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INFORMATION PROCESSING

270 SBR 61 July 2009 270-289

Andreas Wald*

A MICRO-LEVEL APPROACH TO ORGANIZATIONAL INFORMATION PROCESSING**

ABSTRACT

Based on network analytical techniques, in this paper I extend information-processing theory to the micro level and develop a new approach to directly analyze information processing within individual organizations. I introduce the concept of information fit for measuring organizational information processing capacity. To demonstrate how the approach can be applied empirically, I perform a comparative case study in two corpo-rations. The findings suggest that the micro-level analysis reveals a fine-grained picture of information processing within organizations. It provides a diagnostic tool for research and practice.

JEL-Classification: D23, L22, M10.

Keywords: Information Processing; Network Analysis; Organizational Networks; Organi-zational Structure.

1 ORGANIZATIONS AS INFORMATION PROCESSING SYSTEMS

Th e concept of information processing has a long tradition in organizational research (Th ompson (1967); Galbraith (1973); Tushman and Nadler (1978); Knight and McDaniel (1979); Larkey and Sproull (1984); Keller (1994); Premkumar, Ramamurthy, and Saun-ders (2005)). Its relevance originates in the interrelation of organizational information processing capacity and performance (Habib and Victor (1991); Smith et al. (1991); Rogers, Miller, and Judge. (1999); Wolf (2000); Rogers and Bamford (2002)). Most current approaches view organizations as information processing systems that face internal and external uncertainties. Uncertainty prevails if the amount and quality of informa-tion required to perform the tasks cannot be provided and processed by the organization (Galbraith (1973)). An organization is assumed to be eff ective if there is a fi t between its

* Andreas Wald, Assistant Professor, Strascheg Institute for Innovation and Entrepreneurship (SIIE), European

Business School (EBS) International University Schloss Reichartshausen, D-65375 Oestrich-Winkel, Germany,

Phone: +49 (0) 6723 6022-13, Fax: +49 (0) 6723 6022-29, e-mail: [email protected].

** I would like to thank two anonymous referees for their valuable comments and suggestions.

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information processing capacity and the information processing requirements of its envi-ronment. In that case, decision makers receive the right amount and quality of informa-tion required for coping with uncertainty and complexity. Researchers assume that the corporate strategy causes the information processing requirements of the organization (Venkatraman and Camillus (1984)). Diff erent organizational structures are supposed to be adaptable for diff erent environments. Information-processing theory is based on ideas from the contingency theory, because the choice of the structure is contingent on its infor-mation processing capabilities and the information processing requirements of its envi-ronment (Rogers, Miller, and Judge (1999)).

Several studies apply information-processing theory to the organizational structures of multinational corporations (MNCs) where information processing is even more crucial than in national organizations (Egelhoff (1991); Tihanyi and Th omas (2005)). Large MNCs have to deal with increased organizational complexity. In addition to diff erent product lines, the organization must coordinate subsidiaries operating in diff erent national and cultural envi-ronments (Manev (2003)). In the literature, researchers classify formal structures of MNCs on the basis of their information processing capacity. Egelhoff (1982; 1991) distinguishes four types of organizational macro structures. For instance, he considers that a worldwide product division structure is an adequate solution for an environment in which the pres-sure for worldwide economies of scale is high, because within this structure, information about product related matters can easily be transmitted from subsidiaries to headquarters. On the other hand, worldwide product divisions do not support the fl ow of information on country matters and therefore are not suited to an environment in which a response to specifi c local conditions is essential. Wolf ’s (2000) comprehensive study considers about 15 structural alternatives including subtypes and mixed forms. Both Egelhoff and Wolf have a macro-level perspective and do not directly measure organizational information processing capacity. Instead, they use information processing as an abstract intervening concept (Egel-hoff (1981); Wolf (2000); Wolf and Egelhoff (2002)). Th ese studies describe the prevalence of organizational structures, their evolution over time, and the interrelations between the structure and diff erent dimensions of the strategy. More recent research applies informa-tion-processing theory to the fi elds of information-technology management (Anandarajan and Arinze (1998); Andres and Zmud (2001); Cooper (2005)), supply chain management (Premkumar (2005); Defee and Stank (2005)), and innovation management (Song, van der Bij, and Weggemann (2005)) However, neither earlier nor more recent work has directly examined information processing.

Th e question of whether a functional organization or a corporation with a divisional structure actually has the information processing capabilities that information-processing theory assumes has not been investigated. However, this is a crucial step for linking the macro-level approach to the level of individual organizations. From a managers perspec-tive, it is essential to know which information fl ow actually occurs and whether the information processing capacity of the organization fi ts to the information processing requirements.

Th e micro-level approach that I suggest in this paper analyzes information processing requirements and information processing capacities for all organizational units, including


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vertical information fl ow from the headquarters to subsidiaries and divisions and hori-zontal information fl ow between subsidiaries and divisions. Because my approach focuses on the details of information processing within organizations, I label it as “micro level” to distinguish it from the macro-level approach. Th e micro-level concept is based on network analytical techniques as suggested by Egelhoff (1982). I conceptualize organizations as networks in which organizational units represent the nodes and information fl ow repre-sents the relations (Gupta and Govindarajan (1991); Hansen (2002)).

Th e paper is organized as follows. Th e next section discusses the existing macro-level studies on information processing and develops the micro-level approach. Th e third section shows how this approach to organizational information processing can be applied empirically by means of network analysis. It follows the description of the two empir-ical cases and the process of data collection. In the empirical part, I assess the informa-tion processing requirements and capacities of the two organizations. After discussing the results, I describe perspectives for further research and derive managerial implications.

2 MACRO-LEVEL AND MICRO-LEVEL OF INFORMATION PROCESSING

2.1 MACRO-LEVEL STUDIES

Th e most comprehensive empirical studies on information processing in corporations have been conducted by Egelhoff (1982), Habib and Victor (1991), Wolf (2000), and Wolf and Egelhoff (2002). Th ese authors argue in the framework of the Strategy-Structure-Approach based on the work of Chandler (1962). Th e scope of the analysis is the formal structure which has been studied on the macro-level of the entire organization. Th e corporate structure serves as the dependent variable while the corporate strategy acts as the explanatory variable (Egelhoff (1982); Habib and Victor (1991); Wolf (2000)). Th ese studies measure corporate strategy by variables such as ‘foreign product diversity’ or ‘number of foreign subsidiaries’. Th ey do not directly assess information processing requirements or processing capacity. Rather, the authors assume that information processing requirements are implicitly part of the corporate strategy and that processing capacity is implicitly part of the organizational macro structure (Egelhoff (1982)). For instance, high foreign product diversity is assumed to be related to the requirement for processing a great deal of information on product matters. As a consequence, a worldwide product division is suggested as a formal structure.

Previous studies hardly paid attention to the type (strategic vs. tactical) and subject of information (Wolf (2000); Egelhoff (1982)). Likewise, further dimensions of organiza-tional information processing, like the richness, the importance and the media of infor-mation (Daft and Lengl (1986)) have not been considered empirically.

2.2 THE MICRO-LEVEL APPROACH

To develop a micro-level approach, I shift the focus from generic organizational macro structures to the information processing that actually takes place within individual orga-



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nizations. Doing so implies a shift from the formally prescribed structure to the actual structure. A change in perspective is also required for the scope of analysis. Further, I do not restrict the analysis to the whole organization. Large, diversifi ed corporations gener-ally comprise a variety of organizational units and subsidiaries that are located in diff erent regions of the world. For the coordination of such complex organizations, not only must one examine the information processing capacity of the entire organization, but espe-cially the information fl ow within and between diff erent regions or business segments. Identifying parts of the organization in which processing capacity is low is a necessary prerequisite if management is to take action for improvement.

Besides the scope and the level of analysis, the most striking diff erence between the macro and the micro approaches is the direct assessment of information processing require-ments and capacity. More recently, Lamont et al. (2000) undertook a fi rst step to assess the actual information fl ow between corporate headquarters and foreign subsidiaries. Th ey measured the amount of information received by corporate CEOs. However, their approach is limited to the vertical information fl ow from divisions to the headquarters and does not examine horizontal relations between divisions.

From a micro-level perspective, a corporation is considered as a network of relations between the organizational units (Ghoshal and Bartlett (1990)). Th e direct measurement of information fl ow inside and between organizations is not a new approach but has been applied in many network analytical studies. Th ese studies deal with a variety of research questions. Tsai (2001) for instance, analyzes the eff ect of an organizational unit’s position in the information network on the rate of innovation and performance. Hansen (1999) shows that the strength of network ties aff ects knowledge sharing across organizational units. Although the application of network analysis to measure organizational information processing suggests itself, existing network studies neither refer to information-processing theory nor attempted to measure the information processing capacity of diff erent orga-nizational structures empirically. One of the few exceptions is an early study of Tushman (1978) who examined the structure of communication in R&D Laboratories and collected data on ego-centered networks. However, his approach relied on standard survey tech-niques and did not analyze complete networks. Th e micro-level approach suggested in this paper analyzes information processing requirements and information processing capaci-ties for all organizational units including vertical information fl ow from the headquarters to subsidiaries and divisions as well as horizontal information fl ow between subsidiaries and divisions.

Th e concept of information fi t constitutes the core of the approach. Information fi t measures information processing capacity as the degree of fi t between an organizational unit’s demand for information and the supply of information it actually receives. Figure 1 shows three diff erent cases of information fi t and misfi t. In the fi rst case, unit A demands information from unit B and B supplies information to A. Since supply meets demand, this case represents a fi t of information fl ow. In contrast, the second case describes infor-mation misfi t. Unit A demands from unit B, but B does not supply all the required information to A. Th is situation is one of undersupply, as unit A does not receive the information required. Similarly, an oversupply of information must be considered as


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misfi t. Looking at case number three, B supplies information to A, but A has not asked B for that information. Oversupply may lead to an information overload, and thus reduces organizational effi ciency.

Figure 1: Information Fit and Misfit

A demands

B suppliesUnit

AUnit

B

A demands

UnitA

UnitB B supplies

UnitA

UnitB

1. fit: demand = supply 2. misfit: demand > supply 3. misfit: supply > demand

Organizational units in large, complex corporations not only demand information from one source, but also require information from diff erent parts of the organization. To assess the complete information fi t of a unit, one must measure fi t and misfi t for all the demand relationships in a unit. For example, I assume that unit A demands information from ten other units. If all of the ten units supply information to A, there is a perfect infor-mation fi t of 100%. On the other hand, if none of the ten units supply information to A, then the fi t is zero. Likewise, information fi t is 70% if seven out of ten units supply information to A. One can extend the concept of information fi t to measure information processing at every level of the organization. For the whole corporation, information fi t can be measured as the mean of the individual units’ fi ts. In Table 1 I compare the central properties of the micro-level approach to those of the macro-level approach.

Table 1: Macro-Level and Micro-Level Approach to Organizational Information Processing

macro level micro level

scope of analysis formal structure formal and actual structure

level of analysis whole organization organizational units

Information processing requirement (IPR) not directly measured directly measured

strategy = IPR demand = IPR

Information processing capacity (IPC) not directly measured directly measured

formal stucture = IPC fit of demand and supply = IPC

substance of information strategic / tactical all kind of information

Th e main argument of the micro-level approach is that every single organization is diff erent from every other in its information processing capacity. Th e concept of infor-mation fi t provides a framework for managers and researchers to directly assess organiza-tional information processing.



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3 APPLICATION OF NETWORK ANALYSIS TO MICRO-LEVEL INFORMATION PROCESSING

Network analysis serves as the basis for developing the micro-level approach to orga-nizational information processing. Network analysis is a prevalent and well established methodology in organizational research (Borgatti and Foster (2003); Brass et al. (2004)). A network perspective considers organizations as networks consisting of the organiza-tional units, which are the nodes, and a set of relations between them. Th e content of the relations may be formally prescribed lines of command, work-related fl ows of goods and services, or social ties such as friendship or trust. Network analysis provides powerful tools for describing the structures of networks, for identifying the underlying patterns in one or more networks, and for exploring the coherence between network structure and action (Wasserman and Faust (1994); Carrington, Scott, and Wasser-mann (2005)). In contrast to statistical analysis, network data is not about the attributes of the cases (nodes) but about the patterns of relationships between the nodes. Th us, this approach requires diff erent analytical methods that limit the potentials of statis-tical testing on the one hand, but develop good opportunities for structural analysis on the other hand. For information processing, information fl ows are the relations the researcher must consider. Th e organizational units represent the nodes of the network.

3.1 TOTAL AND PARTIAL NETWORK DENSITIES

According to information-processing theory, the diff erent formal structures should correspond to specifi c information processing capacities. Although the macro-level approaches generally deal with information processing on the level of the entire organi-zation, several authors also make statements about the information fl ow between orga-nizational units. Th ey argue that diff erent formal structures facilitate and/or constrain certain information fl ows between headquarters and units on the second level (Egel-hoff (1982, 1988); Habib and Victor (2001); Wolf (2000); Lamont et al. (2000); Wolf and Egelhoff (2002)). Th inking of the organization as a network of actual information fl ow between organizational units, network regions of denser interaction should become clearly distinguishable from regions with sparser interaction.

Network analysis provides a universal measure for describing a network’s denser and sparser regions. Th is measure is called network density. Network data is stored in matrices with all the players in both the rows and the columns. For instance, if in the network of information demand a unit i demands information from a unit j, then the cell of the matrix where row i intersects with column j has the value of one, otherwise zero. Th e density ∆ of a network k is the ratio of the number of present ties (relations) to the maximum number of all possible relations. Th e number of the maximum possible ties in a network of n units is n(n – 1). One can obtain the number of present ties by counting all ties x between the units i in the rows and the units j in the columns of the network matrix (Wasserman and Faust (1994)).


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∆k =

∑ i = 1

n

∑ j = 1

n

X ij

_________ n(n – 1)

(1)

One can also calculate partial densities for diff erent subsets of units in a network, for instance, the density between the headquarters and the regional divisions. In this case, one must adjust the number of units in the denominator of equation 1. Th e density of a network ranges from zero (0%), where no ties are present, to one (100%), where all units are connected to one another.

3.2 NETWORK OF INFORMATION FLOW IN A MATRIX

In the empirical analysis, I apply macro-level and micro-level theory to analyze informa-tion processing in two corporations. Th e fi rst fi rm is a matrix organization, and the second is organized as a management holding company. Consistent with information-processing theory, diff erent structures should have diff erent information processing capacities.

Matrix organizations are multiple command systems that are formed to manage two key requirements simultaneously. Th is structural alternative is designed to cope with high environmental complexity. External complexity is refl ected in the organizational structure. Informal horizontal communication channels in single command systems become formal-ized in matrix structures. As a consequence, a set of formal horizontal relationships overlies the vertical hierarchy (Knight (1976); Davis and Lawrence (1977); Ford and Randolph (1992)). Due to their organizational complexity, matrix organizations should process more information than any single command system. Proposition 1, which concerns the amount of information processed by the whole organization, predicts a high density of informa-tion fl ow.

Proposition 1: The overall density of information flow in a matrix organization is high.

Single command systems facilitate the information fl ow between the board and the units of one dimension (e.g. regional or functional). As a multidimensional structure, a matrix facilitates information fl ow between the board and the units of several dimensions. Th ere-fore, information exchange in a matrix organization should be high between the board and the units of all organizational dimensions (Wolf (2000); Wolf and Egelhoff (2002)).

Proposition 2: The density of information flow between the board and the units of all dimensions is high.

One-dimensional structures facilitate information processing in only one area. For instance, a product division structure is supposed to process mainly product related infor-mation, but country-related information is widely neglected (Egelhoff (1982)). In contrast, a matrix is able to process both country- and product-related information. Information channels between the two diff erent dimensions are formalized. Th e underlying assump-tion is that the formal structure exerts a strong infl uence on information fl ow within the



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organization (Egelhoff (1982)). Th erefore, formalized information channels should result in actual information fl ows. In the matrix, the density of information fl ow between orga-nizational units of diff erent dimensions should be high. Likewise, information fl ow that has not been prescribed by the formal structure should be low. Units of the same dimen-sion have no need to exchange much information, since they are responsible for diff erent product lines or diff erent regions.

Proposition 3: The density of information flow between units of different dimensions is high.

Proposition 4: The density of information flow between units of the same dimension is low.

3.3 NETWORK OF INFORMATION FLOW IN A MANAGEMENT HOLDING COMPANY

While a matrix approaches environmental complexity by creating organizational complexity, a management holding fi rm does quite the opposite. Complexity is reduced by designing a very simple formal structure. Th is organizational form consists of the headquarters and several subsidiaries that report directly to it. At the headquarters, a few functional units provide services for the globally dispersed subsidiaries. Staff at head-quarters is kept to a minimum. A management holding company can be considered as a special case of a product-division structure that has the distinguishing feature of legally independent subsidiaries. Th e subsidiaries enjoy a high degree of autonomy in their operational business, but headquarters coordinates strategic matters. Hence, the overall density of information fl ow in a management holding company should be much lower than in a matrix:

Proposition 5: The overall density of the information flow in a management holding company is low.

Most information in a management holding company is transferred and processed within the organizational units, i.e., the subsidiaries (Wolf (2000)). However, there should be regions of denser information exchange between certain parts of the holding organization. In particular, information fl ow between the headquarters and subsidiaries should be high. Th e board is in charge of the coordination of the corporate strategy and therefore requires information from subsidiaries. Due to the high degree of decentralization, most informa-tion is generated and located in the subsidiaries. Likewise, subsidiaries require information on the corporate strategy so that they can adjust their operations accordingly.

Proposition 6: The density of information flow between the board and subsidiaries is high.

Providing services for the board and for the subsidiaries, the functional units are supposed to exchange a great deal of information with both sides. For strategy formulation, the diff erent functional units (law, marketing, management accounting) must also mutually exchange information:


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Proposition 7: The density of information flow between the functional units and the board is high.

Proposition 8: The density of information flow between the functional units and the subsid-iaries is high.

Proposition 9: The density of information flow between the functional units is high.

A management holding company is a highly decentralized and fl exible organizational form. It is often chosen for fast-expanding corporations, because it enables to integrate freshly acquired fi rms without restructuring the existing organization. Th e operational autonomy of the subsidiaries corresponds to a high degree of independence. Since coor-dination is the task of the parent company’s board, subsidiaries need not coordinate their business. Consequently, information fl ow between subsidiaries should be negligible.

Proposition 10: The density of information flow between subsidiaries is low.

Th e propositions derived from macro-level theory consider that the two dissimilar formal structures correspond with dissimilar information processing capabilities. On a micro-level, these diff erences should aff ect the density of information fl ow between organizational units. Table 2 summarizes the propositions about the densities in both organizations.

Table 2: Propositions about Network Densities

Matrix – total network density: high Holding – total network density: low

densities between…

Board of Directors

Product Divisions

Regional Division

Functional Divisions


Board of Directors

Functional Units

Subsi-diaries

Board of Directors – high high high Board of Directors – high high

Product Divisions high low high high Functional Units high high high

Regional Division high high low high Subsidiaries high high low

Functional Divisions high high high low

3.4 MEASUREMENT OF INFORMATION FIT

As developed in the theoretical part, information fi t measures the degree of fi t between demand for information and supply of information. Th us, the unconfi rmed networks of information demand and supply measure information fi t. For a binary network (0,1), let dij be the demand of actor i from actor j (j = 1, m) and sji be the supply of actor j to actor i. Let ∆SDij = 1 if dij = sji. Th is case represents information fi t as supply meets demand (case 1 in Figure 1). Moreover, let ∆SDij = 0 if dij ≠ sji. Th is case exhibits misfi t, since there is either an under- or oversupply of information (cases 2 and 3 in Figure 1).



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Putting all fi t constellations ∆SDij = 1 of a unit i in proportion to its total demand m reveals its information fi t. Generally, the information fi t IF for a unit i is:

IFi =

∑ j

∆SDij

________ m , i ≠ j. (2)

Th e range of IF is between zero (0%) and one (100%), with higher values indicating higher information fi t. Th e concept of information fi t can equally be applied to the entire organization. Information fi t IF of the whole organization O is the sum of the fi ts over all organizational units divided by the total number of units n:

IFO = ∑

i

IFi

______ n . (3)

4 RESEARCH SITE AND DATA COLLECTION

4.1 RESEARCH SITE

I choose the empirical cases because of their dissimilar macro-structures, which I assume diff er signifi cantly in their organizational information processing. Th e aim is not to describe the particular cases in detail, but demonstrate how to conduct a micro-level analysis.

Th e fi rst case is a large chemical corporation. Th e formal organization is a three-dimen-sional matrix (sometimes referred to as a tensor organization) comprising product, regional, and functional divisions. Responsibilities are unequally assigned to the three divisions. Th e product division’s main responsibility is worldwide strategic alignment. Th e regional divisions are in charge of the operations in the respective regions. Functional divi-sions off er services for the whole group. For instance, one functional division prepares and coordinates worldwide strategic planning. Th e board of this corporation consists of eight executive directors who are individually responsible for several divisions.

Th e second case is a smaller company that produces generic and specialized lubricants for a variety of applications. Th e formal structure almost perfectly fi ts the ideal type of a management holding company. Th e headquarters comprises eight executive directors and a relatively small staff in functional units that provides services for the board and the sub sidiaries. About 80 subsidiaries that are dispersed worldwide report to the board directly and run their operational businesses autonomously.

4.2 DATA COLLECTION

In collecting data by interviewing top executives in corporations, the problem is that interviewees are short on time and hard to approach. For this reason, I limit the empir-


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ical study to one type of information fl ow. In this study, I consider the information fl ow within corporate strategic planning. In both corporations, an expert in strategic plan-ning provided support in identifying the relevant organizational units. Th e set of actors includes all units that participate signifi cantly in corporate strategic planning. For the matrix organization, I analyze n = 48 units. Of the 48 units, eight are the members of the board of directors (the headquarters). Furthermore, 14 product divisions, ten regional divisions, and 16 functional divisions belong to the actor set. In the matrix organization, subsidiaries do not play any important role in the managerial structure. Th ey exist only for legal and fi scal reasons and thus are not considered. For the manage-ment holding company, n = 63 units turn out to be relevant for corporate strategic planning. In addition to the eight members of the board, I identify 38 subsidiaries and 17 functional units located in the corporate headquarters. In the management holding company, the managerial structure coincides with the legal structure, so subsidiaries are the units to be considered.

I collected network data by conducting personal interviews with the executive direc-tors, the heads of divisions, and the general managers of the subsidiaries. Other methods of network data collection were initially considered but fi nally ruled out (Wasserman and Faust (1994); Scott (2000)). Information fl ow within corporate strategic planning is highly confi dential and data on the networks cannot be collected by means of partic-ipating observations or document analysis. I asked the interviewees to answer on behalf of their entire unit and not only to quote their personal relationships. I presented a list with all relevant organizational units to the interviewees. To obtain the network of infor-mation demand, I asked the interviewees to mark all organizational units from whom they demand information regularly and directly. I used a second list to collect the supply network data.

Data collected by interviewing key informants in organizations can be a source of biases (Huges and Preski (1997)). Th us, to enhance the validity of the data, a network of confi rmed information fl ow has been generated from the network of information demand and the network of information supply. Th is method, introduced by Krackhardt (1990), among others, can increase face validity. In the confi rmed network, a relation x ij

confirmed between two units i and j is considered to exist only if i says that he demands informa-tion from j and j says that he supplies information to i (Krackhardt (1990); Hansen (1999)):

{ 1 if xiji = 1 and xijj = 1} x ij

confirmed = . (4) 0 otherwise.

I use the network of confi rmed information to test the propositions about the density of the information fl ow.



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5 RESULTS

5.1 INFORMATION DENSITY AND TEST OF PROPOSITIONS

For both the confi rmed and unconfi rmed networks, Table 3 shows the size of the networks (number of units), the amount of information measured by the number of relationships (ties) between the units, the total densities, and the average number of ties per unit. For example, in the matrix, the network of information demand comprises 1,102 ties between the n = 48 units. Th is is a network density of 0.488. On average, a unit demands infor-mation from 23 other units in the organization.

Table 3: Network Properties

Matrix Holding

demand of information

supply of information

confirmed information

demand of information

supply of information

confirmed information

Number of actors 48 48 48 63 63 63

Ties 1102 1099 771 1346 1271 724

Density 48.8% 48.7% 34.2% 34.5% 32.6% 18.6%

Average ties per unit 23.0 22.9 16.1 21.4 20.2 11.5

In the matrix organization, the network densities are about 0.49 for the unconfi rmed and 0.342 for the confi rmed information networks. Th ese values provide clear evidence for Proposition 1. Taking into account the size of the network, it appears that network densi-ties are high. Furthermore, this fi nding can be substantiated by the average number of ties. Within the strategic planning process, an organizational unit holds an average of 16.1 direct information relationships in the confi rmed network.

For the whole network of the management holding company, Proposition 5 postulates that the overall density of information fl ow should be low. Th e density for the demand network of the management holding fi rm is 0.345 and for the supply network 0.326. Th e confi rmed network of information fl ow has a density of 0.186. Th e density in the manage-ment holding company is clearly lower than the density of the matrix organization. Th ese fi ndings support Propositions 1 and 5.

A fi ner-grained picture shows the partial densities between diff erent categories of organizational units. First, I assign the units of the networks of information fl ow to the diff erent categories. Second, I calculate the partial densities between diff erent categories (see equation 1). Because the propositions about the information fl ow between diff erent subsets of organizational units refer to high and low values, I need to specify the rating of densities. Following an approach often used in empirical studies, I use the density of the complete network as a threshold. Partial densities that are above total density are considered to be high, but partial densities below total density are rated as low (Lincoln (1982); Wasserman and Faust (1994)).


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Figure 2 presents the partial densities. Th e arrows at the ends of the lines show the direction of the information fl ow. For example, in the matrix, the partial density of the information fl ow from the board to the product divisions is 0.2. Looking at the opposite direction, I observe a partial density of 0.46. Th e values supporting the propositions appear in boldface.

Figure 2: Partial Densities Between Organizational Units

Board ofDirectors

ProductDivisions

RegionalDivisions

FunctionalDivisions

FunctionalUnits

Board ofDirectors

Subsidiaries

0.12 0.16

0.46

0.20

0.34

0.33

0.35

0.26

0.24

0.28

0.29

0.58

0.490.420.35

0.07

0.26

0.33

0.35

0.35

0.22

0.19

0.14

Matrix, total density = 0.34 Holding, total density = 0.185

In the matrix, none of the propositions on partial densities is fully supported by the empir-ical data. Proposition 2, which predicts a high partial density between the board and the units of the diff erent dimensions of the matrix, fi nds only partial support. Th is assumption is supported only by the information fl ow between the board and the functional divisions, which is high in both directions. In contrast, the partial densities between the board and the regional divisions are low in both directions. A mixed result shows the densities between the product divisions and the board, which are high in only one direction. In addition, Prop-osition 4 is not fully confi rmed. Contrary to the assumptions, the units of the functional dimension exchange a huge amount of information. Partial support for Proposition 4 is provided by the density between regional divisions and between product divisions, both of which are low. Proposition 3, which predicts a high density of information fl ow between units of diff erent dimensions, is only weakly supported. Th e densities between product divi-sions and regional divisions are high, but the densities between product divisions and func-tional divisions, and between regional divisions and functional divisions, are low.

For the management holding company, the values in Figure 2 show diff erent results. Except for Proposition 9, all propositions that consider partial densities are supported. Both, the board and the functional units in the headquarters exchange a lot of informa-tion with the subsidiaries (Propositions 6 and 8). In line with Proposition 10, the partial density of information exchange between the subsidiaries is very low (0.07). Th e board and the functional units are engaged in an intensive exchange of information (Proposition 7).Th e density of information fl ow between the functional units is only slightly below average (Proposition 9), which might indicate that only some of the units need to cooperate in order to support the corporate strategic planning process.



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Table 4 reports in boldface the values that support the propositions. Th e values that reject the propositions are in italics.

Table 4: Results Network Densities

Matrix – total network density: high Holding – total network density: low


Board of Directors

Product Divisions

Regional Division



Board of Directors

Functional Units

Subsi-diaries

Board of Directors – low low high Board of Directors – high high

Product Divisions high low high low Functional Units high low high

Regional Division low high low low Subsidiaries high high low


high low low high

5.2 INFORMATION PROCESSING REQUIREMENTS

For a fi nal assessment of organizational information processing on the micro-level, I analyze the specifi c information processing requirements and capacities of organizational units.

I measure the information requirement of an organizational unit by its demand for infor-mation. I summarize (block) the units according to the formal organizational categories, e.g., regional divisions or subsidiaries. Table 5 shows the information requirements of the whole organization and for each category. In addition to the total number of ties, Table 5comprises the average ties per unit of each category (the number of demand relationships of each unit), the maximum and the minimum of a unit’s demand, and the categories’ share of the total demand of the entire organization.

Table 5: Information Processing Requirements (Demand)

Matrix Holding

ties ties per unit max/min % of total demand

ties ties per unit min/max % of total demand

Board of Directors

258 32.3 19/44 23.4% Board of Directors

208 26.0 11/38 15.5%

Product Divisions

215 15.4 5/28 19.5% Functional Units

478 28.1 3/48 35.5%

Regional Division

196 19.6 6/36 17.8% Subsidiaries 660 17.4 2/46 49.0%


433 27.1 10/46 39.3%

Total 1102 23.0 100,0% Total 1346 28.0 100,0%


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For both organizations, I observe that the information processing requirements vary consider-ably across the diff erent categories. In the matrix, it is obvious that the members of the board have by far the highest demand for information. On average, each board member demands information from 32.3 other organizational units. Because I am examining strategic informa-tion, this result is not surprising. Strategic planning is one of the core activities of the executive directors and information is the most critical resource for strategic decision making. Product divisions (15.4) and regional divisions (19.6) have a much lower demand for strategic informa-tion. Again, this result is easy to explain. On the one hand, regional divisions and product divi-sions only have to cope with their own partial strategy and are not involved in the coordination of corporate strategy. On the other hand, functional divisions are important service providers for strategic decision makers. Th eir high demand for information, 27.1 on average, is consis-tent with this task. Th e need to consider a unit’s actual information requirement becomes even more obvious if I consider the demand of the individual units. For instance, the demand of the product divisions ranges from fi ve (minimum) to 28 (maximum) and the diff erence between the highest and the lowest demand of the functional divisions even is 36 (10-46).

In the management holding company, the functional units’ demand is highest (28.1). Th e board follows close behind (26). Th e subsidiaries’ average demand is much lower (17.4). Compared to the matrix, the diff erences among the categories in the management holding company are even higher. Th e highest demand observed for a subsidiary is 46, which means that the respective unit demands information from about 73% of all other organizational units. In contrast, the lowest demand amounts to two. In the management holding company, the subsidiaries vary strongly in size and in their strategic relevance for the entire corporation. Moreover, the activities of some subsidiaries comprise the whole value chain, while other subsidiaries only provide marketing and selling of products. Th us, their demand for information varies. Additionally, there is a considerable variance in the demand for strategic information for the board and for the functional units.

5.3 INFORMATION FIT

Th e fi nal step in my analysis is to assess the actual information fi t for both cases. Following equations 2 and 3, I put the information fi t in relation to total demand. Table 6 shows the results for the whole organization and for the diff erent categories.

Table 6: Total and Partial Information Fit

Matrix HoldingTotal (IFO) 81.5% Total (IFO) 74.0%

Board of Directors 70.5% Board of Directors 92.8%

Product Divisions 85.6% Functional Units 62.1%

Regional Division 76.0% Subsidiaries 75.5%

Functional Divisions 82.0%

min 26.0% min 8.0%

max 100.0% max 100.0%

S.D. 0.194 S.D. 0.256



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Contrary to the results obtained from the analysis of partial densities between organi-zational units, the matrix has a higher information fi t (81.5%) than the management holding company (74%). Th e assessment of the organizational microstructure shows very diff erent patterns for each case. On average, the board in the matrix has the lowest fi t (70.5%), and product divisions and functional divisions exhibit values above 80%. For the management holding company, the board shows the highest fi t (92.8%). Contrary to expectations, the functional units that support strategic decision-makers only receive 62.1% of the information they need.

A common fi nding for both cases is the high variance in the information fi t of the indi-vidual units. Th e range for the matrix is between 26% and 100%, and even higher for the management holding company (8%-100%). Quite a few organizational units in both organizations are heavily undersupplied with important strategic information.

6 DISCUSSION

Th e aims of this paper were fi rst, to develop a micro-level approach based on network analysis and second, to show how organizational information processing can be inves-tigated empirically. Th erefore, the concept of information fi t has been introduced as a measure of information processing capacity.

In a fi rst step, propositions derived from macro-level information-processing theory served as a guideline for the analysis. Partial network densities between units belonging to diff erent categories of the formal organizational structure were used to measure the densi-ties of information fl ow between the organizational units. Empirical results provided only partial support for the propositions. At fi rst glance, it seemed that the holding has a higher information capacity than the matrix, as the information fl ow observed in the holding was in line with the propositions. A more plausible reason for this fi nding is that the theoreticalconsiderations regarding the information processing capacity of organizational structures are too generic for a micro-level analysis. Abstracting from the specifi c characteristics of an organization does neither fully capture its information processing requirement nor its information processing capacity. Ostensibly, the theoretical considerations concerning the information processing capacities of diff erent organizational macro-structures are too abstract to be usefully applied to individual organizations.

Accordingly, in a second step, I analyzed information processing at the micro-level. Th e organi-zational units demand served to measure the information processing requirement. Th e results demonstrate that a micro-level analysis is able to cover the idiosyncrasies of actual organiza-tions. For both organizations, a high variance could be observed. Moreover, variance not only existed between units of diff erent organizational categories but also was a common occurrence among units belonging to the same category. Information requirements of individual units depend on a variety of contingency-factors, such as the units’ strategic role or environmental dynamics and complexity. In addition, corporate strategy is a multidimensional variable (Caves (1980)). Instead of trying to include all these factors, it is more convenient and more exact to directly assess information-requirements by measuring the demand of each unit.


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Finally, I applied the concept of information fi t empirically to measure information processing capacity on the micro-level. Again, the results point to the fact that it is impor-tant to consider the actual capacity of the diff erent organizational units as individual orga-nizations do not match with the abstract prototypes. Th e actual information processing requirements and processing capacities do not conform to those suggested in literature on organizational information processing. Looking at single units like board members, subsidiaries, or functional units, the idiosyncrasies of individual organizations became even more apparent as in both organizations a highly heterogeneous pattern could be observed. Th ese results provide support for the argument that a micro-level approach is a useful complement to the macro-level approach, as the latter does not consider the actual information processing of individual organizations.

Although the fi ndings of this study are encouraging, some limitations should be recog-nized for further research. Th e main concern addresses the considerable eff orts required for data collection. Due to the specifi c requirements of network analysis, one cannot rely on sampling techniques, but has to collect data on the relations between all actors of the system (Burt (1983)). In this study many executives on the fi rst two levels of hierarchy had to be interviewed. To gain access to those persons was a very challenging task. For this reason, a comparative case study design was chosen and standard statistical methods could not be applied. Accordingly, the fi ndings cannot be generalized. Moreover, several other dimensions of information processing could not be considered in this research. As the aim was to develop a new approach and demonstrate its application, I focused on the information fl ow exclusively. To keep things tractable, the subject, the richness, and the importance of information were not treated as variables but as constants. Without ques-tion, these are important dimensions of information which must be integrated in subse-quent studies.

7 CONCLUSION

I presented a micro-level approach to organizational information processing. It provides an analytical tool for further research that might investigate the coherence between infor-mation processing capacity and organizational performance. To date, macro-level studies showed mixed results, and with few exceptions, no clear eff ect of processing capacity could be determined (Habib and Victor (1991); Smith et al. (1991); Rogers, Miller, and Judge (1999); Wolf (2000); Rogers and Bamford (2002)). Applying the micro-level approach might assure that diff erences in information processing capacity could adequately be assessed, and might improve the explanatory power of fi t-performance models. Based on these insights, macro-level theory can be improved and refi ned.

Th e micro-level approach might also serve as a foundation for empirical research beyond questions on the coherence between strategy and structure. Th is approach can be applied to address questions and concepts in knowledge management, such as the absorptive capacity of organizations (Cohen and Levinthal (1990)) or the knowledge-based view of the fi rm (Grant (1996)).



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Examining information processing at the level of individual organizational units also allows to deduce managerial implications. Managers concerned with organizational design may recognize which parts of the organizational information processing do not match the processing requirements. Information bottlenecks are revealed, as are isolated units. On this basis, management can take actions to enhance the information fi t. As a diagnostic tool for revealing actual patterns of information fl ow, the micro-level approach is particu-larly well suited to comparisons with the formally described channels of information fl ow. A promising area of application mentioned by Cooper and Wolfe (2005) could be the implementation of new information technology. Complex, comprehensive solutions like Enterprise Resource Planning (ERP) systems signifi cantly aff ect information fl ow within the entire organization. Measuring actual information processing requirements and infor-mation processing capacity before and after the implementation of ERP systems can be used as an indicator of ERP implementation success.

Similar to earlier studies on organizational information processing, this study focuses on information fl ow within corporations. Other recent contributions have shown that the application of information-processing theory must not be limited to intraorganiza-tional information fl ow but can be extended to processes spanning the entire supply chain (Premkumar (2005)). An important task in the optimization of business processes is the design of an effi cient and eff ective information fl ow. Th e approach developed in this paper may also serve as a tool analyzing processes that cut across organizational boundaries.

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