Sources of social structure in a start-up organization: work networks, work activities, and job status

Social Networks North-Holland

191

16 (1994) 191-212

Sources of social structure in a start-up organization: work networks, work activities, and job status * Leo F. Brajkovich

1

School of Social Sciences, University of California, Iwine, CA 92717, USA

This paper explores possible salient dimensions of social structure in a start-up organizational setting. In particular, the relationship between judged similarity and three types of data representing structure in a small entrepreneurial medical technology firm is examined. Data concerning unconstrained judged similarity, work networks, work activities, and job status was collected within a month of the organization’s initial start-up utilizing ethnographic, systematic and network methods of data collection. Data was analyzed using hierarchical clustering, multidimensional scaling, and correspondence analysis to determine the structure of the organization according to each type of data collected. Measures of similarity were computed to determine the correspondence between the various organizational structures and the patterns of judged similarity of organizational actors. The multiplexity of the organizational structures was also assessed. Findings indicate that patterns of judged similarity correspond equally highly to each of the organizational structures, and that the structures are multiplex in only one case; work networks and work activities. Also, a high degree of consensus exists among the actors concerning their perceptions of organizational structure. Implications for research on social cognition, the process of innovation, and the growth of entrepreneurial organizations are discussed.

1. Introduction

“In other social network contexts, different types of behaviors would be expected to have the most explanatory power vis-h-vis ’ Present address: International Survey Research Corporation, 303 East Ohio Street, Chicago, IL 60611, USA. e-mail: [email protected]. * John Boyd, Xeronimo Kirk, and Devon Brewer gave helpful comments on an earlier draft of this paper. This work was aided by a Grant-in-Aid of Research awarded to the author from Sigma Xi, The Scientific Research Society. 0378-8733/94/$07.00 0 1994 - Elsevier SSDI 0378-8733(93)00237-Q

Science

B.V. All rights reserved

judged-similarity data. Our theoretical point is that cognitive data reflects an averaging of the salient dimensions of behavior and culture. The identification of these dimensions is an empirical question” (Johnson and Miller, 1986). The purpose of this paper is to discover some of the dimensions of behavior and cognition that are salient to the actors in a emerging organization. The organizational actors in an emerging organization arc in a situation of changing, evolving, and developing priorities, activities, and relationships. To understand what aspects of the situation individuals use to determine the structure of that social situation, is of great interest to entrepreneurs and managers, but also to the body of research devoted towards understanding social cognition and the development of innovation. The multiplexity of links, or the ability for a link to reprcscnt multiple social relations, is also an important aspect to understanding perceptions of social structure. To what extent do relations overlap within a social situation or context? Among salmon fishermen in Alaska, it was found that cognitive data concerning similarity was related to fishermen’s patterns of co-residence and economic exchange, and that the relations were not multiplex (Johnson and Miller, 1986). In a study of a college administrative unit Boster et al. (1987) found that job status, and personal advice networks to a lesser extent, were closely related to perceptions of similarity, and that personal and professional advice relations were highly multiplex. Also, recent rcstarch on friendship ties moderately supports the idea that regular equivalence is a source of structure in similarity judgements over time (Michaelson and Contractor, 1992). So for the study of social cognition in emerging organizations, the question is, what aspects of the social structure are represented in data obtained by having organizational actors judge the ‘similarity’ of other organizational actors without giving them a specific criteria to use in their judgement? It is hypothesized that in an initial start-up organizational situation, actors will USC their perceptions of work-related activities and work relationships to guide their perceptions of similarity and social structure. This idea is based on earlier findings, indicating informant’s reports of primary group structure were consistent with observable patterns of interaction (Sailer and Gaulin, 1984; Freeman et al., 1988, 1989). Also under consideration is the extent to which formal structure or job status influences actors’ judgements of

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similarity. In a start-up situation job titles are usually less formal, and communication in general is informal. But does that mean that organizational actors are not aware of the hierarchy associated with various positions? Although they may be informal, start-ups are rarely democratic (Collins, 1988). If the organizational actors are trying to assess the political landscape of the organization, recent research of a more ‘mature’ and larger entrepreneurial firm indicates that the formal positions occupied by actors is something the organizational actors are cognizant of (Krackhardt, 1990). How much does the initial formal hierarchy structure influence similarity judgements is another question to be examined by this research. In this paper we will compare data that represents the unconstrained judged similarity among actors in a start-up firm with data that represents the work relationships, work activities, and job status of those actors. By comparing and analyzing these data, we hope to discover some of the dimensions those actors use to understand their social situation and its structure. We also assess how multiplex these various structures of the organization are. The implications of the findings toward research on organizational foundings and growth and social cognition will be discussed.

2. Data 2.1. Case of start-up medical technology firm The organization that was the source of data for this research is best described as a small, start-up medical technology firm that was spun off from a much larger more established organization. The organization consists of 15 individuals, 11 men and four women. The study of this firm commenced within a month of its ‘start’ and research is continuing at the present time. 2.2. Unconstrained judged similarity Each organizational actor completed an unconstrained judged similarity task. The task consisted of a triads questionnaire utilizing a balanced incomplete block design (BIB) with each pair of actors’ names appearing twice (lambda = 2). Each actor chose the name of

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the actor that was most different from the other two for 70 sets of triads. This technique has been shown to provide stable results for less than 20 items for comparison, and has been used in a variety of settings (Burton and Nerlove, 1976). Each actor’s responses were converted to matrices that will be referred to as the similarity data. 2.3. Work network

questionnaire

Each organization actor filled out a network questionnaire in which they were asked for each organization actor, to circle the name of every other actor they thought worked closely with the original actor named. The answer to this question was hoped to be the main work ties each organization actor has. Also, the ‘works closely with’ relation was asked because it was reasoned that asking something like ‘who do you have important work communication with’ would be hampered by heterogenous definitions of importance. We also wanted the work network to reflect the individuals’ most fundamental work ties, and ‘works closely with’ will get those ties. Organizational actors are much more likely to report accurately who they ‘work closely with’ than what specifically they have talked about with someone. The data collected included not just the actor’s own network, but also their perception of every other actor’s ‘works closely with’ network. This type of data, referred to as Cognitive Social Structure (CSS) data, allows us to build the network out of each actor’s perception of the entire organization (Krackhardt, 1987). ’ The CSS is a three-dimensional array of linkages, Ri,j,k, among a set of N actors, where i is the sender of the relation, j is the receiver of the relation, and k is the perceiver of the relation. The actors’ responses were converted to matrices that will be referred to as the work data. 2.4. Orgunizational

acticities

In the process of interviewing the organizational actors, each was asked to name and describe the functions, responsibilities, and activities of each organization member as well as themselves. The descriptions and statements made during pilot interviews were utilized later ’ The word cognitive here is somewhat troublesome. The social structure does not exist inside an actor’s head, only their perception of it does. The term ‘cognized social structure’ is a more accurate description of what the data represents.

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on to clarify responses and categories of activities. The responses to the questions concerning each actor’s responsibilities and activities was converted into actor by activity matrices that will be referred to as the activity data. 2.5. Job status The status similarities matrix was created by first assigning a status rank to each actor based on status terms mentioned in descriptions of each actor during interviews. Status ranks of 1 (for the president), 2 (for managers/VP& 3 (for the head engineer), 4 (for the engineers), and 5 (for the technicians) were assigned. Status similarities were measured as the difference between the maximum difference in status rank and the absolute value of the difference in status rank (Boster et al., 1987; Brajkovich, 1991). For example, the status similarity between the president and an engineer would be computed as (4 - 11 - 4 )> = 1, the status similarity between an engineer and a technician would be computed as (4 - 14 - 5 I> = 3, etc.

3. Analysis and results 3.1. Ethnographic description The organization can best be described as really two groups, with a few liaisons, each group with a different purpose and a delicate balance between them. One group consists of the engineers and technicians who are responsible for developing the innovative product technology. The other group is concerned with the ‘business’ end of the organization. The technical group is led by a technical ‘guru’ who is the brains behind the product technology and also did the early market feasibility research for the proposed product. The rest of the technical group is made up of individuals with a variety of technical specialties including: optics, semiconductors, mechanical design, clean room operations, electronic test equipment, and medical device technology. The business group is led by the ‘president’, who is the one most responsible for the start-up of the firm. The rest of the business group consists of individuals with experience in various aspects of business management and operations including: sales and marketing,

clinical testing of medical technologies, regulatory procedures, facilities operation, venture capital, and quality control. The activities described and named by everyone involved were electronics (ele), assembly (asm), engineering (eng), manufacturing (mfg), facilities (fat), marketing (mkt), clinical testing (cts), quality assurance/ regulatory assurance (qara), venture capital (vc), planning (pin), interface-to-other businesses (iob), finance (fin), management (mgt), and administration (adm). 3.2. Similarity structure In an attempt to understand how organization actors perceive themselves, others, and perhaps the structure of their organization, the similarity data was summed for all actors and put into an aggregate 15 X 15 similarity matrix. This matrix was then analyzed using a non-metric Multi-Dimensional Scaling (MDS) program 2 to help represent the underlying cognitive or semantic structure of the perceptions of the organization actors (Romney et al., 1972). The aggregate similarities were also analyzed using a hierarchical clustering program to help determine grouping of organization actors based on judged similarities (Johnson, 1967). ’ The MDS plot of the aggregate similarities with the results of the clustering analysis arc presented in Fig. I. The clusters are displayed in Table 1. The clusters are named with terms used by the actors themselves in describing each other concerning a variety of topics (Spradley, 1979). In the triads test actors apparently based their judgements of the similarity of actors principally on the basis of the relative involvement in the technical development of the product, with a twist. The two clusters on the left-hand side of the plot are the technically oriented members of the organization and the one on the right are the business-oriented members. The cluster of actors in the upper left of the plot are the engineers (1, 2, 3, 5, 6, 7). These are the actors most closely related to the design and development of the product technology. Within this engineering cluster, the grouping together of actors (1, 5, 7) and (2, 3, 61 ’ All scaling (MDS and CA) and hierarchical clustering were software package by Steve Borgatti (Borgatti, 1989). ’ The clusters were determined using the average-link method.

done

using

the ANTHROPAC

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1.5 r 1

ENGINEERS

1..

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5

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BUSINESS/ADMIN

7

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10

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Fig. 1. MDS of aggregate

;

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similarities

Llnk Method Crlterla DIsplayed)

showing

clusters

might represent a further differentiation within technology development. Actors (1, 5, 7) were described during the interviews as being the ‘technical specialists’, having very detailed knowledge about certain areas of the technology. The guru (11, in fact, was described as ‘knowing a lot about a lot’. On the other hand, actors (3, 2, 6) were generalists or ‘generally technically knowledgeable’.

Table 1 Clusters of actors

by judged

similarity

Clusters

Actors

1. Engineers 2. Technicians 3. Business/Management

1, 2, 3, 5, 6 7 4, 8, 13 9, 10, 11, 12, 14, 15

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The twist in this technical differentiation is the second cluster in the lower part of the plot. Actors (4, 13, 8) are very technically oriented, but in a different way than the engineers. Actors (4, 13, 8) are the lab technicians involved in the ‘hands-on’ assembly and testing of the pieces of the product technology. The position of the technicians in the plot, roughly equidistant from the other two clusters suggest that the actors are making some sort of status or hierarchy distinction between management, professional, and support staff. This distinction of organizational actors according to status in an unconstrained judged similarity task is supported by research on social position and shared knowledge by Boster et al. (1987). The third cluster on the right-hand side of the plot contains all the actors involved with the business (non-technical) activities. Actors (9, 15, 14) are involved in a lot of the management, finance, and regulatory activities, (10, 14) do marketing and sales work, and actor 12 is everyone’s administrative assistant. One interesting result is that the guru (1) and the president (9), are not set off or separated from the others in any particular way. This is interesting since in the interviews those two actors were always distinct and usually referred by names such as ‘the brains’, ‘the innovator’, or ‘the entrepreneur’. 3.3. Work actir;ities structure To try and understand the structure of the various organization activities, and how those actors performing those activities are related, the actor by activity matrices were summed into a 15 X 14 aggregate activities matrix and input into a correspondence analysis program. Correspondence analysis (CA) represents row and column entities of a contingency table in the same geometrical space (Weller and Romney, 1990). This means that we can examine relations among actors or activities, but also between actors and activities. CA can be used as a discrete analysis for grouping together equivalent rows and equivalent columns (i.e. deciding which levels of the row (col) variables are similar with respect to the column (row) variable) (Wasserman et al., 1989). The CA two-dimensional plot of the aggregate activities matrix is presented in Figs. 2 and 3, and the resulting singular values and goodness-of-fit results are in Table 2. From the results of the CA in Table 2, we can see that the fit

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5.0 0

r adm 12

4 &C eassm 2 3 engr mfgr 1

6 fat mkg 11 10 ctsf g iob qaraplngvcap -1.1; r

__

15 5.00

-1.17

Fig. 2. Structure

of aggregate

actor-activity

data in a two-dimensional

plot.

between the CA representation and the data is a good one. The correlation between the expected values from the reconstruction and the data in 2 and 3 dimensions is 0.752 and 0.824, respectively. These representations account for 57% and 68% of the association in the data, respectively. From the two-dimensional plot we can explore the relationship between the actors, between the activities, and between the actors and activities. The first noticeable feature is that actor 12 (the secretary) is the only one who does administrative support work. This large distance between her and her work and the rest of the actors makes it necessary to enlarge the area of the plot where the rest of the actors and activities are, to see the structure clearly. In the enlarged plot of Fig. 3, we can immediately see the technical involvement dimension present, similar to the judged similarity results. One major feature is

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4 7ele 8asm 2 3

6 fat IO

iob

cts g

wa 15 Fig. 3. Structure

11

mkt

p,;

14--

of aggregate

actor-activity

data in a two-dimensional

plot.

the intermixing of the technicians and the engineers among activities as we move down the plot upper left to lower center. Involved with electrical equipment design and testing, respectively, are actors 7 (cng) and 4 (tech). Actors (4, 8, 13) are the technicians who all do assembly work, hence its central location in the plot between all three of them. Actors (1, 6, 10) are all involved in activities (facilities, marketing, and technical management) that require interfacing with

Table 2 Results of CA showing

goodness-of-fit

Factor

Value

% PRE

Cum PRE

Corr (r)

% Var

1 2 3 4 s 6 7

0.880 0.815 0.606 0.55 1 0.464 0.399 0.270

29.6 25.4 14.0 11.6 x.2 6.1 2.8

29.6 55.0 69.1 80.6 88.9 94.9 97.7

0.570 0.752 0.824 0.886 0.938 0.974 0.986

0.325 0.566 0.679 0.785 0.879 0.948 0.073

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the business actors. Also involved in marketing are actors (1, 10, 9, 111, hence its location between those actors. The president, actor 9 is the one most involved in raising venture capital, planning and interfacing with other businesses. Regulatory issues are activities handled by actors 15 and 14. Overall, the CA plot shows a division in the organizational activities with the more technical activities above the guru (11, and the more business management activities below actor 6. This split can be thought of as representing a group of core technology activities and a group of core business activities (Thompson, 1967). The guru is placed between the technical core and the business core since he does have considerable responsibilities supporting the marketing and business planning activities. The president (9) is in the middle of the business core and has very little involvement with the technical activities beyond managing their timely completion. It is clear from this analysis of the actor activity structure that the guru, the engineers, and technicians form a technological core involved in the development of the innovative product with no involvement in the management or business activities. Also clear is that the president and other business actors form a business core, responsible for the management, marketing, raising of capital, and organizing of the firm with no involvement in the technology of the innovative product. There are actors who are in liaison roles (1, 6, 9, 101, but to different degrees. In order to compare the organizations activity structure to the judged similarities, the work structures, and the job status hierarchy structure, an activities structure similarity matrix was constructed by computing the correlation between each and every actor’s row in the aggregate actor-activities matrix, and entering the correlation coefficient in a matrix cell representing the similarity between two actors regarding their work activities. The result is a 15 x 15 aggregate work similarities matrix. The MDS plot of this aggregate activities similarity matrix is shown in Fig. 4. The overall clustering of actors based on similar activities is contained in Table 3. 3.4. Work network structure The organization naire were summed

actors responses to the work network questionand put into an aggregate 15 X 15 work matrix.

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ADMINISTRATIVE

12 I-ECHNICAL

7

11 1

4

9

2 5

10

3

14 15

8

13

6 BUSINESS/MANAGEMENT

Fig. 4. MDS of similarities

based on work activities.

For a first look at the network, the matrix was dichotomized in the following manner: if a cell had 50% or more of the actors perceiving a link between that dyad it was made a one, else a zero. The proportion of ties reported above this threshold is considerably larger than the proportion reported below this level that were non-zero. In fact a large number of links (20) were reported by 80% of the organization

Table 3 Clusters of actors

by similar

organizational

activities

Clusters

Actors

1. Technical 2. Business/Management 3. Administrative

I, 2, 3, 4, 5, 7, 8, 13 6, 9, 10, 11, 14, 15 12

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1.5

i 1

0.5

0

-0.5

-1

-1.5

L

-1.5

-1

-0.5

0

0.5

1

1.5

Fig. 5. Work network.

actors including ten links at the 100% level. This is not particularly surprising given the small number of actors involved and the physical proximity within which most of them work. This is why the 50% threshold is a very liberal criterion for considering a link established, which is a good starting point for preliminary analysis of the network (Krackhardt, 1987). A sociogram depicting the work network (links drawn at threshold 2 50%) was made using an MDS of the aggregate work network and is presented in Fig. 5. Some interesting aspects of the work structure are the overlapping clique-like structure of the technical core (1, 2, 4, 13, 5, 6) with (1, 2, 5, 6, lo), the clique-like structure of the business core (9, 11, 12, 15), connected by liaisons (mainly, 1 and 9 but also 6, 10, 11) to the technical core. A key distinction between the liaisons is that actors 6 and 10 are not as numerously linked to either the technical or

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Table

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Networks

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4

Clusters

of actors hy work

linkages

Clusters

Actors

I. Technical

I <.2. 3. 4. 5 ‘I, 6 h. 7.

2. Business/Management

9 1’. IO i. II $1.12, 14. IS

j’ Denotes

actors with

one link

” Denotes

actors with

two links

‘ Denotes

actors with

three

between

x, 13

clusters.

between

links between

clusters. clusters

business cores as actor 9 or 1. From the CA we know they do not participate in any core activities besides being involved in the management/planning aspects. Actors 8 and 7 are connected to only one other actor, 3 and 1, respectively. Actors 8 and 3 work mainly in a separate facility from the rest, and actor 7 is a consultant who is only responsible for electronics design and coordinates that with the guru (1). The clusters of actors based on the overall work network are presented in Table 4. 3.5. Reliability of organizational

structures

One implicit assumption of this research that has not been validated yet is the use of ‘overall’ or aggregate representations of the various organizational structures. In a small start-up organizational situation, it was assumed that there would be consensus concerning the work network and work activities but this reasoning does not apply to the judged similarities. Whether or not there is consensus among the actors concerning their judgements of similarity is an empirical question. The question that remains then, is how much consensus among the actors is there in their reports of the work network, the work activities, and the judged similarities? To assess this assumption of consensus, or reliability, Cronbach’s (Y was calculated for each of the three organizational structures represented by actors’ reports. Job status was excluded since the status hierarchy was constructed after the interviews from descriptions of the various job titles. Job status might be thought of here as ‘eventual’ or future job status. Cronbach’s cy is an overall measure of agreement on a variable of interest, in this case organizational structures. The

L.F. BrajkoCch

formula 44):

for Cronbach’s

/ Social Networks

a is as follows

16 (1994) 191-212

(Carmines

and Zeller,

205

1979:

NP (y= l+p(N-1) where N is the number of structures reported, one for each organizational actor (in this case 151, and p is the average interrater correlation, or agreement, between all individual pairs of the 15 organizational actors. The (Y is high (close to 1) to the extent that everyone agrees on the structures and linkages that exist in the organizations (i.e. ‘who works closely with whom?‘, ‘what work activities does perform?‘, and ‘who is similar to whom?‘). All three measures of organizational structure had high reliability coefficients (Cronbach’s (Y= 0.92 for similarities, (Y= 0.96 for work network, and (Y= 0.97 for work activities), demonstrating that there was very high consensus in the organization regarding the various perceptions of structure. Given the high degree of consensus, it seems reasonable to interpret the aggregate data as representative of the overall organizational structure, and representative of the perceptions of the organizational actors as a whole. 3.5. Comparison

t3fstructures

To examine the similarity of the various organizational structures to the judged similarity results, and to examine the multiplexity of each of them, all aggregate data sets were compared to each other using two measures of similarity (Pearson’s Y and Quadratic Assignment Procedure (QAP)). The QAP answers the question of whether two N X N matrices representing similarities or connections between row and column entities are similar to each other beyond a level one would have expected through chance arrangement (Hubert and Schultz, 1976; Hubert, 1987). Significance levels are calculated by generating a distribution of all possible outcomes (Monte Carlo), given the structure of each matrix. The actual similarity between the two matrices is compared to this reference distribution of possible similarities. The QAP yields a normalized statistic (expressed in Z scores) which would

206

Table 5 Comparison

L.F. Brajkmich

of judged

similarity,

Aggregate data

Similarity

Similarity Job status Work network Activities

X

/ Social Networks

work activities, Job status 5.99 **

16 (1994)

work networks,

191-212

and job status ”

Work network

Activities

6.23 **

6.54 * *

0.59 * *

X

2.21 *

- 0.71

0.61 ** 0.64 * *

0.23 * - 0.07

X 0.47 **

X

3.61 * *

,’ This table compares four symmetric matrices of relationships aggregated from 15 organization actors tasks (N = 105 pairs). Pearson r is presented below the diagonal, QAP z is presented above the diagonal. The single and double asterisk denote a one-tailed probability for the QAP of P 5 0.03 and P 5 0.001 respectively. The single and double asterisks are two-tailed for the Pearson r, P s 0.02 and P I 0.001 respectively.

be large to the extent that the match-up was greater than would occur by chance arrangement of similarity. The results of the comparative analysis are presented as a matrix of comparison measures in Table 5. Table 5 shows that judged similarity has a moderate correspondence with all three organizational structures; (Y = 0.59) for status, (Y = 0.61) for the work network, and (r = 0.64) for the work activities, indicating a substantial relationship. The results also indicate that the three organizational structures are not multiplex, with one exception moderately supported. The job status hierarchy does not correspond very much to the work network (Y = 0.231, and not at all to the work activities (Y = - 0.071, but the work network corresponds moderately to the activity structure (Y = 0.47). One interpretation of these results is that the organizational actors use status, work relationships, and work responsibilities approximately equally to determine similarity among each other. Also, an actor probably works quite a bit with others of varying job status, and the nature of those tasks involves a fair amount of interaction and cooperation, hence the multiplexity of activities with work networks. In an effort to understand the comparisons in terms of the actors themselves, we can examine the ‘movement’ of certain actors through the various clusters estimated from the organizational structures and the judged similarities. Comparing the judged similarities with the work network, we see that technicians (actors 4, 8, 13) are a separate cluster in the judged similarities, but are part of the technical cluster

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or core in the work network. This difference represents the influence of status on the judged similarities that has little to do with the actual work relationships within the organization. Another difference between the perceptions of structure is the membership of actor 6 in the engineering cluster of the judged similarities, in the technical cluster of the work network, and the business/management cluster of the activities structure. The various perceptions of actor 6 most likely reflect his liaison position in the organization. He worked closely with the engineers and technicians, but he did so in management capacity: to gather information related to planning and organizing the future manufacturing and facilities needs and requirements of the organization. He also had been an engineer for several years (in another firm) before coming to this start-up. It is clear then that actor 6 had the

AC

ST

SI

WN

J-1.5 Fig. 6. MDS of similarities

of organizational

structures.

20X

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activities of a manager, the work links of both a technical and management actor, and possibly the ‘lingering’ status of an engineer, all contributing to his various cluster memberships, and depressing the correlations between structures. Also interesting is the positioning of actor 12 (the secretary) as an isolate in the activity structure. She is the only one who is perceived as doing administrative work, and that is her only activity, hence she is set apart from the others in the activity structure, yet she is part of the business cluster in the judged similarity structure and the work network. Even in start-ups (or especially in start-ups), the secretary is hard for the others to classify exactly.

1.5

1

I ~~

I

0.5 I

0

1

-0.5

I

-1

-1.5

1

-1.5

‘WORKS CLOSELY WITH” RELATlON BETWEEN CLUSTERS ‘WORKS CLOSELY WITH” RELATlON WITHIN CLUSTERS PERCEIVED

Fig. 7. flypergraph where the dotted

GROUPS

FROM CLUSTERING

showing clusters from judged similarities and work relations lmes are intra-cluster links and the solid line\ are inter-cluster

between links.

actors,

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An MDS of the matrix in Table 5 is presented in Fig. 6, as a straightforward way of assessing the relative dimensions of salience to the judged similarity task (Johnson and Miller, 1986). From the configuration, similarity (SI) lies centrally intermediate between organizational status (ST), activities (AC), and work networks (WN). This is to say, job status, work activities, and close work relationships all contribute approximately equally towards shaping the judgements of similarity among the organizational actors. To help illustrate the relationship between the judged similarity structure and the work network, a hypergraph displaying the judged similarity clusters and the work network is shown in Fig. 7 (Berge, links and the dotted 1973). 4 The solid lines emphasize intercluster lines emphasize intracluster links. From the hypergraph, we can see that the engineers, for the most part, work closely with one another as well as with some of the technicians. The technicians really do not work with one another, while the business/ management actors largely do work closely with one another. We can also see that actors 1 and 9, the guru and president respectively, are central, liaisons, and core members of the work network, while 6 and 10 are liaisons but are less core members.

4. Discussion This study set out to explore and discover the salient dimensions of social cognition related to judged similarity in a start-up organization. Equally moderate correspondence to unconstrained judged similarity in this social context were: work activity (Y = 0.64, P I 0.0011, work network (0.61, PI 0.0011, and job status (0.59, PI 0.001). We also wanted to assess the amount of overlap between, or the multiplexity, of the various dimensions of social structure used in this analysis. The results indicate no overlap between work activities and job status ( - 0.07, n.s.1, weak overlap between the work network and job status (0.23, P I 0.021, and moderate overlap between the work network and work activities (0.47, P I 0.001). The multiplexity of work activities and the work network may be the result of a general social relation4 The couple H = (x, 5) is called a hypergraph, where X=(x,, x,,...,x,) is a finite set and 5 = (E, /i E I) is a family of subsets of X such that: (1) E, # $J (i E I), and (2) u It ,E, = X.

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ship between working and working with others, or it may be a result of the particular tasks in this organization. Further research on the type of activities versus the amount of overlap between activities and the work network is required to resolve the issue. It may be the case that the answer to questions like ‘Who do you work closely with?’ may overlap more or less with ‘Who has similar activities and responsibilities to yours?’ depending on the degree of cooperation or dependency on others necessary for the completion of the tasks involved. Overall, the triads task tapped the job status hierarchy, work activities, and work network aspects of the organizational structure with work network and work activities being moderately multiplex. This status finding most likely has something to do with the various experiences with job hierarchies of each actor in previous organizations. All the actors had experience in their current jobs at larger organizations. For example, all the technicians were previously technicians, and technicians are generally the lowest rung on the technical ladder. The engineers were all previously engineers, with the guru having been a ‘project scientist’. All the other were previously in management positions, mostly VPs, with the exception of actor 12 who was the president’s secretary previously. All this goes to support a notion that formal position is always a source of power in an organization, and is therefore important to know. Since everyone knows this formal structure and how everyone else fits into it, it is a likely source or criterion of comparison when judging the similarity of each other. Also supported by the findings of this study is the notion that the multidimensionality of an individual’s social context influences their judgement of similarity multidimensionally. Which dimension(s) will influence a particular kind of cognitive data in a particular setting is not clear. Since judged similarity is used as a measure of social structure in a variety of social settings it is an important topic for future research on social perception and cognition. The structures used in this study were thought to be possible important sources of social structure in this social context. The findings generally support these assertions. These findings also add to a growing body of research concerning the sociology of entrepreneurship (Aldrich, 1991) and innovation development (Van de Ven et ul., 1989). Recent research on the process of innovation underscores the importance of understanding the social cognition of people in new organizational contexts and hints that the links between people is the

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key to understanding the process. The framework for comparing and integrating findings across all innovations “is based on a definition that the process of innovation is the invention and implementation of new ideas, which are developed by people, who engage in transactions with others over time within an institutional context, and who judge outcomes of their efforts and act accordingly” (Van de Ven and Poole, 1990). Understanding what the salient dimensions of social structure are to participants (and how those perceptions vary as a function of time as a result of organizational development) in entrepreneurial organizations would be useful, not only to current entrepreneurs trying to plan or manage their start-up business (Good, 1989; Kao, 1989; Greve, 1992), but also to small business owners with similar organizational structure and tasks (Cunningham and Lischeron, 1991).

References Aldrich, Howard 1991 “Need for more research on entrepreneurship.” The American Sociological Association Organizations and Occupations Newsletter Spring. Berge, Claude 1973 Graphs and Hypergraphs. Amsterdam: North-Holland. Borgatti, Steve 1989 ANTHROPAC Software Package. School of Social Sciences. University of California, Irvine. Boster, James S., Jeffrey C. Johnson and Susan C. Weller 1987 “Social position and shared knowledge: actors’ perception of status, role, and social structure.” Social Networks 9: 375-387. Brajkovich, Leo 1991 “Perceived groups in a start-up company.” Paper presented at the Sunbelt XI International Sunbelt Social Network Conference, 15-18 February 1991, Tampa, Florida. Burton, M.L. and S.B. Nerlove 1976 “Balanced designs for triads tests: two examples from English.” Social Science Research 5: 247-267. Carmines, Edward G. and Richard A. Zellner 1979 Reliability and Validity Assessment. Beverly Hills: Sage. Collins, Randall 1988 Theoretical Sociology. New York: Harcourt, Brace, Jovanovich. Cunningham, J. Barton and Joe Lischeron 1991 “Defining entrepreneurship.” Journal of Small Business Management 10: 45-57. Freeman, Linton C., Freeman Sue C. and Alaina G. Michaelson 1988 “On human social intelligence.” Journal of Social and Biological Structure II: 415-425. 1989 “How humans see social groups: a test of the Sailer-Gamin models.” Journal of Quantitatice Anthropology I: 229-238.

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/ Sociul Networks

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Good, W.S. 1989 Buildmg a Dream: A Comprehensive Guide to Starring a Business of Your Own. Toronto: McGraw-Hill. Greve. Arent 1992 “Networks and entrepreneurship (in Sweden).” Paper presented at the Sunbelt X11 International Sunbelt Social Network Conference, 13-16 February lY92, San Diego, California. Hubert, L.J. 1987 Assignment Methods in Combinatorial Data Analy.sis. New York: Marcel Dekker. Hubert, L. and J. Schultz lY76 “Quadratic assignment as a general data analysis strategy.” British Journal of Mathematical and Statistical Psycho& 29: 190-241. Johnson, Jeffrey C. and Marc L. Miller 1986 “Behavioral and cognitive data: a note on the multiplexity of network subgroups.” Social Networks 8: 65-77. Johnson, L. 1967 “Hierarchical clustering schemes.” Psychomefrica 32: 241-2.54. Kao, R.W.Y. IYXY Entrepreneurship and Enterprise Dec’elopmenr. Toronto: Holt. Rinehart. and Winston ot Canada, Ld. Krackhardt. David 1987 “Cognitive social structures.” So&l Networks 0: 1(39%134. 1990 “Assessing the political landscape: structure, cognition, and power in organizations.” Administrativz Science Quarterly 3.5: 342-369. Michaelson, Alaina and Noshir Contractor Paper presented at the Sunbelt XII lYY2 “Cohesive structure and similarity perception.” International Sunbelt Social Network Conference, 13-16 February 1992, San Diego. California. Romney. A. Kimball, Roger N. Shepard, and Sara Beth Nerlove 1972 Multidimensional Scaling: Theor): and Application in the Beharioral Sciences. New York and London: Seminar Press. Sailer. L. and S. Gaulin 1984 “Proximity, sociality, and observation: the definition of social groups.” Amencan Anthropologist 86: 91-98. Spradley. James P. 1979 The Ethnographic IntercGw. New York: Holt, Rhinehart, and Winston. Thompson, James D. 1967 Urgunization~ in A&on. New York: McGraw-Hill. Van de Ven, Andrew H. and Marshall Scott Poole 1990 “Methods for studying innovation development in the Minnesota Innovation Research Program.” Organization Science l(3): 313-335. Van de Ven, Andrew H., H. Angle and M.S. Poole lY8Y Research on the management of Innol,ation: The Minnesota Studies. New York: Harper and Row. Wasserman. Stanley, Katherine Faust, and Gerald Galaskiewicz 1989 “Correspondence and canonical analysis of relational data.” Journal of Mathematical Sociology 1(l): 1 l-64. Weller, Susan C. and A. Kimball Romney 1990 Metric Scahng: Correspondence Analysis. Beverly Hills: Sage Publishing.