Within-person analysis of information seeking: the effects of perceived costs and benefits

Journal of Management 2000, Vol. 26, No. 1, 119 –137

Within-Person Analysis of Information Seeking: The Effects of Perceived Costs and Benefits Elizabeth W. Morrison New York University

Jeffrey B. Vancouver Ohio University

This study focused on how perceived costs and benefits affect information seeking across multiple types and sources of information. This focus required a within-person approach to data collection and analysis. Respondents were 282 early-career engineers. The results demonstrate that individuals selectively seek different types of information, and utilize different sources, based on assessments of corresponding costs and benefits. Results provide insight into individuals’ decisions about what information to seek and from whom, and highlight the value of studying within-person patterns of information seeking. © 2000 Elsevier Science Inc. All rights reserved.

Information seeking can be a useful strategy for individuals dealing with uncertain environments (Berger, 1979; Miller & Jablin, 1991). In organizational settings, individuals often actively assess organizational expectations and goals, as well as their progress toward meeting those expectations and goals, as part of a process of self-regulation (e.g., Ashford & Tsui, 1991; Morrison, 1993a). Thus, understanding the conditions that motivate or deter information seeking has become a critical concern for organizational researchers. During the past decade, there have been several empirical studies of individual and contextual factors that affect information-seeking behavior. With few exceptions, these studies have built upon the model of feedback-seeking behavior from Ashford and Cummings (1983) (e.g., Ashford, 1986; Ashford & Cummings, 1985; Ashford & Northcraft, 1992; Ashford & Tsui, 1991; Brett, Feldman, & Weingart, 1990; Fedor, Rensvold, & Adams, 1992; Levy, Albright, Cawley, & Williams, 1995; Morrison & Cummings, 1992; Morrison & Weldon, 1990; VandeWalle & Cummings, 1997). Consistent with earlier models of information

Direct all correspondence to: Elizabeth W. Morrison, Department of Management, Stern School of Business, New York University, 44 West 4th Street, New York, NY 10012; Phone: 212-998-0230; E-mail: [email protected]. Copyright © 2000 by Elsevier Science Inc. 0149-2063 119

120

E.W. MORRISON AND J.B. VANCOUVER

seeking found within the communication literature and the uncertainty reduction literature (e.g., Berger & Calabrese, 1975; Berlyne, 1960), the Ashford and Cummings (1983) model conceptualizes information seeking as a process of uncertainty reduction, whereby an individual decides whether to allocate energy toward information seeking based on the anticipated benefits of acquiring information and the anticipated costs of obtaining that information. The empirical studies that have built upon this basic model have operationalized costs and benefits in a variety of ways. Perceived benefits, for example, have been represented as the anticipated value of feedback (Ashford, 1986; Mignerey, Rubin, & Gordon, 1995; VandeWalle & Cummings, 1997), feedback diagnosticity (Morrison & Cummings, 1992), goal orientation (VandeWalle & Cummings, 1997), uncertainty (Ashford & Cummings, 1985), source credibility (Fedor et al., 1992), and desire for control (Ashford & Black, 1996). Perceived costs have been operationalized as anticipated effort (Ashford, 1986), perceived social costs (Ashford, 1986; Fedor et al., 1992; Holder, 1996), negative performance expectations (Morrison & Cummings, 1992; Northcraft & Ashford, 1990), the presence of an audience (Ang, Cummings, Straub, & Earley, 1993; Ashford & Northcraft, 1992: Levy et al., 1995), and low self-confidence (Ashford, 1986; Fedor et al., 1992). Overall, the results of these studies indicate that the frequency of feedback seeking is positively related to perceived benefits and negatively related to perceived costs. Although the above studies have provided empirical support for the basic cost-benefit model of information seeking, it is noteworthy that these studies have all focused on explaining just one of the decisions that face information seekers: the decision whether or not to seek feedback. Yet, employees are confronted with many information-seeking options. There are several different types of information that they can seek and several different ways in which they can do so (Morrison & Bies, 1991; Vancouver & Morrison, 1995). Hence, it is useful to understand how perceived costs and benefits affect not simply the decision of whether to seek information, but also decisions about what information to seek and from whom. The objective of this study is to shed some light on this issue by looking at the effects of perceived costs and benefits on information seeking across multiple types and sources of information. Studying Information Seeking at a Within-Person Level of Analysis Expanding upon the existing research in this way not only provides a richer understanding of information seeking, but requires a unique approach to testing the underlying theory. With only one exception (Vancouver & Morrison, 1995), research in this area has focused exclusively on between-person relationships, yet the theory at the foundation of that research is actually more consistent with a within-person approach. Although research on information seeking has not always been explicit in delineating its underlying theoretical framework, the emphasis on costs and benefits has clear parallels to more general theories of behavioral choice, such as expectancy theory and other expectancy-value models (Kanfer, 1990; Stevenson, Busemeyer, & Naylor, 1990). These theories were designed to explain JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

121

within-person choices, or, in other words, variance across situations or options (Mitchell, 1974; Van Eerde & Thierry, 1996; Vroom, 1964). Because the same cost-benefit paradigm is at the foundation of information-seeking research, it is appropriate that researchers also focus on within-person dynamics. The difference between a within- and a between-person approach is not trivial, as we illustrate in Figure 1. Figure 1a represents a typical between-person approach to research. One variable, x, is correlated with another variable, y, across persons. An example is the study by Fedor et al. (1992), which assessed, among other things, the relationship between feedback seeking and source credibility. In these types of cases, the correlation is based on the similarity between the two elements of each pair of scores, relative to differences in the scores across people. This approach often makes sense when the variables reflect properties of the person (e.g., if one is interested in the relationship between a personality trait and a specific behavior). In contrast, when one is interested in how individuals allocate resources across an array of options or “targets,” then variance across targets is more relevant than variance across individuals. In Figure 1b, each person has multiple targets or sources of information, each with a different level of perceived credibility. In this example, one might expect that individuals would seek information from those sources that they perceive as having the highest credibility. If one were

Figure 1. Differences between the Between- and Within-Person Approach to Examining Information Seeking JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

122

E.W. MORRISON AND J.B. VANCOUVER

interested in testing this prediction, it would be necessary to correlate information seeking with credibility across various information sources. A between-person approach would not make sense in this case. If one is interested in Person A’s choices, the credibility of the sources available to Persons B through E is irrelevant. Consider an extreme example. Suppose that the average level of perceived source credibility (averaged across the various sources of information) did not vary from one respondent to the next. Yet, suppose that, for each respondent, some sources were viewed as more credible than others: If one were to form an aggregate measure of credibility for each respondent, and correlate this with information seeking, no correlation would be found. But it would be erroneous to conclude from this that credibility does not affect how people seek information. To test this idea, one would need to focus on within-person relationships. One would assess, for each individual person, whether variance in credibility across the different sources relates to variance in information seeking across those sources. At the other extreme, consider a situation where each respondent regards all sources as equally credible, but the respondents differ from one another (e.g., Person A rates all sources as high on credibility, and Person B rates all sources as medium). In this case, there is variance between persons, but none within. If one were, however, interested in assessing whether credibility affects a person’s selection of an information source, the between-person variance would be irrelevant. A between-person analysis might lead one to erroneously conclude that source credibility affects how an individual seeks information, when, in fact, each person is likely to use the available targets equally as sources of information. Unfortunately, calculating correlations between persons but separately for each source (e.g., Ostroff & Kozlowski, 1992), may lead to similar errors of inference. Although these are extreme examples, it should be clear that the research showing a positive relationship between information seeking and perceived benefits, and a negative relationship between information seeking and perceived costs, does not enable one to conclude that similar effects will be found at the within-person level when multiple targets are available. In the case of expectancy theory research, within-person studies have generally found stronger relationships than between-person studies (Van Eerde & Thierry, 1996), and we suspect that the same would be true for research on information seeking. In sum, this study addresses a question that has not previously been addressed. It looks at employees’ perceptions of the costs and benefits associated with seeking different types of information and associated with different information sources. It then assesses how these perceptions relate to how much of the different types of information they seek and how much they rely on different sources. This study also employs a methodological approach that has not typically been used in the information-seeking literature. Rather than assessing betweenperson relationships, it assesses relationships at a within-person level. This approach is consistent with the accepted theoretical paradigm within the information-seeking literature, which argues that individuals allocate their information JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

123

seeking energies based on their perceptions of costs and benefits (e.g., Ashford & Cummings, 1983). Information Seeking Across Types of Information To date, there have been no studies, at either the between- or within-person level, of how employees allocate energy toward acquiring different types of information. Studies of organizational newcomers, however, have shown that information seeking is not restricted to feedback. Research (e.g., Miller & Jablin, 1991; Morrison, 1993a; Ostroff & Kozlowski, 1992) indicates that there are five types of information that employees tend to seek, each corresponding to a particular domain of knowledge: task information about how to perform specific job activities and assignments; role information about the expectations and responsibilities associated with the job; social information about co-workers and about how to behave within one’s workgroup; organizational information about organizational policies, procedures, structures, and objectives; and performance information indicating how well one is performing the job. It follows from the underlying cost-benefit paradigm that information seeking across these five types of information will reflect, in part, an individual’s evaluation of the benefits that can be gained by acquiring each of them. The benefits associated with feedback seeking have often been operationalized as the perceived value or importance of feedback for helping one to achieve valued goals (Ashford, 1986; Migerney, Rubin, & Gordon, 1995). Likewise, the benefits associated with the seeking of other types of information can be operationalized as the perceived value or importance of those types of information. Expectancyvalue models of behavior suggest that an individual’s allocation of energy toward acquiring various types of information will relate to how valuable or important he or she regards each type. Hence, we predict the following: H1: An employee will seek more of a given type of information to the extent that he or she perceives that information of that type is important. Thus, within-person differences in information seeking across various types of information will reflect differences in the perceived importance of those information types.

Much of the between-person research supports the notion that individuals who evaluate the costs of information seeking to be high, seek less information than those who evaluate the costs to be low (Ashford, 1986; Ashford & Northcraft, 1992; Fedor et al., 1992; VandeWalle & Cummings, 1997). Nonetheless, the results have not been uniformly supportive. Ashford (1986) found feedback seeking to be unrelated to both perceived effort and perceived risk. Although it is unclear why these relationships were unsupported, one possibility is that individual perceptions of effort and risk are more powerful in explaining within- than between-person differences in information seeking (cf. Van Eerde & Thierry, 1996). That is, effort costs may more strongly predict the type of information one seeks when multiple options are available, than whether one seeks a particular JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

124

E.W. MORRISON AND J.B. VANCOUVER

type of information in an absolute sense. Based on within-person models of behavioral choice (e.g., Vroom, 1964), we hypothesize that perceived effort will relate to an individual’s choices about the types of information to seek. H2: An employee will seek less of a given type of information to the extent that he or she perceives that it is difficult to obtain that type of information. Thus, within-person differences in information seeking across various types of information will reflect differences in the perceived difficulty of obtaining those information types.

Information Seeking Across Sources Research on newcomer information seeking indicates that the following may be utilized as sources of work-related information: supervisors, friends, experienced co-workers, mentors, and written documents (Morrison, 1993b; Ostroff & Kozlowski, 1992). There has not, however, been any research on factors that influence the relative utilization of these different sources. In this study, we focus on the impact of perceived expertise and perceived accessibility. Research on how individuals respond to various feedback sources (Ilgen, Fisher, & Taylor, 1979), and on how they evaluate information from various sources (Gerstberger & Allen, 1968; O’Reilly, 1982), provides some indirect evidence that these two factors affect the relative utilization of the information sources listed above. Perceived expertise is defined as the extent to which a source is believed to possess accurate and useful knowledge. Perceived accessibility is defined as the anticipated ease with which one would be able to locate and utilize a particular source. These two source characteristics reflect the basic cost-benefit analysis that is theorized to drive information seeking (e.g., Ashford & Cummings, 1983). Source expertise increases the potential benefits of information seeking, whereas source accessibility affects the potential costs that would be incurred if one chose to seek information. There has been relatively little between-person research on information seeking and perceived expertise, and the research that has been done has been inconclusive. Fedor et al. (1992) found partial support for their prediction that information seeking would be positively related to source credibility (a construct closely related to expertise), but another study failed to find such a relationship at all (Fedor, Eder, & Buckley, 1989). One possible reason for these inconclusive findings is that the relationship is more appropriately studied at a within-person level. That is, perceived expertise may be a better predictor of from whom one seeks information than simply how much one seeks from a single source. In a within-person policy-capturing study, Vancouver and Morrison (1995) found that individuals were more willing to seek information from a colleague, to the extent that the colleague was believed to possess high expertise. That study, however, used a student population responding to paper stimuli and did not assess information seeking across a range of different sources or information types. Thus, the present study significantly expands upon Vancouver & Morrison (1995) by testing whether employee information seeking is related to differences in perceived expertise across several different sources. JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

125

H3: An individual will seek more information from a given source to the extent that he or she evaluates the source as possessing expertise. Thus, within-person differences in information seeking across sources will reflect perceptions about the sources’ expertise.

There is evidence from between-person research on organizational decision making that, in addition to expertise, source accessibility also affects the amount of information that individuals seek from a given source (Gerstberger & Allen, 1968; O’Reilly, 1982). Yet, it is less clear whether perceived source accessibility affects within-person patterns of information seeking. The only study that has taken a within-person approach to studying this issue found that source accessibility related to differences in information seeking across situations (Vancouver & Morrison, 1995). As noted, however, that study did not assess information seeking across a range of different sources. The present study tests whether information seeking relates to perceived differences in expertise across multiple sources. Based on within-person models of behavioral choice, we predict the following: H4: An individual will seek more information from a given source to the extent that he or she evaluates that source as accessible. Thus, within-person differences in information seeking across various sources will reflect perceptions about the sources’ accessibility.

Moderating Effects of Need for Achievement Although we argue that individuals will use cues about target properties when selecting sources for information, we also expect that individual traits or dispositions will influence how heavily one relies upon those cues. In their policy-capturing study, Vancouver and Morrison (1995) found that need for achievement related to the degree to which subjects used expertise as a cue in deciding whether to seek feedback from a colleague. The inclusion of need for achievement as a moderator in that study was based on the existing feedbackseeking literature, which conceptualizes information seeking as a goal-motivated behavior (Ashford & Cummings, 1983). Halish and Heckhausen (1977) and Trope (1975) found evidence that high need achievers “attach greater value to accurate and diagnostic feedback than low need achievers” (Vancouver & Morrison, 1995). In addition, Cherrington (1989) argued that individuals with high need for achievement have a strong desire for performance feedback, regardless of the sign of that feedback. Vancouver and Morrison (1995) argued that if individuals with high need for achievement attach greater value to accurate information than low need achievers, they should attach greater weight to source expertise when selecting feedback sources, because expertise implies more accurate and valuable information. The global nature of need for achievement (Kanfer, 1990) suggests that its role in information seeking will extend beyond feedback information. Hence, we expect the moderating effect of need for achievement on the relationship between expertise and information seeking (Vancouver & Morrison, 1995) to generalize across the five types of information assessed in this study. JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

126

E.W. MORRISON AND J.B. VANCOUVER

We also expect need for achievement to moderate the relationship between information seeking and the perceived importance of different types of information. The rationale for this prediction is similar to the rationale for the prediction above. If individuals with high need for achievement attach greater value to accurate and diagnostic information than individuals with low need for achievement (Vancouver & Morrison, 1995), then they should be more attuned to differences in information utility when deciding how to allocate energy toward information seeking. H5: The positive within-person relationship between information seeking and perceived source expertise will be moderated by need for achievement. As need for achievement increases, the relationship becomes stronger. H6: The positive within-person relationship between information seeking and perceived importance at the level of information types will be moderated by need for achievement. As need for achievement increases, the relationship becomes stronger.

To summarize, this study looks at both the types of information that employees seek and the sources that they utilize. Our first dependent variable is information seeking across five types of information. We hypothesize that this variable will relate to an employee’s perceptions of the importance and difficulty of those five types. Our second dependent variable is information seeking across five sources of information. We hypothesize that this variable will relate to an employee’s perceptions of the expertise and accessibility of those five sources. In essence, we are predicting that decisions about what information to seek, and from whom to seek it, are affected by similar factors (i.e., difficulty has parallels with accessibility, and importance has parallels with expertise). The study also assesses the moderating effect of need for achievement, both on the relationship between perceived expertise and information seeking across sources, and on the relationship between perceived importance and information seeking across types. Method Sample The sample for this study was comprised of aerospace engineers. Although information seeking is likely to be an important activity for professionals of all types, it may be especially important in engineering. Engineering entails a high degree of uncertainty and requires that information, data, and knowledge be continually acquired, produced, transferred, and utilized (Pinelli, Bishop, Barclay, & Kennedy, 1993). In fact, there have been several studies focused on the role and diffusion of information within the engineering profession (Allen, 1977; Gerstberger & Allen, 1968; Rosenbloom & Wolek, 1970; Young & Harriot, 1979). These studies, however, have been primarily descriptive and have restricted their focus to scientific and technical information. JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

127

The engineers in this study were recent college graduates who had held full-time positions in engineering for three years or less. We focused on employees of relatively short organizational tenure (i.e., newcomers), because we wanted a sample of employees who would be experiencing relatively high levels of uncertainty and thus a strong need for information (Miller & Jablin, 1991). Because this was not a study of the entry process per se, however, we adopted a broader definition of “newcomer” than typically found within the literature on organizational entry. Most empirical studies of newcomers have restricted their focus to the first year of employment. Yet, for the sample used here, it was appropriate to view the newcomer period as lasting considerably longer. Lee (1992; Lee & Allen, 1982) noted that it takes at least two years for professional engineers to develop fully in a new work environment, and, in one of the few empirical studies of new engineers, Gundry (1993) defined the newcomer period as the first three years in an organization. This long newcomer period reflects the complex nature of aerospace engineering, where individuals are viewed as apprentices for the first three to five years. In the spring of 1995, surveys were sent to all members of the American Institute of Aeronautics and Astronautics (AIAA) who had converted their membership status from student to professional within the previous five years. Seven hundred surveys were sent, and 319 completed surveys were returned from individuals indicating they were currently employed as engineers. (An additional 68 individuals indicated, via e-mail, phone, or mail, that they were not currently employed in engineering and thus inappropriate for the study.) Of the 319 surveys returned, 25 were eliminated because the respondents indicated that they had been employed for longer than three years, and 12 were eliminated because of missing data. The final sample size was 282, for an overall response rate of 40.3%. Measures The questionnaires assessed information seeking across five types of information (task, role, social, organizational, and performance) and across five information sources (supervisor, friend, colleague, mentor, and documents). Further, they assessed perceptions of the importance and difficulty of obtaining each of the five types of information, and perceptions of accessibility and type-specific expertise for each of the five sources. The surveys also assessed need for achievement, and they assessed job tenure to verify that respondents met the inclusion criteria. The assessment of information seeking via self-reports is consistent with other studies on information seeking (Ashford, 1986; Ashford & Black, 1996; Ashford & Cummings, 1985; Fedor et al., 1992; Migerney et al., 1995; Morrison, 1993a, 1993b), and with Morrison’s (1993b) conclusion that self-reports are more valid than reports from supervisors or co-workers, because the latter may not always attend to a focal employees’ information-seeking behavior. Three questions were used to assess perceived importance. Respondents answered these questions for each of the five types of information, which were defined for them at the top of the page. Responses were on a five-point scale, ranging from “strongly disagree” to “strongly agree.” For example, respondents JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

128

E.W. MORRISON AND J.B. VANCOUVER

were asked the degree to which they agreed with the following statement: “It is extremely important that someone in your position have information of this type.” The items were averaged to create an importance scale for each of the information types. Internal consistency reliabilities for the scales were .79, .69, .76, .75, and .62 for task, role, social, organizational, and performance information, respectively. Difficulty of obtaining information was assessed with two items about each of the five types of information. The items were: “Information of this sort is extremely difficult for people in your position to obtain” and “Assuming you wished to do so, it would be very difficult for you to obtain such information.” Responses were on a five-point agree/disagree scale. Reliabilities for the resulting scales were .74, .78, .77, .77, and .76 for task, role, social, organizational, and performance information, respectively. Five items assessed perceived expertise for each of the sources. The five sources were defined for respondents at the top of the page. This was done to ensure consistent understanding and to ensure that the sources were distinct from one another. For example, “colleagues” were defined as “co-workers at the same level as yourself who perform duties similar to you (other than those you view as personal friends),” to distinguish them from “friends,” and “mentor” was defined as “a person at a higher level than yourself, other than your direct supervisor, who is committed to providing you with career guidance and/or support,” to distinguish mentor from “supervisor.” The items were in the format “This source is a good repository of knowledge about ...,” with each item pertaining to a particular information type. Responses were on a five-point agree/disagree scale, with a “not applicable” option. We averaged the five items for each source to obtain overall expertise measures, reflecting the extent to which each source possessed expertise across the range of information types. The reliabilities for the resulting scales were .80, .77, .78, .77, and .76 for supervisor, friend, colleague, mentor, and documents, respectively. Source accessibility was assessed with three items for each of the information sources. A sample item is “You could contact this source very easily if you needed to obtain information.” Responses were on a five-point agree/disagree scale, with a “not applicable” option. The items were averaged to create an accessibility scale for each source. Internal consistency reliabilities for these scales were .77, .68, .75, .79, and .81 for supervisor, friend, colleague, mentor, and documents, respectively. Information seeking was assessed by asking respondents to indicate how much of each information type they had actively obtained from each of the five sources. We assessed “information actively obtained” rather than “information actively sought,” based on the argument that researchers should “assess the amount and quality of information that newcomers actually obtain from seeking . . .[F]requent information seeking does not guarantee that a seeker will obtain a sufficient amount of information or information that is useful” (Morrison, 1993: 584). In all, there were 25 information-seeking items (five sources x five types). Responses were on a five-point scale ranging from “none at all” to “a great deal.” There was also a “not applicable” option. When the items were collapsed across JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

129

information type (to obtain a scale for each source), the reliabilities were .76, .81, .80, .85, and .71 for supervisors, friends, colleagues, mentors, and documents, respectively. When the items were collapsed across information source (to obtain a scale for each type), the internal consistency reliabilities were .39, .58, .70, .65, and .68, for task, role, social, organizational, and performance, respectively. Need for achievement was measured with the achievement subscale of the Manifest Needs Questionnaire from Steers and Braunstein (1976). The scale contains five items on five-point response scales (strongly agree/strongly disagree). The scale had a mean of 4.21 (SD ⫽ .59) and an internal consistency reliability of .75. Analysis To test the four within-person hypotheses, we performed two repeatedmeasures regression analyses (Cohen & Cohen, 1983). The dependent variable for the first analysis was information seeking across information types, and the dependent variable for the second analysis was information seeking across sources. Unlike repeated-measures ANOVA, repeated-measures regression handles repeated and continuous independent variables, as well as repeated and continuous dependent variables. The procedure treats each set of observations as a separate case. Hence, there were five “cases” for each respondent, corresponding to the five information types for the first analysis, and to the five sources for the second analysis. The source of the cases (i.e., the respondent) was accounted for via the creation of a dummy variable for all but one participant. These dummy variables were entered in the first step of a hierarchical regression analysis. This procedure controlled for person effects in all subsequent steps in the analysis. Given the potential for type and source effects beyond the attributes examined in this study, two additional sets of dummy variables were created— one reflecting information type and the other reflecting information source. The appropriate set was entered in the second step of the hierarchical regression analysis. This controlled for any main effects due to information type in the first analysis, or to information source in the second analysis. Finally, we entered the set of independent variables of interest for each analysis (e.g., importance and difficulty for information seeking across types, expertise and accessibility for information seeking across sources). The repeated-measures procedure enabled us to test the within-person hypotheses, but not the cross-level hypotheses (Hypotheses 5 and 6). To test these hypotheses, a random effects model was required (Bryk & Raudenbush, 1992). Hierarchical linear modeling (HLM) is emerging as a popular method for random effects modeling (Bryk & Raudenbush, 1992; Hofmann, 1997; Vancouver, 1997; Vancouver, Millsap, & Peters, 1994). Essentially, HLM calculates a regression equation for each person, which allows for additional person-level variables to serve as predictors of the within-person regression equation parameters (i.e., the intercepts and slopes). JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

130

E.W. MORRISON AND J.B. VANCOUVER

Results The average means, standard deviations, and intercorrelations of the withinperson variables are presented in Table 1. To calculate these statistics, the values were calculated for each individual, and then these values were averaged across persons. Fisher z-transformations were used in calculating the average intercorrelations. It is worth noting, however, that the within-person correlations exhibited a great deal of variance. These values tended to range from ⫺1 to 1, with standard deviations ranging from .39 to .84. Given the within-person nature of the data collected, it was possible that the ratings across types and sources of information were ipsative (i.e., not independent) within a respondent. To assess whether this was the case, we calculated the intercorrelations among the ratings of the five types and among the ratings of the five sources. All of the intercorrelations were positive. We can therefore conclude that the ratings were independent. Had the measures been ipsative, the intercorrelations would have been negative (i.e., rating one source as high on expertise would mean that other sources would be rated as relatively lower). Hypotheses 1 and 2 referred to the relationship between information seeking and the perceived importance and difficulty of obtaining each type of information. For each variable, there were five observations per person. Table 2 presents the results of the hierarchical regression analysis when information seeking across types was used as the dependent variable. As shown, the addition of importance and difficulty added a significant increment in variance explained (p ⬍ .001). In support of Hypotheses 1 and 2, perceived importance was positively related to information seeking across the five types, ␤ ⫽ .14, p ⬍ .001, and perceived difficulty was negatively related, ␤ ⫽ ⫺.12, p ⬍ .001. Hypotheses 3 and 4 referred to the relationship between information seeking and the perceived expertise and accessibility of different sources of information. Table 2 presents the results of the hierarchical regression analysis when informa-

Table 1. Average Means, Standard Deviations, and Intercorrelations of the Within-Person Variablesa Variable 1. Importance 2. Difficulty 3. Information Seeking Across the Five Types 1. Expertise 2. Accessibility 3. Information Seeking Across the Five Sources

Average Mean

Average Std. Dev.

Types of Information 3.82 .76 2.30 .67

1

⫺.37 (.52)

3.03 .47 Sources of Information 3.66 .80 4.27 .68

.38 (.39)

2.96

.80 (.49)

a

.76

2

.35 (.49)

⫺.33 (.48)

.38 (.84)

All correlations are significant at the .001 level. Values in parentheses are standard deviations for the correlations. JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

131

Table 2 Repeated Measures Regression Results Step/Predictors 1. Persons 2. Types 3. Importance Difficulty 1. Persons 2. Sources 3. Expertise Accessibility

B

.10*** ⫺.08***

.52*** .11***

Std. Error

␤

R2

Adj. R2

Information Seeking Across Types .536 .419 .679 .597 .020 .14 .016 ⫺.12 .693 .614 Information Seeking Across Sources .335 .154 .473 .328 .041 .44 .035 .09 .572 .452

df1

df2

⌬R2

280 4

1118 1114

.536*** .143***

2

1112

.014***

281 4

1035 1031

.335*** .138***

2

1029

.098***

***p ⬍ .001.

tion seeking across sources was used as the dependent variable. As shown, the addition of expertise and accessibility accounted for an additional 10% of the variance in information seeking from different sources (p ⬍ .001). Individually, expertise had a significant positive effect, ␤ ⫽ .44, p ⬍ .001, as did accessibility, ␤ ⫽ .09, p ⬍ .01. Hence, Hypotheses 3 and 4 were supported. Hypothesis 5 predicted that need for achievement (a between-person variable) would moderate the within-person effect of perceived expertise on information seeking across sources, whereas Hypothesis 6 predicted that need for achievement would moderate the within-person effect of perceived importance on information seeking across types. HLM (Bryk & Raudenbush, 1992) was used to test these hypotheses. An initial analysis revealed that there was significant variance across persons in the regression slopes for both importance, ␹2(240) ⫽ 282, p ⬍ .05, and expertise, ␹2(245) ⫽ 319, p ⬍ .001. In other words, these variables had stronger effects for some individuals than for others, indicating that their effects could potentially be predicted by a person-level variable such as need for achievement. In contrast, there was no significant variance across persons in the regression slopes for difficulty, ␹2(240) ⫽ 273, ns, or accessibility, ␹2(245) ⫽ 250, ns, indicating that the effects of these variables on information seeking were relatively stable across persons and unlikely to depend on individual difference variables. To test Hypothesis 5, the regression coefficients for expertise (there were 282 coefficients, one for each respondent) were regressed on need for achievement, using a weighted least-squares procedure (Bryk & Raudenbush, 1992). As a control, the constant terms (i.e., intercepts) from the 282 regression equations were also regressed on need for achievement. Need for achievement was significantly related to the expertise coefficients, ␥ ⫽ .08, SE ⫽ .040, p ⬍ .05, indicating that the effect of expertise on information seeking was greater for individuals with high need for achievement than for those with low need for achievement. Hence, Hypothesis 5 was supported. The analysis also revealed that JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

132

E.W. MORRISON AND J.B. VANCOUVER

there was additional variance in the expertise coefficients that could be explained by other person-level variables, ␹2(244) ⫽ 321, p ⬍ .001. To test Hypothesis 6, the regression coefficients for perceived importance were regressed on need for achievement. The intercepts from the regression equations were also regressed on need for achievement as a necessary control. The regression coefficients for perceived importance were not significantly related to need for achievement (␥ ⫽ .09, SE ⫽ .050, ns), meaning that the effect of perceived importance on information seeking did not vary according to an individual’s need for achievement. Hypothesis 6 was, therefore, not supported. Discussion The results of this study demonstrate that individuals seek varying amounts of different information types, based on their perceptions of the importance of those types of information and their perceptions of the difficulty of obtaining them. The results also demonstrate that individuals seek varying amounts of information from different sources, based on their perceptions of those sources’ expertise and accessibility. These results significantly broaden our understanding of information seeking. Past research has provided insight into why some individuals seek more information than others. This research, by contrast, provides insight into the broader issue of how people seek information. In so doing, it helps us to better understand how an employee tailors his or her information-seeking activities to the specifics of his or her information environment. Further, by studying within-person patterns of information seeking across multiple information types and sources, this study contributes to a more multidimensional view of the process than has typically been provided. A few studies have assessed multiple types and sources of information (Morrison, 1993b; Ostroff & Kozlowski, 1992). That research, however, has been primarily descriptive, informing us of which types of information are sought the most and which types are relied upon the most. It has not addressed why individuals seek some types more than other types or use some sources more than other sources. In concluding her study of newcomer information seeking, Morrison (1993b) noted that “like other studies in this area. . .[this study] did not provide a direct test of why newcomers sought information in the ways that they did. It will be necessary for future research to build on this study and focus more directly on issues such as social cost, perceived quality of information, and personality variables that lead newcomers to prefer certain tactics and sources to others” (Morrison, 1993b: 584). This study is the first to address this gap in the literature. The results of this study also demonstrate that the overarching cost-benefit paradigm at the foundation of much of the existing between-person research on feedback-seeking behavior is relevant to understanding within-person choices regarding how to seek information. Nonetheless, at the level of specific variables, the results of this within-person investigation diverged in some cases from related results at the between-person level. For example, Ashford (1986) failed to find a relationship between the overall frequency of feedback seeking and perceived effort costs. In this study, we found that perceived effort costs do affect the JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

133

amount of feedback that individuals seek, relative to other types of information. It appears that perceived effort costs may not explain why one person seeks more information than another person, but they may explain why a given person seeks more of one type of information than another type. It is also worth noting that this study replicates the laboratory findings of Vancouver and Morrison (1995) regarding the impact of perceived source expertise and accessibility. Indeed, the effect sizes for those two independent variables were almost identical for the two studies. The average regression coefficient for expertise in the Vancouver and Morrison study was .55, as compared to .53 in this study. Likewise, the average coefficient for accessibility was .10 in the Vancouver and Morrison study and .11 in this study. Although these two studies differed from one another in many ways, taken together they support the robustness of the effects of perceived expertise and accessibility across both laboratory and field contexts (cf. Locke, 1986). The results of this study also demonstrate that, at a between-person level, need for achievement moderates the relationship between perceptions of source expertise and information seeking. In other words, variations in perceived expertise across the five sources were more closely related to information seeking for individuals high on need for achievement than for individuals low on need for achievement. Similar results were found by Vancouver and Morrison (1995). Together, the two studies provide evidence that individuals with high need for achievement are better able to adapt their information-seeking tactics according to the perceived expertise of available sources. On the other hand, the results did not support the prediction that need for achievement would moderate the relationship between perceptions of importance and information seeking across the five types. Even though need for achievement moderated the effect of information seeking across sources, there was additional between-person variance that could potentially be explained by other person-level variables. We did not engage in any systematic attempt to investigate the source of this variance, yet we believe that an important direction for future research is to go beyond this study by more fully investigating both within- and between-person effects within the same study. In particular, it would be useful for future research to investigate individual difference variables, other than need for achievement, which might moderate various within-person relationships. Variables that might be investigated include selfesteem (Vancouver & Morrison, 1995), tolerance for ambiguity (Ashford & Cummings, 1985), and public self-consciousness (Levy et al., 1995). Limitations There are some limitations that must be kept in mind when interpreting the results of this study. Although a within-person approach reduces the threat of response sets, all of the data were collected from a single source. As a result, some covariance may reflect respondents’ implicit theories of association. On the other hand, the large number of ratings was likely to have mitigated this possibility. Another factor that may have mitigated consistency effects is that, wherever possible, different formats were used to assess independent and dependent variables. Whereas the matrix used to assess information seeking had rows correJOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

134

E.W. MORRISON AND J.B. VANCOUVER

sponding to sources and columns corresponding to types, the matrix used to assess expertise had the opposite format. Nonetheless, these procedures may not have completely eliminated spurious covariance. The cross-sectional nature of the data is also a limitation. First, it did not enable us to capture changes over time, and in particular, the fact that information obtained at one point in time might impact one’s motivation to obtain subsequent information (Ashford & Cummings, 1983). Second, the cross-sectional data do not enable us to draw firm conclusions about direction of causality. It is possible, for example, that perceptions about the importance of a particular type of information were influenced by whether the individual had sought that type of information. Although our results are consistent with existing theory, longitudinal data are necessary to tease apart what are likely to be reciprocal relationships between information seeking and perceptions of costs and benefits. It is noteworthy, however, that the results related to seeking across sources replicated results found in the laboratory, where causality could be established. The lab results from Vancouver and Morrison (1995) provide convincing evidence that variance in expertise and accessibility has causal effects on information seeking. Another limitation of this study is that some of the information-seeking scales had low internal-consistency reliabilities when items were collapsed across sources. In retrospect, it is actually not surprising that there were weak relationships between the items related to different sources. For example, if an individual obtains a great deal of role information from his or her colleagues, there is less need for him or her to obtain role information from a supervisor. Thus, we should not expect a high positive correlation between the amount of information obtained from one source and the amount obtained from another. Morrison (1993a) made a similar argument in her study of newcomer information seeking. Practical Implications and Future Research The importance of understanding information-seeking behavior increases as many jobs become more complex and employees are encouraged to be more autonomous (Vancouver & Morrison, 1995). This study suggests that organizations can expect employees to seek information that the employees view as not only important, but also easy to obtain. Hence, it is in an organization’s interest to make sure that there is a correspondence between what management views as important information and what employees view as important. Further, organizations should ensure that important information is easy for employees to locate. At the same time, this study also suggests that employees will focus more heavily on expertise than on accessibility when selecting from among different information sources. Hence, it is important that employees have access to knowledgeable sources. Indeed, if an organization must make a trade-off between source expertise and source accessibility, our results suggest that priority be given to expertise. For example, if an organization wants employees to direct certain inquiries to a centralized technical support staff, it would be preferable for there to be one or two highly competent support personnel who may not always be readily available than for there to be a large (and hence more accessible) staff of individuals with JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

135

somewhat less expertise. Our results suggest that employees are more concerned with finding knowledgeable sources than with minimizing effort costs. Another practical implication relates to our findings regarding need for achievement. As noted, the results suggest that the higher one’s need for achievement, the more responsive one is to variations in source expertise, and hence, the more capable one is of obtaining information that will help one to succeed. These findings underscore the importance of organizations hiring individuals with high need for achievement, and training employees to regard work as an opportunity for achievement (Cherrington, 1989). Although this study investigated multiple types of information and multiple sources, it is important to recognize that it did not cover the full range of the typical employee’s information environment. In particular, it did not consider self-generated information, which is often emphasized within the feedback literature (Herold & Parsons, 1985). We suspect that some individuals may have a preference for internally generated feedback, and may therefore rely less upon sources such as supervisors and peers. It is also important to recognize that this study did not consider all of the ways in which information seeking might have benefits, nor all of the ways in which it might be costly. For example, we did not consider the interpersonal or impression management risks that may deter information seeking (Ashford & Cummings, 1983; Morrison & Bies, 1991), focusing instead on effort costs (cf. Ashford, 1986). Lastly, it is important to recognize that, in this study, we focused on just one of the many types of activities in which employees engage (i.e., information seeking). Employees, however, make numerous tradeoffs when deciding how to allocate their time and energy. Assessing the properties associated with the various options available to employees should help researchers to further understand the allocation of effort toward information seeking. For example, it is possible that an employee who regards another activity (e.g., politicking) as more important than uncertainty reduction will allocate less energy toward information seeking than toward political behaviors. To fully understand the motivation to seek information, therefore, researchers must also understand the other goals and options facing the individual. This is a direction that we regard as especially promising for future research. Acknowledgments: We are extremely grateful to Thomas E. Pinelli of the National Aeronautics and Space Administration, and to the American Institute of Aeronautics and Astronautics, for facilitating the data collection for this study. We also thank Susan Ashford, Ronda Callister, Bruce Carlson, Don VandeWalle, and two anonymous reviewers for their helpful comments and suggestions. References Allen, T. E. 1977. Managing the flow of technology: Technology transfer and the dissemination of technological information within the R&D organization. Cambridge, MA: MIT Press. Ang, S., Cummings, L. L., Straub, D. W., & Earley, P. C. 1993. The effects of information technology and the perceived mood of the feedback giver on feedback seeking. Information Systems Research, 4: 240 –261. Ashford, S. J. 1986. The role of feedback seeking in individual adaptation: A resource perspective. Academy of Management Journal, 29: 465– 487. JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

136

E.W. MORRISON AND J.B. VANCOUVER

Ashford, S. J., & Black, J. S. 1996. Proactivity during organizational entry: The role of desire for control. Journal of Applied Psychology, 81: 199 –214. Ashford, S. J., & Cummings, L. L. 1983. Feedback as an individual resource: Personal strategies of creating information. Organizational Behavior and Human Performance, 32: 370 –398. Ashford, S. J., & Cummings, L. L. 1985. Proactive feedback seeking: The instrumental use of the information environment. Journal of Organizational Psychology, 58: 67–79. Ashford, S. J., & Northcraft, G. B. 1992. Conveying more (or less) than we realize: The role of impressionmanagement in feedback-seeking. Organizational Behavior and Human Decision Processes, 53: 310 –334. Ashford, S. J., & Tsui, A. S. 1991. Self-regulation for managerial effectiveness: The role of active feedback seeking. Academy of Management Journal, 34: 251–280. Berger, C. R. 1979. Beyond initial understanding: Uncertainty, understanding, and the development of interpersonal relationships. In H. Giles & R. N. St. Clair (Eds.), Language and social psychology: 122–144. Oxford: Basil Blackwell. Berger, C. R., & Calabrese, R. J. 1975. Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication. Human Communication Research, 1: 99 –112. Berlyne, D. E. 1960. Conflict, arousal, and curiosity. New York, NY: McGraw-Hill. Brett, J. M., Feldman, D. C., & Weingart, L. R. 1990. Feedback-seeking behavior of new hires and job changers. Journal of Management, 16: 737–749. Bryk, A. S., & Raudenbush, S. W. 1992. Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage. Cherrington, D. J. 1989. Organizational behavior. Needham Heights, MA: Allyn Bacon. Cohen, J., & Cohen, P. 1983. Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum. Fedor, D. B., Eder, R. W., & Buckley, M. R. 1989. The contributory effects of supervisor intentions on subordinate feedback responses. Organizational Behavior and Human Decision Processes, 44: 396 – 414. Fedor, D. B., Rensvold, R. B., & Adams, S.M. 1992. An investigation of factors expected to affect feedback seeking: A longitudinal field study. Personnel Psychology, 45: 779 – 805. Gerstberger, P. G., & Allen, T. J. 1968. Criteria used by research and development engineers in the selection of an information source. Journal of Applied Psychology, 52: 272–279. Gundry, L. K. 1993. Fitting into technical organizations: The socialization of newcomer engineers. Transactions on Engineering Management, 40: 335–345. Halish, F., & Heckhausen, H. 1977. Search for feedback information and effort regulation during task performance. Journal of Personality and Social Psychology, 35: 724 –733. Herold, D. M., & Parsons, C. K. 1985. Assessing the feedback environment in work organizations: Development of the Job Feedback Survey. Journal of Applied Psychology, 70: 290 –305. Holder, T. 1996. Women in nontraditional occupations: Information seeking during organizational entry. Journal of Business Communication, 33: 9 –26. Hofmann, D. A. 1997. An overview of the logic and rationale of hierarchical linear models. Journal of Management, 23: 723–744. Ilgen, D. R., Fisher, C. D., & Taylor, S. M. 1979. Consequences of individual feedback on behavior in organizations. Journal of Applied Psychology, 64: 349 –371. Kanfer, R. 1990. Motivation theory in I/O psychology. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (2nd ed.), Vol. 1: 75–170. Palo Alto, CA: Consulting Psychological Press, Inc. Lee, D. M. 1992. Job challenge, work effort, and job performance of young engineers: A causal analysis. IEEE Transactions in Engineering Management, 39: 214 –226. Lee, D. M., & Allen, T. J. 1982. Integrating new technical staff: Implications for acquiring new technology. Management Science, 28: 1405–1420. Levy, P. E., Albright, M. D., Cawley, B. D., & Williams, J. R. 1995. Situational and individual determinants of feedback seeking: A closer look at the process. Organizational Behavior and Human Decision Processes, 62: 23–37. Locke, E. A. 1986. Generalizing from laboratory to field settings. Lexington, MA: Lexington. Mignerey, J. T., Rubin, R. B., & Gordon, W. I. 1995. Organizational entry: An investigation of newcomer communication behavior and uncertainty. Communication Research, 22: 54 – 85. Miller, V. D., & Jablin, F. M. 1991. Information seeking during organizational entry: Influences, tactics, and a model of the process. Academy of Management Review, 16: 92–120. Mitchell, T. R. 1974. Expectancy models of satisfaction, occupational preference and effort: A theoretical, methodological and empirical appraisal. Psychological Bulletin, 81: 1053–1077. Morrison, E. W. 1993a. A longitudinal study of the effects of information seeking on newcomer socialization. Journal of Applied Psychology, 78: 173–183. JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000

WITHIN-PERSON ANALYSIS OF INFORMATION SEEKING

137

Morrison, E. W. 1993b. A longitudinal study of newcomer information seeking: exploring types, modes, sources, and outcomes. Academy of Management Journal, 36: 557–589. Morrison, E. W., & Bies, R. J. 1991. Impression management in the feedback seeking process: A literature review and research agenda. Academy of Management Review, 16: 522–541. Morrison, E. W., & Cummings, L. L. 1992. The impact of feedback diagnosticity and performance expectations on feedback seeing behavior. Human Performance, 5: 251–264. Morrison, E. W., & Weldon, E. 1990. The impact of an assigned performance goal on feedback seeking behavior. Human Performance, 3: 37–50. Northcraft, G. B., & Ashford, S. J. 1990. The preservation of self in everyday life: The effects of performance expectations and feedback context on feedback inquiry. Organizational Behavior and Human Decision Processes, 47: 42– 65. O’Reilly, C. A. 1982. Variations in decision makers’ use of information sources: The impact of quality and accessibility of information. Academy of Management Journal, 25: 756 –771. Ostroff, C., & Kozlowski, W. J. 1992. Organizational socialization as a learning process: The role of information acquisition. Personnel Psychology, 45: 849 – 874. Pinelli, T. E., Bishop, A. P., Barclay, R. O., & Kennedy, J. M. 1993. The information seeking behavior of engineers. Encyclopedia of Library and Information Science, 52: 167–201. Rosenbloom, R. S., & Wolek, F. W. 1970. Technology and information transfer: A survey of practice in industrial organizations. Boston, MA: Harvard University Press. Steers, R. M., & Braunstein, D. N. 1976. A behaviorally based measure of manifest needs in work settings. Journal of Vocational Behavior, 9: 251–266. Stevenson, M. K., Busemeyer, J. R., & Naylor, J. C. 1990. Judgment and decision-making theory. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology, Vol 1: 283–374. New York: Wiley. Trope, Y. 1975. Seeking information about one’s ability as a determinant of choice among tasks. Journal of Personality and Social Psychology, 32: 1004 –1013. Van Eerde, W., & Thierry, H. 1996. Vroom’s expectancy models and work-related criteria: A meta-analysis. Journal of Applied Psychology, 81: 575–586. Vancouver, J. B. 1997. The application of HLM to the analysis of the dynamic interaction of environment, person and behavior. Journal of Management, 23: 795– 818. Vancouver, J. B., Millsap, R., & Peters, P. A. 1994. Multilevel analysis of organization goal congruence. Journal of Applied Psychology, 79: 666 – 679. Vancouver, J. B., & Morrison, E. W. 1995. Feedback inquiry: The effect of source attributes and individual differences. Organizational Behavior and Human Decision Processes, 62: 276 –285. VandeWalle, D., & Cummings, L. L. 1997. A test of the influence of goal orientation on the feedback-seeking process. Journal of Applied Psychology, 82: 390 – 400. Vroom, V. H. 1964. Work and motivation. New York: Wiley. Young, J. F., & Harriot, L. C. 1979. The changing technical life of engineers. Mechanical Engineering, 101: 20 –24.

JOURNAL OF MANAGEMENT, VOL. 26, NO. 1, 2000