitate information exchange. Task Instability. Instability contributes to task complexity because a changing work situation requires more frequent monitoring and adjustment than does a stable one. Task instability in nursing depends upon the extent to which the client's condi­ tion changes over time (Overton, Schneck and Hazlett 1977). Within a moment's time, changes in an unstable patient's condition often result in emergency-like conditions. In this environment, the nursing professional is responsible for constant observation of patients in an effort to ascertain whether modifications to the patient's regimen of care are necessary. Observed changes in patient condition trigger another important activity on the part of the nurse. According to Overton et al. (1977) the nurse also engages in "search" behavior surrounding the application of the appropriate technical skills and medical equipment. The entire process is facilitated by open communication with physicians and other health-care professionals. For these reasons, nurses responsible for the care of unstable patients are likely to communicate with other hospital staff more fre­ quently than those involved in the care of patients with stable conditions. Hence, we apply the following information-processing hypothesis to the case of hospital nurses: H : Task instability will be positively related to nurses' participation in intraunit and interunit networking.

Decision-Making Autonomy. Decision-making autonomy is the extent to which nurses have the ability to make patient care decisions themselves. Decision making autonomy enhances task complexity because it requires the use of higher-order cogni­ tive skills. Autonomy enhances the development of interpersonal networks, as workers seek information with the goal of making the best possible decisions. Nurses who have more discretion in administering patient care may benefit from information exchange with other health-care professionals. Supporting this contention, previous research by Ito and Peterson (1986) has shown that autonomy was positively correlated with the frequency of interaction with members of other work units among employment service workers. Hence, we apply the following information-processing hypothesis to the case of hospital nurses: H : Decision-making autonomy will be positively related to nurses' participation in intraunit and interunit networking.

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A natural link exists between the conduct of complex work and advanced skill and experience. The care of patients with unpredictable problems demands the ser­ vices of nurses capable of assuming the role of a "multisystem specialist" (Emmett 1974, p. 52). To fulfill that role, experience, education and technical expertise have become important requisites. Thus, in addition to our examination of the relationships between task complexity and networking, we explore the impact of education and experience on networking. Human Capital Endowments

Experience and Education. Under the information-processing perspective, human capital characteristics, specifically experience and education, were also expected to have positive effects on networking among nurses. Schein (1978) argued that more experienced workers may take on increased responsibility for task comple­ tion. The careers of experienced workers can take many paths involving promotions, increased responsibility and mentoring roles (Slocum, et al. 1985). Increased respon­ sibility and mentoring activities suggest a willingness to share task-related infor­ mation with other nurses in order to aid in the accomplishment of their work. Partial support for these arguments was provided by Zenger and Lawrence (1989), who demonstrated that organizational tenure was positively associated with the degree of technical communication outside the immediate work group. Also, more experienced and highly educated nurses may be sought by others throughout the organization for information and advice on how to conduct patient-care activities. For these reasons, we hypothesized that: H : Education and experience will be positively related to intraunit and interunit networking. Effects on Self-Perceived Influence

Our model suggests that intraunit and interunit networking will have direct effects on nurses' influence over policy and procedure. Participating in communication networks helps individuals accrue power and influence because they have more oppor­ tunities to share their ideas with others and to persuade others of the value of those ideas. Networking also provides the individual with information regarding the response of opinion leaders to their ideas, which they can use to modify either the pre­ sentation of the ideas or the ideas themselves. A number of studies have established important links between informal networking patterns within organizations and indi­ vidual power and influence (Fombrun 1983; Brass 1985). Following these important works, we propose that: H : Nurses who participate in intraunit and interunit networking will perceive themselves to have greater influence over nursing unit, nursing department and hospital policies.

Our model also suggests that task complexity and human capital endowments may have direct effects on influence. Resource scarcity represents the core of this argument and suggests that those who possess organizationally relevant knowledge and skills and who conduct complex work acquire influence over organizational

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decisions (Pfeffer and Salancik 1974; Bacharach and Lawler 1980). As such, we pre­ dicted that for nurses: H : Education, experience, decision-making autonomy and task instability will be positively related to perceptions of influence.

OVERVIEW OF RESEARCH PROCEDURES The entire staff of Registered Nurses at a large, affiliated suburban hospital was sent a mailed questionnaire in the spring of 1989. Participation in the study was voluntary, with respondents being assured that their individual responses would not be made available to nursing and hospital administration. The questionnaire was fol­ lowed by a reminder letter and a second reminder that included a second copy of the survey. This procedure resulted in a 67 percent response rate and a sample size of 432. Almost all of the respondents (99 percent) were women. Respondents' ages varied from 21 to 68 years, with an average age of 34. Average length of service with the organization was six years, and the average level of nursing experience was nine years. The educational levels of respondents included 47 percent holding a Nursing Diploma, 15 percent holding an Associate Degree, 36 percent holding a Bachelor of Science in Nursing and 2 percent holding a Master of Science in Nursing. The survey included several multi-item scales to measure constructs included in the model. These measures are discussed in detail below. Measurement of Variables

Human Capital Endowments. Respondents were asked to complete survey items indicating highest nursing degree earned (1 = Nursing Diploma, 2 = Associate of Arts or Science in Nursing, 3 = Bachelor of Science in Nursing, 4 = Master of Science in Nursing) and total months of nursing experience. Task Characteristics. Measures of task instability were adapted from Overton, Schneck and Hazlett's (1977) and Leatt and Schneck's (1981) measures of nursing technology. The original questions required respondents to supply answers in percentage form (e.g., What percentage of your patients require nursing observation more often than once every half hour?). Pretesting indicated that estimating percentages was difficult and that many of the questions were unclear. The final survey items were modified with the assistance of nursing education and professors. The items read as follows: How many of your patients need nursing observation more often than once every half hour? (Instability 1) How much of your work involves performing technical procedures and special tests? (Instability 2) How many of your patients require the use of technical equipment (e.g., cardiac monitors, respirators, etc.)? (Instability 3)

Response options ranged from 1 = almost none to 5 = almost all. A high score on each item indicated high task instability.

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Decision-making autonomy was measured by questions adapted from Curry (1986) asking the extent to which respondents were free to make their own patientcare decisions. The items read as follows: My job allows me to take part in patient-care decisions almost all the time. (Decision Making 1) I can make patient-care decisions on my own almost all the time. (Decision Making 2) I have a great deal of freedom in conducting patient-care activities. (Decision Making 3)

Response options ranged from 1 = strongly disagree to 5 = strongly agree. A high score on this index indicated high decision-making autonomy. Networking. Survey items developed by Trow (1975) and Trow and Associates (1975) and used in two studies (see Pfeffer and Langten 1988; Konrad and Pfeffer 1990) were adapted for the present study to measure the various patterns of network­ ing. The amount of networking with nurses working on the same unit (intraunit) and on different units (interunit) were assessed. The items assessing intraunit networking read as follows: How often do you have lunch with other nurses from your unit? (Intraunit Networking 1) How many nurses from your unit do you see socially? (Intraunit Networking 2)

Response options for the first item ranged from 0 = never to 5 = every day. Response options for the last items ranged from 0 to 5 or more. Influence. Influence over unit, department and hospital policies was also measured using items developed by Trow (1975). The items read as follows: How active are you in making policy or procedural decisions on your unit? (Influence 1) How active are you in making policy or procedural decisions in the nursing department as a whole? (Influence 2) How active are you in making policy or procedural decisions in the hospital as a whole? (Influence 3)

Response options for these items ranged from 1 = much less than average to 5 = much more than average. DATA ANALYSIS We developed a multiple-indicator structural equation model to represent the hypotheses and estimated the parameters using the maximum-likehhood method of LISREL (Joreskog and Sorbom 1984).1 In Figure 2, we present the model using Greek letters (by convention) to represent the parameters we estimated. We report the chisquare goodness of fit test using LISREL maximum-likelihood estimates to assess how well the model fit the data. Hypothesis testing was conducted by examining the direc­ tion and significance of regression weights (ß s ) estimated by the LISREL program. The tests of significance for the model regression weights are indicated in the columns labeled "C.R." in the tables. The critical ratio is an observation on a random variable that has an approximate standard normal distribution. Thus, a critical ratio that exceeds 1.96 in magnitude is significant at the .05 level, using a two-tailed test. A

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critical ratio exceeding 2.58 is significant at the .01 level and a critical ratio exceeding 1.64 is significant at the . 10 level. Since the chi-square test is sensitive to sample size, we reported additional goodness of fit indices developed by previous authors, including the NNFI (Tucker and Lewis 1973), the Delta (Bentler and Bonett 1980), the AIC (Akaike 1987) and the BCC (Browne and Cudeck 1989). The Bentler-Bonett Delta essentially compares the chisquare observed for the theoretical model with the chi-square which would be obtained under the assumption of the "independence" model that all observed variables are uncorrelated with one another. The ratio of the chi-square for the theoretical model to the chi-square for the independence model is calculated and subtracted from 1.0. Bentler and Bonett (1980) suggest that a Delta of .90 or greater indicates good fit. The index developed by Tucker and Lewis (1973) is similar but adjusts for degrees of free­ dom and has been called the Bentler-Bonett non-normed fit or NNFI for this reason. An NNFI of .90 or greater indicates good fit. FIGURE 2 STRUCTURAL MODEL OF NETWORKING AND INFLUENCE

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The Akaike (1987) and Browne and Cudeck (1989) indices follow a different logic altogether. The Akaike (AIC) index is essentially comprised of the observed chi-square plus twice the number of parameters estimated in the model. A smaller AIC indicates a better fit in that the model with a smaller AIC both fits the data better and is more parsimonious than a model with a larger AIC. Goodness of fit is assessed by compar­ ing the AIC for the theoretical model to the AIC for the independence model. The theoretical model's AIC is also compared to that of the "saturated" model, a model that allows every observed variable to be correlated with every other observed variable. The saturated model is extremely unparsimonious while the independence model is extremely difficult to fit to any real data. A theoretical model must be a better fit than either of them in order to be acceptable. For this reason, a theoretical model that has a smaller AIC than both the independence and the saturated models is considered to have a good fit. The BCC index (Browne and Cudeck 1989) is similar to the AIC but imposes a slightly larger penalty for lack of parsimony than the AIC does. Model Estimation. We made two adjustments to our original model that greatly improved fit to the data and which we believed did not violate the theoretical integrity of our model. First, we estimated the covariance between the measurement errors on the two latent networking variables. This step relaxed the assumption that the two networking constructs had a covariance of zero and corrected any bias in the estimates of the regression weights arising from that restrictive assumption. Second, we estimated the covariance between the measurement errors on two of the three indicators of influence. This improved the fit of the model to the data, indicating that the two indicators shared some commonality that was not part of the third indicator or the latent influence variable. Examining the wording of these two survey items showed that both asked the respondent about influence outside the immediate nursing unit. Evidently, our respondents who felt they had influence over policy in the nursing department as a whole also felt they had influence over policy in the hospital as a whole. Respondents who felt they had influence over unit policy did not as consistently feel influential in matters outside the immediate unit. RESULTS Means, standard deviations and correlations for observed variables are shown in Table 1. Table 2 shows the goodness of fit indices for our model. As depicted in Table 2, all goodness of fit indices showed that the model fit the data. The chi-square was nonsignificant, indicating that the data did not depart significantly from the model. The Delta and NNFI were both greater than .90, indicating that the fit of our model was sufficiently better than the independence model. The AIC and BCC indices were smaller for our model than for either the saturated or the independence models, indi­ cating that on the basis of parsimony and fitting the data, our model was acceptable. The convergence of all these different indicators of fit validates our confidence in the model. Tables 3 and 4 show the parameter estimates for our model. The parameter estimates shown in Table 3 comprise the tests of construct validation for the unob­ served or latent variables in the model. This is sometimes referred to as the "measurement model" or confirmatory factor analysis. For each construct, one regres­ sion weight (λ) on the latent variable was constrained to 1.0 for purposes of model

S.D. 0.94 91.20 1.29 1.11 1.62 0.86 0.90 0.87 1.76 1.18 1.42 1.31 1.80 1.22 0.94 0.82

Mean

1.95 Education 111.80 Experience 3.37 Instability 1 3.20 Instability 2 3.41 Instability 3 3.83 Decision Making 1 3.46 Decision Making 2 3.45 Decision Making 3 2.55 Interunit Networking 1 Interunit Networking 2 1.37 1.17 Interunit Networking 3 4.33 Intraunit Networking 1 2.49 Intraunit Networking 2 2.36 Influence 1 1.82 Influence 2 1.54 Influence 3

Variable

1. 2. 3. 4. 6. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. -21 -05 -09 -06 -00 -08 -02 -05 -01 -15 -03 -03 -07 -05 -05

1

03 06 07 01 02 -12 -10 -09 -13 -18 -06 -03 -04 -09

2

57 46 -00 -02 -05 05 03 08 10 08 14 12 17

3

53 -01 -02 -07 -00 00 05 12 07 14 06 07

4

-05 -06 -07 08 07 03 09 16 16 06 04

5

44 46 05 10 06 -06 07 13 18 11

6

55 11 13 07 -04 07 09 13 08

7

14 09 11 -02 03 13 15 12

8

36 31 13 11 20 14 10

9

32 12 18 23 17 07

10

12 20 10 05 07

11

MEANS, STANDARD DEVIATIONS AND CORRELATIONS AMONG OBSERVED VARIABLES

TABLE 1

16 18 15 14

12

27 16 08

13

68 54

14

76

15

16

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identification. Critical ratios on all other observed variables were significant well beyond the .01 level, indicating that a significant amount of variance in the observed variables was accounted for by the latent variables, validating our measurement of the theoretical constructs. TABLE 2 GOODNESS OF FIT INDICES Theoretical Model

Index

Independence Model

Saturated Model

_ df n parameters Delta NNFI AIC BCC

87.18 84 52

1243.37 120 16

0.00 0 136

.93

0.00

1.00

.996

0.00

—

223.18

1307.37

304.00

230.86

1310.98

321.17

Table 4 shows the causal relationships among the latent variables specified in our model. A substantively significant percentage of the variance in all three endoge­ nous variables was accounted for by the model. The R2s, indicating the amount of variance accounted for in the networking variables, were .22 and .12 for intraunit and interunit networking, respectively. The Rr for the influence variable was .39. Although many of the hypothesized relationships were not significant, the data provided some support for the hypotheses developed under the information-processing perspective. TABLE 3 MAXIMUM LIKELIHOOD ESTIMATES FOR CONSTRUCT VALIDATION Parameter

S.M.L.E.

U.M.L.E.

S.E.

CJL

Instability λ2 λ3

.70 .80 .67

h h h

— — —

H

Decision-Making Autonomy λ4 λ5 *6 δ4 «5 δ«

.60 .71 .77

— — —

1.00 0.98 1.19 0.84 0.44 1.46

0.10 0.13 0.10 0.08 0.15

1.00 1.24 1.30 0.47 0.40 0.31

0.15 0.16 0.05 0.05 0.05

—

—

—

9.71 9.53

— — — —

8.45 8.35

— — —

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118 TABLE 3 (Continued)

Parameter Intraunit Networking λ7 >-8