Information transformation: Some missing links - IOS Press

Human Systems Management 26 (2007) 157–172 IOS Press

157

Information transformation: Some missing links Ivan P. Vaghely ∗ , Pierre-André Julien and André Cyr Institut de recherche sur les PME, Université du Québec à Trois-Rivières, 3351 Boul., des Forges, C.P., 500, Trois-Rivières, Québec, G9A 5H7, Canada Tel.: 819-376-5011x 4031; E-mail: [email protected] Abstract. Using grounded theory along with participant observation and interviews the authors explore how individuals in organizations process information. They build a model of human information processing which links the cognitivist-constructionist perspective to an algorithmic-heuristic continuum. They test this model using non-parametric procedures and find interesting results showing links to efficient information processing outcomes such as contributions to decision-making, knowledge-creation and innovation. They also identify some elements of best practice by efficient human information processing individuals whom they call the “information catalysts”. Keywords: Human information processing, algorithmic and heuristic information processing, cognitivist and constructionist perspectives, efficient information processing outcomes, information catalyst, information best practices

Ivan P. Vaghely is professor of Business Strategy at the Université du Québec à Trois-Rivières and Adjunct Chair of the Bell Research Chair for World Class SMEs at the Institut de Recherche sur les PME. His research interests are in human information processing and its links to decision making, innovation and knowledge management.

Pierre-André Julien is professor emeritus of Economics at the Université du Québec à Trois-Rivières and former Chair of the Bell Research Chair for World Class SMEs. He is a founding member of the Institut de Recherche sur les PME a thirty year old institute dedicated to SME research and a founding member and former editor of the Revue Internationale PME. He is the author of numerous articles and volumes on entrepreneurship.

* Corresponding author.

André Cyr is professor of Management at the Université du Québec à TroisRivières and a member of the Institut de Recherche sur les PME.

1. Introduction According to business historian Alfred Chandler, the firm is ultimately an information processing system that transforms raw data into usable information in order to meet market needs. From this perspective, the firm’s success depends on its members’ capacity to acquire and adapt information in order to seize existing opportunities in the environment, or even create new ones. Information acquisition within the organization ranges from the capture and processing of strong and weak signals, to knowledge creation, informationbased decision-making and innovation. The scope of

0167-2533/07/$17.00  2007 – IOS Press and the authors. All rights reserved

158

I.P. Vaghely et al. / Information transformation: Some missing links

this subject includes the environment or sources of information signals and the networks that convey those signals to the organization’s individual members and boundary spanners [48,49]. This subject also includes the individual in an organizational context who receives, exchanges, processes and transforms information. Finally, the scope includes the organization itself, which generates data through systems and information through individuals. The organizational context can foster or hamper information exchange. The organization uses rich information in its decision and sense-making processes; it also amplifies and disseminates this information which has been transformed into knowledge by individuals. The cognitivist and constructionist perspectives provide the basis for a theoretical framework on information processing at the individual level within the organization. The cognitivists pool tacit information by holding shared and complete representations without any doubt about the correspondence of these representations to reality. The constructionists have a different point of view: they submit that every individual has unique, personal information, much of which is tacit. The individual must justify such information in order to share it. Sense-making, which helps in the construction of organizational reality, is identified with the latter. Some clarification is required in order to identify the process’ building blocks. For the purposes of this study, we shall focus on the individual’s information processing strategies while putting aside external factors such as the organizational networks that convey this information. We will also discard elements such as knowledge-creation which result from the individual’s information processing. In sum, the individual in an organizational context is the focus of our analysis. We are interested in signals, data, information and human information processing strategies. Decision making, innovation and knowledge-creation activities interest us as outcomes of human information processing only. We are therefore not directly interested in the

different phases of innovation or insight as a creative process: preparation such as analysis, incubation defined as the chance combination of thought processes below the threshold of awareness, insight itself which is the “Aha!” experience and the evaluation which completes the process. Cognitive psychologists relate these multistage models to the “individual’s ability to let the relevant information interact with information from other domains at a subconscious level and to synthesize such information . . .” [11]. Constructionists [53] achieve such creation through socialization, internalization, combination and externalization [35]. Both processes require social interaction. We shall not discuss signal detection based on strong or weak ties [24], information networks, decisionmaking, innovation or knowledge-creation in the organization. The focus of our paper is illustrated in the shaded section of Fig. 1 below. Our objective is to develop a better understanding of the interdependent relationship between the individual’s cognitive framework, his/her information processing strategies and ability to transform information in order to establish links to knowledge creation, decision making and, in some cases, innovation. We attempt synthesis through model building. Johnson-Laird [28] puts it this way: “The mind is too complicated to be seen clearly, or to be studied with advantage, from the perspective of a single discipline. The scientific understanding of cognition depends on a synthesis . . .” Johnson-Laird pursues, “. . . like clocks, small-scale models of reality need neither be wholly accurate nor correspond completely with what they model in order to be useful [. . .] There are no complete mental models for any empirical phenomena. What must be emphasized, however, is that one does not necessarily increase the usefulness of a model by adding information to it beyond a certain level [. . .] to understand a phenomenon is to have a working model of it”. The idea here is to strike a balance; modeling is a form of vicarious learning. As Korzybski [31] argues, “The map is not the territory”. In other words the model, no

Fig. 1. Scope of this paper.


matter how complete, will always remain a constructed representation of reality. While it allows the individual to make sense out of that reality and act upon it, it is still an abstract representation that may vary from one individual to the next. We recall the definition of key elements that are central to the scope of our research, as in the case of insight and intuition, information and informationrichness, as well as strong and weak signals. We shall also define a number of elements that are excluded from our research such as knowledge, which is sometimes wrongly equated with rich-information. In defining the scope of our study, key concepts such as: knowledge, rich information and information itself had to be clearly defined in order to obtain a cohesive unit of analysis. In removing knowledge we had to consider what we were eliminating. We will also define “weak signals” because this concept is less well understood. We relate these definitions to each other based on an information processing perspective, and outline schemata and scripts from the individual’s cognitive perspective. They are essential elements that determine the level of information processing by the individual and his concomitant propensity to use information-based insight for knowledge-creation, decision-making and, in some cases, innovation. We synthesize and present extant research relevant to information processing by the individual. We will then present our model and discuss its implications. Our conceptual framework is built on two groups of related constructs. The first group is made up of seven information variables that represent so many areas of performance in the evaluation of the individuals’ level of information processing. These variables are related to the individuals’ information schemata. The second group is made up of seven informationrelated contextual factors in the individual’s organizational environment. These factors may have an impact on the individual’s information-processing performance. Thirdly, we will present the methodology used in this study. We found some research in the literature [6– 8,26,27,42,43,47] which can help the development of a conceptual framework on the acquisition of information and its transformation into a usable format within the organization, but due to the complexity of this field of research, human information processing calls for an exploratory methodology. For instance, the literature provides little discussion toward the specific development of a model of a conceptual framework on information acquisition, processing and use by the individ-

159

ual [6–8]. Similarly, researchers neither agree on the boundaries between information and knowledge management, nor on the definition or treatment of richinformation [1,5,13,14,16,30]. In order to develop our conceptual model, we used grounded theory along with an approach based on participant observation. We then tested the model through 55 in-depth qualitative interviews in multiple case studies of nine SMEs. The data gathered from these interviews were also used to enrich the theoretical framework and develop measures for the model. We will then present and discuss the results of our research. Making sense out of information is a vital activity in all organizations. In the specific context of SMEs, competitive strategy results from this sensemaking capacity on the part of the entrepreneur and his key staff, including so-called boundary spanners and gatekeepers. Among the high-information performing SMEs, however, we discovered another category of individuals who acquire, synthesize, transform, interpret and transmit information much more efficiently. We chose to call these people “information catalysts”. In a path-dependant way, these individuals play a major role in reducing the equivocality of fragmented information contained in weak signals. In so doing, they help identify or create opportunities and thus allow for innovative initiatives. In this section we also have a brief discussion on information-processing best practices. In conclusion we highlight a number of variables which allow for the measurement of an organization’s level of information alertness and examine some other findings of our study; we also identify areas of further research on information processing by the individual.

2. Some definitions recalled and extant research 2.1. Insight According to Simon [45, p. 63], intuition can be viewed as “analyses frozen into habit”, a view not shared by Mintzberg [34] nor by constructionists such as Weick [54], Von Krogh, Ichijo and Nonaka [53]. Psychologists of the cognitive school define intuition in the context of insight. They define insight as seeing and understanding the inner nature of things, especially by intuition. They define intuition as the immediate knowing of something without the conscious use of reasoning. Knowing is having a range of information, while reasoning is thinking logically and analytically.

160


An insight is typically said to occur when an individual is exposed to some new information that results in a new way of looking at a known problem or phenomenon in such a way that its essential features are grasped. According to Csikszentmihàlyi and Sawyer [11] insight is part of an extended mental process. It is based on a previous period of conscious preparation, requires a period of incubation during which information is processed in parallel at a subconscious level, and is followed by a period of conscious evaluation and elaboration. 2.2. Knowledge Knowledge can be defined as information combined with experience, context, interpretation, and reflection. It is a high-value form of information that can readily be applied to decision-making and action. While knowledge and information may be difficult to distinguish at times, both are more valuable and involve more human input than raw data [14]. Some authors define knowledge as rich information in an actionoriented context [52], others define it as a process; it’s the purposeful coordination of action [56]. It relies on experience to provide expertise and sense-making in the interpretation of equivocal data about environmental change. This definition requires further clarification of the concept of information richness. 2.3. Information and information richness Information is that which alters mental representations. Daft and Lengel [12] define informationrichness as “. . . the ability of information to change understanding within a time interval”. Communication transactions that can overcome different frames of reference or clarify ambiguous issues to change understanding in a timely manner are considered rich. Other authors define information as simply a symbolic description of action, like data it can be generated per se which means without direct human interpretation [55]. Rich media, such as face-to-face discussion, enable people to interpret data and make sense of ambiguous information such as rich-information weak signals. Equivocality is integrated through extensive discussion and sense-making in order to develop a common interpretation of the environment. Nonaka and Takeuchi [35] distinguish two philosophical dimensions related to information. The first, an ontological dimension, is a dialogical process of exchange between the individual, who synthesizes in-

formation and thus creates knowledge, and the group that acts as knowledge amplifier and disseminator. The second, an epistemological dimension inspired by Polànyi’s [39] work, distinguishes between tacit and explicit information. Whereas information reduces uncertainty, rich information helps make sense of ambiguous situations. It is conceptually closer to knowledge than to data. We note here that Polànyi and Nonaka do not define tacit information exactly the same way. Polànyi’s tacit dimension refers to innate intelligence, perception and reasoning as opposed to Nonaka’s definition which refers to memory and experience which can be made explicit through socialization. 2.4. Signals Blanco and Lesca [4] define weak signals as anticipatory, qualitative, ambiguous and fragmentary, of various formats originating from very diverse sources. These sources can be from within or from outside the organization. Explanations are given in Table 1 below. According to Ansoff [3] information that is required to anticipate unpredictable changes can be assimilated to weak signals. Blanco and Lesca [4] relate weak signals to early warnings from a business intelligence perspective and equate weak signals with early warning signals. For Porter [40] early warning signals can be defined as any action by a competitor that provides direct or indirect indications about its intentions, motivation, objectives or internal situation. However, early warning signals can also concern the technological, political, economical or social environment. In accordance with these definitions, signals can be categorized as “weak” or “strong”. The origin of both kinds of signals can be either external or internal to the organization: external signals come from the organization’s environment and consist of market cues, early warnings, competitive signals, etc, while internal signals such as company folklore, storytelling and tacit information are strongly influenced by the firm’s constructed reality, dominant logic and organizational culture. Strong signals are the opposite of weak signals. So if we define “strong signals” as: – Unambiguous, usually complete and clear, but new-information poor messages. Then we should define “weak signals” as:


161

Table 1 Characteristics of weak signals Characteristics

Explanations

Anticipatory

Weak signals are related to future potential events that may affect the organization’s survival; so a piece of information has no intrinsic relevance as it is not related to immediate decisions.

Qualitative

Weak signals do not consist of numbers or extrapolations as they concern events that have not occurred yet or that are at their very beginning; for which factual or quantitative data are not available.

Ambiguous

Weak signals can be interpreted in various ways, which makes them difficult to understand and use.

Fragmentary

Weak signals present themselves in the form of fragments, each of which is insignificant and questionable at best.

Various formats and sources

Weak signals can be picked up in any shape or form, such as bits of conversation, fragments of electronic data, messages from conferences, discussions with customers and suppliers, etc.

Adapted from: S. Blanco and H. Lesca, (2002). Business intelligence: integrating knowledge into the selection of early warning signals, working paper, Université Pierre Mendès France, Grenoble.

– Ambiguous, fragmentary and unclear, but innovative and new-information rich messages. Orderly archived information in a filing cabinet or on a computer disk can represent a family of strong signals unless the filed data are ambiguous so that the conveyed message confuses the individual’s mental representation of his environment. On the other hand feedback loops, which verify the quality of the information being processed, can either provide a strong or a weak signal. We have classified tacit information as a family of weak signals by its very nature. 2.5. Data Data are a collection of facts, measurements and statistics that are transactional in nature (e.g., banking transactions). They represent a unit of analysis. If information is defined as that which alters a mental representation, data must then effect a change in the individual’s understanding of reality in order to qualify as information. From this perspective, information is defined as organized or processed data, indicating trends, and reducing uncertainty in the decisionmaking process. Rich information is also timely, that is, available when strategically needed. Knowledge goes further; it allows for predictions, causal associations, or prescriptive decisions about what must be done. Thus, knowledge can be defined as contextual, relevant and actionable information. Zeleny [56] provides a sharper distinction: “knowledge is action and information is a description of action”. At the practical level, information is used through cognitive schemata and scripts, two knowledge structures that are critical to information processing by the individual.

2.6. Schemata and scripts Kiesler and Sproull [29] submit that information has a greater impact if it can be embedded into existing, heavily organized, and interconnected knowledge structures. Because of the individual’s limited information processing capacity, attention demanding information (i.e., salient information) will only be incorporated into organized knowledge structures and longterm memory if it seems relevant to those structures. Discrepant information, on the other hand, will tend to be discounted and forgotten. Organization members, in other words, do not actively process all informational cues anew in order to decide how to behave. Rather, they usually depend on a set of personal and consensual schemata to understand (and sometimes fill the gaps in) existing scripts. This is what allows them to respond to organizational situations with relatively little active information processing. Implicit assumptions about the importance of information signals are part of such schemata. Gioia [21] defines “schemata” as a built-up repertoire of tacit knowledge that is used to impose structure upon, and impart meaning to, otherwise ambiguous social and situational information in order to facilitate understanding. Well established schemata enable the individual to process information subconsciously, especially if a good match between current context and existing schema is achieved. This frees the person’s cognitive capacity to handle other more pressing, novel or innovative demands. Gioia and Poole [23] describe “cognitive scripts” as schematic knowledge structures, held in memory, that indicate the appropriate behavior or sequence of events in specific situations.

162


Information-processing models are organized into such abstract structures. These knowledge structures are the constructs against which new information is tested for relevance. Schemata simplify information processing and storage but, in the process, introduce biases. Such associative thinking leads to information expectations that are both well organized and resistant to new evidence. In this fashion schema-based expectations affect judgment and memory, and also filter the use of new or innovative information in weak signals. Motivational processes – such as external, vicarious or self-applied reinforcements – can provide incentives to enact an information-processing related script. In this sense an organization might be viewed as “a culture composed of a complex set of interrelated scripts that influence one another in both supportive and contrary ways” [22]. Although the map is not the territory, in sum, it nevertheless has a major impact on the individual’s perception of that territory, and on his actions within that territory. We now use these definitions in conjunction with grounded data, the cognitivist and constructionist theoretical perspectives to develop a conceptual model of human information processing.

3. Outline of a model

We can hypothesize that in unstructured, novel and complex information situations, such as information from weak signals, the individual tends to use a more heuristic approach to information processing. Conversely in structured information situations, such as problem-solving or uncertainty-reduction situations, the individual tends to use a more algorithmic approach to information processing. We can also hypothesize that by shaping the individual’s implicit assumptions about information acquisition and use, organizational culture influences the level of individual information-alertness, and therefore of receptivity to information and potentially innovation-rich weak signals. Information-alertness is thus socially constructed. Furthermore we find that insight is both algorithmic (a pattern of creative intuition based on experience) and heuristic (a process of social construction based on the communication of new information); both processes are shaped by environmental factors. Current research on information processing by the individual can be classified into two categories:

3.1. Information processing variables Information processing variables (i.e., elements of the individual’s information processing performance) are framed by cognitive information schemata. 3.2. Information processing contextual factors Information processing contextual factors (i.e., factors that shape the level of individual information processing or alertness) are framed by the organizational environment. The individual’s information processing variables are: – – – – – – –

Heuristic information processing [12,51]; Algorithmic information processing [12,44,45]; Weak-signal processing [3,4]; Strong-signal processing [3,4]; Use of tacit information [35–39]; Use of archived information [9,41]; Use of feedback loops [6–9,57].

The contextual factors which influence the individual’s information processing are: – The level of competition in the industry [40]; – The level of activity of Boundary Spanners [48,49]; – The level of information sharing by individuals [13,15]; – The existence of an information culture (trust and collaboration) [10,46]; – The recognition of information sources [13,15]; – The organizational structure [18–20]; – The level of use of rich information [12,15]. The conceptual model presented in Fig. 2 below has been developed on the basis of this literature in conjunction with grounded data. This model illustrates the individual’s information processing continuum between algorithmic and heuristic information treatment. When processing information, for instance, engineers and accountants mostly use established scripts and patterns at the algorithmic end of the continuum. When the situation calls for innovation, however, their problem-solving approach rests on experience-based patterns of creative intuition. Conversely artists and musicians rely on intuition and insight which are heuristic types of information processing at the other end of the continuum; they use a process of social construction based on the communication of new information (insight) in combination


163

Fig. 2. How individuals process information in an organizational context.

with existing tacit information (intuition). In the interpretation, construction and enactment of their environment, entrepreneurs also use a heuristic type of information processing for sense-making and opportunity recognition, which they combine with algorithmic processing based on their experience. From an epistemological perspective, cognitivists use formal models or algorithms. This perspective is characterized by the following treatment of information along the continuum: – Information shapes the representation of reality. Individuals compare their representations of the environment in order to shape the dominant logic of the organization and, by extension, the organizational culture; – On the basis of available information, the individual tries to construct a model of reality which is as accurate as possible. In this sense, the individual may be compared to an informationprocessing machine: artificial intelligence, expert systems and environmental scanning all use such processes; – According to the cognitivist vision of information processing, knowledge is explicit, codifiable and thus formal; such as, for instance, in multistage models of insight and innovation based on intuitive patterns. The constructionist perspective uses interpretative or heuristic models and is characterized by the following treatment of information along the continuum: – Individuals process new information in an interpretative way; they construct their reality by using information from their environment. Informa-

tion, in turn, leads to action. This logic is illustrated by Venkatraman’s [52] DIKAR model of information processing (Data to Information, to Knowledge, to Action and Results); – Individuals use two types of information: explicit information resulting from organizational sensemaking, and tacit information based on the individual’s construction of his own environment and on his innate abilities; – In order to share information, and create new knowledge and innovation, each individual must justify the beliefs that are based on that information. Formalization of such information is the key to innovation. Our conceptual framework integrates both of these epistemological perspectives into a pragmatic model of the individual’s information-processing mechanisms. At both ends of the continuum, the process that comes before and after insight/intuition is heavily dependant on social interaction. Cognitivists and constructionists agree on this. It is this process that transforms information into innovation [11,35]. There is a certain causal relationship between contextual factors and process variables, as well as between the individual’s environment and his information schemata. This relationship has an important but contingent impact on the individual’s information processing strategy (algorithmic-heuristic) for each variable. 4. Methodology In order to measure our respondents’ overall level of information processing performance, their perfor-

164


mance for each variable and contextual factor was ranked on a five-point scale. These individual measurements had to be made in order to generate an aggregate result for each organization. We used in-depth interviews to gather data from a sample of nine SMEs. Interviews lasted on average over 90 minutes. These nine organizations were selected on the basis of our a priori perceptions of the firm’s level of information intensity within Porter and Millar’s [41] information intensity matrix shown in Fig. 3 below. This matrix measures the informationintensity content of the value chain on the vertical axis, and the information-intensity content of the firm’s product/service on the horizontal axis. High information content and information value-added SMEs such as consulting firms, financial brokers or banks are in the right-hand corner of Quadrant 3, and low information content with little information value-added SMEs such as commodity manufacturing firms, mills or cement factories, are in the left-hand corner of Quadrant 1. The interpretation of the matrix can be illustrated by the information content of discount brokerage firms where clients place purchase or sell orders based on processed information. The product has high information content but not the information-value-added of advice provided by a broker in a full-service brokerage firm.

The sampling strategy was designed to generate a variety of information processing styles, cultures and structures in SMEs of different sizes. This sampling has allowed for the contrasting of results from these varied sources and provided a finer grain appraisal of our model’s process variables and contextual factors. In order to implement this strategy, we chose the nine SMEs along the matrix’s diagonal axis in which information value-added and the information content of the firm’s products are more or less in balance. These nine organizations represent a variety of perceived levels of information intensity. They were initially grouped under three identification tags: SME 1, SME 2 and CB (shown with dotted lines in Fig. 3). They comprise six manufacturing SMEs (SME 2: C, L, U, S, D and W) a medium-sized commercial bank (CB) and two business service SMEs (SME 1: A and T). This ascending order along the diagonal of the matrix for the SME 2 group, CB and the SME 1 group reflects our hypothesis that service SMEs are more efficient information processors than their manufacturing counterparts. The firms were selected from the database of the SME Research Institute of the Université du Québec à Trois-Rivières. Table 2 provides some relevant statistics. We met with a total of 55 individuals from our sample of SMEs. Respondents were selected on the basis of their information roles in sales, marketing and pur-

Fig. 3. Information intensity matrix of participating SMEs.


chasing, or their high information processing functions in credit, R&D and pricing. A careful selection had to be made to obtain representatives from strategic, functional and operational levels combined with our criteria. A variety of respondents were targeted: – At the commercial bank (CB) a sample of 12 strategic (Executive Vice-Presidents and VicePresidents), functional (functional VicePresidents and Directors) and operational individuals (Supervisors and Representatives) in credit (high information processing and pricing) and sales (boundary spanners with customers) were interviewed. – At the eight other SMEs individuals consisting of the entrepreneur and his functional managers represented the organization’s boundary spanners, the R&D, engineering or estimating managers represented the high information processing pricing function. An interview protocol was designed on the basis of the seven contextual factors and process variables that we wanted to measure for the purposes of our model. Cues such as innovation and customer complaints were used as performance indicators and triggers to establish a dialogue and a level of trust necessary for the exchange of rich information. Open-ended questions and discussion themes requiring the individual’s interpretation were used as follows: – Recent (maximum 3 years old) success stories within the firm; – Creative or innovative ways to establish an adequate gross margin for the firm; – Creative or innovative ways to handle customer complaints; – Varied sources of innovation within the firm; – Personal sources of information (internal and external);

165

– Sense-making (individually or in group) of fragmentary or ambiguous information. The analysis of the data consisted of an initial review of notes taken during the interviews, listening to the recordings of the interviews and taking note of the salient facts. Based on the interviews and this initial review, five-point qualitative measures (1: none, 5: extensive) were finalized for each of the seven variables and contextual factors. A value was assigned for each process variable and contextual factor for each interview (see Appendix A). The aggregate results for each firm were then discussed and validated with the firm’s CEO. We thus generated a benchmark coding for the eight SMEs and the Commercial Bank. This first phase of coding is summarized in Table 3. We further refined the five-point measures on the basis of the discussions with the CEOs and of a subsequent review of the interview transcripts when they became available six months later. The elapsed time allowed us to take a critical distance from the benchmark coding and to present these results to the participants for a final discussion and validation. We made further refinements to the qualitative measures before a second phase of coding. The second phase comprised three successive adjustments to the results of the benchmark coding, as well as a forced-choice adjustment in order to eliminate all decimals from the final scores. This second phase of coding is summarized in Table 4. The results of our analysis are based on this double review, the rating process, the interpretation of salient facts, the two successive coding phases and the comparison to our interview notes.

5. Results The perceptions that provided the basis for the sample selection proved fairly accurate as evidenced by the

Table 2 Participating organizations SME

Product/Process/Service

Sales (000,000)

Emp.

Remarks

W A CB S L

Plastic parts, blow moulding and casting Customs broker, forwarder and freight agent with warehousing Commercial banking to SMEs and startups Industrial parts and speciality painting Pressure moulding and machining of aluminum parts

65 $ 45 $ portfolio 9 $ bil. 35 $ 30 $

140 450 1,200 90 110

manufacturing service service manufacturing manufacturing

D U C T

Construction materials manufacturer with 8 product lines Machine shop: thermal treating, welding, transmission assy Transmission systems manufacturer Consultants in freight and data management

80 $ 15 $ 20 $ 12 $

250 120 110 35

manufacturing manufacturing manufacturing service

166

I.P. Vaghely et al. / Information transformation: Some missing links Table 3 Coding sequence, first phase

1.

Review of the field notes, listening to the interviews and noting salient facts;

2. 3. 4.

Development and refining of qualitative measures for each of the seven variables and contextual factors; Benchmark coding; Discussion and validation of aggregate results of the benchmark coding with the firm’s CEO. Table 4 Coding sequence, second phase

1. 2. 3. 4.

Review of the interviews and transcripts; first set of adjustments to the results of the benchmark coding; Re-listening to the interviews to validate the transcripts; second set of adjustments to the results; Review of field notes and salient facts of the interviews in order to evaluate and adjust the results based on any discrepancy between the second set of results and the results of the benchmark coding; Elimination of all decimals in the measurements trough a forced choice adjustment using the final, more refined measures as the standard.

movement of each firm from its original position (SME 1 or SME 2) to its final position along the matrix’s diagonal line in Fig. 3. The rankings did not change much, with the exception of the Commercial Bank and W, a plastics manufacturer. This firm has the best information processing practices in this group of SMEs. These rankings were determined by analyzing the information content of the product or service and their information value-added with the firm’s CEO. The proprietary information or know-how required to manufacture the product or provide the service determines its information content, and the proprietary information or know-how required to bring the product or service to market determines its information value-added. These elements also provide good indications of the firm’s potential for product or process innovations. We hypothesized that the service SMEs would be more efficient information processors as evidenced by their initial positioning in the matrix, which is more or less what happened. We did not expect the high level of performance shown by the bank (CB), nor for that matter that the best information practices would come from a manufacturer (W). The two service SMEs (A and T) have both information-rich products and information-intensive product value-added. Table 5 below presents the efficiency scores for contextual factors, and process variables, in decreasing order. Table 6 presents the ranking for process variables and contextual factors. Both of these tables were generated on the basis of a graphic analysis. The graphic analysis is presented in Appendix B. Fifty five interviews within nine organizations rated for seven contextual factors and seven variables yielded 126 indicators. This database was not enough for parametric analysis. We therefore used a graphic approach

[33] to analyze and interpret the results. Further research will increase the database to allow for parametric studies. This graphic analysis shows the level of dispersion or scattering of the results by variables and by contextual factors. Algorithmic information processing provided the highest concentration of scores, and heuristic information processing the lowest. These results are evident in both Tables 5 and 6. In Table 5 the highlighted area in grey shows that the weakest organizations overall are also the weakest in terms of heuristic information processing. In Table 6 the ranking of the information processing variables show that tacit-information processing, weaksignal processing and heuristic information processing are ranked in the last three positions in the 5th, 6th and 7th place. This is where innovation originates. Obviously these areas require remedial action; if these organizations want to increase innovation, they must first increase the level of heuristic information processing 6. Some missing links We will start by examining more closely two results which are new to this area of research, namely the role of the information catalyst and the concept of best practice in information transformation and processing. First, the organizations in which we found a so-called information catalyst (see below) outperformed the information processing level of those without such catalysts. In Table 4 they represent the first five organizations: W, A, CB, S and L. We have also identified W as a best practice in terms of information processing. We will outline some best practice related findings at W, ranked number one in Table 5.


167

Table 5 Summary of results by decreasing scores Summary of results by rank Likert scale ( 1 = none, 5 = extensive) Number of interviews = 55

W 6

A 7

CB 12

S 5

L 4

D 8

U 5

C 4

T 4

Contextual factors Level of influence of organizational structure Level of boundary spanning activity

4 4

3 4

4 4

4 3

3 4

3 4

3 3

4 3

4 2

Level of use of rich information Level of information sharing Level of recognition of information sources Level of competition in industry Level of information alert and trust building culture Information catalysts

4 4 4 4 3 1 catal.

4 4 3 4 4 1 catal.

3 4 3 3 3 2 catal.

4 4 4 3 3 1 catal.

4 2 2 4 3 1 catal.

4 2 2 3 2

3 4 2 3 3

3 2 2 2 2

3 2 3 1 2

Process variables Level of algorithmic processing (use of formulae) Level of strong signals processing Level of archived information use Level of use of feedback loops

4 5 5 4

4 4 3 4

4 4 5 4

4 4 3 4

4 4 3 4

4 4 4 3

4 3 3 2

4 4 4 3

4 4 4 2

Level of tacit information processing Level of weak signal processing Level of heuristic processing (use of trial and error) Rank

4 4 4 1

3 4 3 2

3 3 4 3

4 2 3 4

3 3 4 5

2 3 3 6

3 2 3 7

3 3 2 8

3 2 2 9

6.1. The information catalyst In our interviews we encountered six unusual individuals whom we call “information catalyst”. These individuals treat uncertainty, information equivocality and situation ambiguity with relative ease. They facilitate information transformation and exchange; they process and synthesize rich information effectively along an implicit algorithmic-heuristic continuum in accordance with our model. From a commercial point of view, they are sufficiently knowledgeable about customer needs to recommend alternative and novel product solutions. They are very well apprised of their organization’s capabilities to satisfy customers’ expectations. They are the organization’s information nexus and are often responsible for the preparation of competitive bids. In a manufacturing environment they are usually associated with R&D or cost estimates, so that they have a major impact on the organization’s gross margin. Similarly, in the commercial bank information catalysts are to be found among the firm’s credit officers. Von Krogh, Ichijo and Nonaka [53] also use the term “catalyst” for their so-called “knowledge creation activist”. We could also refer to Anderson and Jack’s

[2] terminology. In their terms, the catalyst acts as both the glue that reinforces interpersonal relationships, and the lubricant that facilitates intercommunication throughout the firm. In some organizations, we found that the catalyst had a somewhat meaningless title, such as “Director of one thing or another”, while his actual job was to be wherever he was needed to facilitate the information transformation process. In the nine organizations we studied they complement the entrepreneur and boundary-spanners as the third leg of the firm’s vital information processors. The organization’s level of information alertness is determined by these three categories of information processors and, to a larger extent, by the information catalyst’s propensity to share information. It is important for the organization to recognize this special ability of the information-catalyst by reinforcing his role as a rich-information disseminator, and by using his special ability to insert such rich-information into product or process innovations and strategy formation to recognize or even generate nascent opportunities. This is what W does so well.

168

I.P. Vaghely et al. / Information transformation: Some missing links Table 6 Ranking of contextual factors and process variables

Contextual factors Level of influence of organizational structure Level of boundary spanning activity Level of use of rich information Level of information sharing Level of recognition of information sources Level of competition in industry

1 2 3 4 5 6

Level of information alert and trust building culture

7

Process variables Level of algorithmic processing (use of formulae) Level of strong signals processing Level of archived information use

1 2 3

Level of use of feedback loops Level of tacit information processing Level of weak signal processing Level of heuristic processing (use of trial and error)

4 5 6 7

6.2. Some elements of best practices – the catalyst in action Some of the “best practice” related findings at W are: 1. A culture of information alertness and information sharing within the organization, which influences the individual’s implicit assumptions and values about information processing: • Timely and accurate information is provided to both members of the management team and users on the production floor; • Problem-solving is used to reduce uncertainty on customer orders including product ecology and ergonomics; • Sense-making through intensive communication of rich information is used to integrate ambiguity in R&D projects; • A “no-nonsense” approach is used to communicate information: there is very little information asymmetry within the management team; • Information is widely shared; information arbitrage is not tolerated within the organization, and seldom if ever practiced with customers; • An ongoing product quality and process improvement program formalizes tacit knowledge. 2. Proactive use of weak signals from customers and from the commercial and technological environment:

• Weak-signal sources are used to tap into rich technical and commercial information through ongoing relationships with institutions of higher learning; • The firm’s reputation and product quality are considered as high-value intangible assets and are used both proactively and reactively to attract and retain customers; • All feasible bids are evaluated irrespective of plant loadings; they are considered a good source of competitive information and constitute a trigger for rich-information exchange. 3. Information source recognition and reciprocity to foster information sharing through the organizational structure and a culture of trust: • Members of the top management team are all involved with customers in one way or another; • Rich-information symbols such as plant capabilities, product know-how and product and process R&D are a source of pride; • Extensive documentation of information, both electronic and hard copy, is made available on an intranet or trough the filing system; • Key individuals are always able to insert rich information into the strategy formation process in order to take advantage of nascent opportunities; • Autonomy is encouraged within the management team and among intensive users of information such as engineering, R&D and quality control. This creates trust and close communications among team members; • Heuristic (trial-and-error) and algorithmic (formal procedures) information processing are recognized and used appropriately according to need. 4. The quick-paced environment calls for the fast resolution of ambiguity; top management is continuously involved in integrating ambiguity trough sense-making and, sometimes, improvisation exercises; 5. Uncertainty is reduced by individual managers and by individuals in high information impact groups, both intuitively and through problemsolving approaches, and communicated at regular management meetings.


7. Conclusion Our research highlights a number of variables that allow for the measurement of an organization’s level of information alertness. First, the findings of our study support the hypothesis that alert, proactive individuals process information along an implicit algorithmic – heuristic continuum. Thus, routine information is processed at the algorithmic end of the continuum, while new information – particularly innovationrelated information – is processed at the heuristic end. In a competitive industry, market forces mandate information alertness towards customers, suppliers, competitors and technological substitutes – to name but a few targets. This is the role of strategic intelligence in which information provides competitive advantage. Secondly, the organization’s culture shapes the individual’s level of information alertness through his/her implicit assumptions on information processing. An efficient way to recognize important information sources is through information reciprocity. In this respect, the major sources of information in our sample are the entrepreneur, the information boundary-spanner and the information catalyst. Information sharing, especially by the information catalyst, has a leveraged effect on the level of innovation and information alertness within the organization. Finally, the most important aspect and the most difficult to measure is the ability of the entrepreneur, the boundary spanners and the information catalyst to continuously insert rich-information into the

169

decision-making process, be it to innovate or to take advantage of nascent opportunities. On the other hand, our research also sheds light on some of the most important obstacles to efficient information processing. First, most individuals have difficulty recognizing weak-signals, or distinguishing competitive information from channel noise. Similarly, some individuals cannot change mental models which no longer fit or explain their environment. In this context, discrepant information is “rationalized away” or simply discarded if it does not fit the existing model. When this happens, the individual retains only those elements of information that fit his/her existing model, even though it may no longer be relevant to the environment. More often than not, these individuals do not realize their own cognitive shortcomings and are not able to recognize, let alone process, competitive information. Finally, a few individuals do not even listen for signals from their customers or suppliers. Practical benchmarking applications will further strengthen the model through refinements of its contextual factors. Comparing information processing and transformation behavior between information-intensive and information-poor organizations, such as consultants and commodity manufacturing, raises interesting areas of new investigation. Appendix A Such instruments were used to measure the seven information processing variables and the seven contextual factors (Table A).

Table A Table outlining the measure used to evaluate the processing of weak signals 1.

Lack of all of the key elements of sense-making in an ambiguous and changing environment; absence of a dominant logic of communication and information exchange; no capacity to detect weak signals (strategic environment scanning); no visible interest in processing unfamiliar, fragmentary, equivocal information from any source; no innovation.

2.

Lack of most of the key elements of sense-making in an ambiguous and changing environment; absence of a dominant logic of communication and information exchange; little capacity to detect weak signals (strategic environment scanning); little visible interest in processing unfamiliar, fragmentary, equivocal information from any source; very little innovation restricted to “me-too” copies.

3.

Presence of some of the key elements of sense-making in an ambiguous and changing environment; absence of an information culture with some elements favorable to the detection of weak signals; occasional processing of unfamiliar, fragmentary, equivocal information from various sources; a little innovation mostly restricted to imitation.

4.

Presence of most of the key elements of sense-making in an ambiguous and changing environment; a dominant logic of communication and information exchange with some elements favorable to the detection of weak signals (information networks, environment scanning); explicit processing of anticipatory, fragmentary, equivocal information from various sources; innovations are quickly disseminated and implemented.

5.

Strategic mix of the key elements of sense-making in an ambiguous and changing environment; a dominant logic of communication and information exchange with several elements favorable to the detection of weak signals; concerted and proactive processing of anticipatory, qualitative, ambiguous, fragmentary information from various sources; innovations are created, disseminated and implemented rapidly.

170

I.P. Vaghely et al. / Information transformation: Some missing links Table B Scatter analysis

Appendix B This table (Table B) shows the level of dispersion of the ratings for each variable and contextual factor. The frequency of rating is indicated by the size of the bubble. The level of rating from values 1 to 5 is indicated on the vertical scale in the graph. The combination of the frequency of a specific value determined by the size of the bubble times the value of the rating shows the dispersion of the rating for each factor (variable and contextual factor). This analysis helped determine the ranking of each factor.

References [1] S. Al-Hawamdeh, Knowledge management: re-thinking information management and facing the challenge of managing tacit knowledge, Information Research 8(1) (2002). http://InformationR.net/ir/8-1/paper143.html. [2] A.R. Anderson and S.L. Jack, The articulation of social capital in entrepreneurial networks: a glue or a lubricant, Entrepreneurship & Regional Development 14(3) (2002), 193–210.

[3] H.L. Ansoff, Managing strategic surprise by response to weak signals, California Management Review 18(2) (1975). [4] S. Blanco and H. Lesca, Business intelligence: integrating knowledge into the selection of early warning signals, Working paper, University Pierre Mendès France, Grenoble, 2002. [5] F. Bouthillier and K. Shearer, Understanding knowledge management and information management: the need for an empirical perspective, Information Research 8(1) (2003). http://InformationR.net/ir/8-1/paper141.html. [6] C.W. Choo, The knowing organization: how organization use information to construct meaning, create knowledge and make decision, International Journal of Information Management 16(5) (1996), 329–340. [7] C.W. Choo, The Knowing Organization: How Organizations Use Information to Construct Meaning, Create Knowledge and Make Decisions, Oxford University Press, New York, 1998. [8] C.W. Choo, Information Management for the Intelligent Organization: The Art of Scanning the Environment, ASIS Monograph series, Medford, 1998. [9] W. Cohen and M.D.A. Levinthal, Absorptive capacity: a new perspective on learning and innovation, Administrative Science Quarterly 35 (1990), 128–152. [10] W.E.D. Creed and R.E. Miles, Trust in organizations: a conceptual framework linking organizational forms, managerial philosophies, and the opportunity cost of controls, in: Trust in Organizations, F.M. Kramer and T.R. Tyler, eds, Sage, Thousand Oaks, 1996, pp. 16–39.

I.P. Vaghely et al. / Information transformation: Some missing links [11] M. Csikszentmihàlyi and K. Sawyer, Creative insight: the social dimension of a solitary moment, in: The Nature of insight, R.J. Sternberg and J.E. Davidson, eds, MIT Press, Cambridge, 1995, pp. 329–363. [12] R.L. Daft and R.H. Lengel, Organizational information requirements, media richness and structural design, Management Science 32(5) (1986), 554–571.

171

[31] A. Korzybski, The role of language in the perceptual processes, Perception: An Approach to Personality, Ronald Press, New York, 1951. [32] F.M. Kramer and T.R. Tyler, Trust in Organizations, Sage, Thousand Oaks, 1996. [33] M.B. Miles and A.M. Huberman, Qualitative Data Analysis: An Expanded Sourcebook, Sage, Thousans Oaks, 1994.

[13] T.H. Davenport, Information Ecology, Oxford University Press, Oxford, 1997.

[34] H. Mintzberg, Mintzberg on Management, Free Press, New York, 1989.

[14] T.H. Davenport, D.W. DeLong and M.C. Beers, Successful knowledge management projects, Sloan Management Review 29(2) (1998), 43–57.

[35] I. Nonaka and H. Takeuchi, The Knowledge Creating Company, Oxford Press, New York, 1995.

[15] T.H. Davenport and L. Prusak, Working Knowledge, Harvard Business School Press, Cambridge, 1998. [16] T.H. Davenport, J.G. Harris, D.W. DeLong and A.L. Jacobson, Data to knowledge to results: building an analytic capability, California Management Review 43 (2001), 117–138. [17] D.W. DeLong, T.H. Davenport and M.C. Beers, What is a knowledge management project? in: Cap Gemini Ernest & Young Center for Business Innovation, Cambridge, 1997. http://www.cbi.cgey.com/pub/docs/KMproject.pdf. [18] J.R. Galbraith, Designing Complex Organizations, AddisonWesley, Reading, 1973. [19] J.R. Galbraith, Organization Design, Addison-Wesley, Reading, 1977. [20] J.R. Galbraith, Designing Organizations, Jossey-Bass Publishers, San Francisco, 1995. [21] D.A. Gioia, Symbols, scripts and sense making, in: The Thinking Organization: Dynamics of Organizational Social Cognition, A. Sims and D.A. Gioia, eds, Jossey Bass, San Francisco, 1986, pp. 49–74.

[36] I. Nonaka, The knowledge creating company, Harvard Business Review (November–December) (1991). [37] I. Nonaka, A dynamic theory of organizational knowledge creation, Organization Science 5(1) (1994), 14–37. [38] M. Polànyi, Personal Knowledge, Routledge & Keegan, London, 1962. [39] M. Polànyi, The Tacit Dimension, Doubleday and company, New York, 1966. [40] M.E. Porter, Competitive Strategy, Free Press, New York, 1980. [41] M.E. Porter and V.E. Milar, How information gives you competitive advantage, Harvard Business Review 63(4) (1985), 149–160. [42] H. Rao, J. Rhagav, S. Varghese, F. Lin, D. Robey and G.P. Huber, Hemispheric specialization, cognitive differences, and their implications for the Decision Support Systems; responses, MIS Quarterly 16(2) (1992), 145. [43] D.M. Schweiger, Measuring manager’s minds: a critical reply to Robey and Taggart, Academy of Management Review 8(1) (1983), 143–151.

[22] D.A. Gioia and C. Manz, Linking cognition and behaviour: a script processing interpretation of vicarious learning, Academy of Management Review 10(3) (1985), 527–539.

[44] H.A. Simon, From substantive to procedural rationality, in: Method and Appraisal in Economics, S.J. Latis, ed., Cambridge University Press, Cambridge, 1976, p. 129.

[23] D.A. Gioia and P.P. Poole, Scripts in organizational behaviour, Academy of Management Review 9(4) (1984), 449–459.

[45] H.A. Simon, Making Management Decisions: the role of intuition and emotion, The Academy of Management Executive 1(1) (1987), 57–64.

[24] M. Granovetter, The strength of weak ties, American Journal of Sociology 78 (1973). [25] S.G. Harris, Organizational culture and individual sense making: a schema-based perspective, Organization Science 5(3) (1994), 309–321. [26] G.P. Huber, A theory of the effects of advanced information technologies on organizational design, intelligence, and decision making, Academy of Management Review 15(1) (1990), 47–71.

[46] A.L. Stinchcombe, Information and Organizations, University of California Press, Berkeley, 1990. [47] W. Taggart and E. Walenzi, Assessing rational and intuitive styles: a human information processing metaphor, Journal of Management Studies 27(2) (1990), 149–172. [48] M.L. Tushman and T.J. Scanlan, Characteristics and external orientations of boundary spanning individuals, Academy of Management Journal 24(1) (1981), 83–98.

[27] G.P. Huber, Cognitive style as a basis for MIS and DSS designs: much ado about nothing, Management Science 29(5) (1983), 567.

[49] M.L. Tushman and T.J. Scanlan, Boundary spanning individuals: their role in information transfer and their antecedents, Academy of Management Journal 24(2) (1981), 289–305.

[28] P.N. Johnson-Laird, Mental Models, Harvard University Press, Cambridge, 1983.

[50] A. Tversky and D. Khaneman, Judgment under uncertainty: heuristics and biases, Science 185 (1974), 1124–1131.

[29] S. Keisler and L. Sproull, Managerial responses to changing environments: perspectives on problem sensing from social cognition, Administrative Science Quarterly 27(4) (1982), 548– 570.

[51] A. Tversky and D. Khaneman, Judgement under Uncertainty: Heuristics and Biases, in: Judgement under Uncertainty: Heuristics and Biases, D. Khaneman, P. Slovic and A. Tversky, eds, Cambridge University Press, Cambridge, 1982, pp. 3–20.

[30] J. Kirk, Information in organisations: directions for information, Information Research 4(3) (1999). http://InformationR.net/ir/4-3/paper57.html.

[52] N. Venkataraman, The value center, in: Strategic Planning for Information Systems, J. Ward and J. Peppard, eds, Wiley, Chester, 2002, p. 207.

172


[53] G. Von Krogh, K. Ichijo and I. Nonaka, Enabling Knowledge Creation, Oxford University Press, New York, 2000. [54] K. Weick, Sense Making in Organizations, Sage, Thousand Oaks, 1995. [55] M. Zeleny, Management support systems: Toward integrated knowledge management, Human Systems Management 7(1) (1987), 59–70.

[56] M. Zeleny, Production of knowledge: moving from data and information to knowledge and wisdom, in: Human System Management: Integrating Knowledge, Management and Systems, World Scientific, USA, 2005, pp. 1–77. [57] A.S. Zhara and G. George, Absorptive capacity: a review, reconceptualization, and extension, Academy of Management Review 27(2) (2002), 185–203.