The impact of manager philosophy on knowledge management systems

This paper analyses how information about managers and technology can be used to provide those managers with a system that is congruent with their needs. In particular, using McGregor's Theories X and Y philosophies, managerial needs are elicited and then contemporary knowledge management technologies, including intelligent agents, and the way they are implemented, are analysed to determine how they meet those manager needs. Different knowledge management technologies are found to be important to manifesting the requirements of particular management philosophies. For example, ‘Theory X’ appears consistent with use of intelligent agents to ‘monitor’ behaviour. This leads to the concept of ‘technology congruence’, where the choice of the technology ultimately is tied to which view of the world the manager employs. Copyright © 2010 John Wiley & Sons, Ltd.     

Models, knowledge creation and their limits

Instead of modelling socio-economic situations as mechanical systems with fixed, predictable behaviour, we now see that socio-economic systems are really complex systems, in which various possible structural changes can occur giving rise to a range of different possible futures. This necessary future uncertainty automatically imposes an uncertainty on the precise pay-off that any particular action or decision that an agent may take. Because of this, the decisions that agents will make are also uncertain and this poses limits to our ability to model socio-economic systems and therefore to the knowledge that we can have at any time about the future. Because of this constant knowledge decay, what matters in real world situations of markets and business is the generation of new, current knowledge. Contrary to traditional science in which the natural laws are independent of who knows them, in social and economic systems, knowledge of system behaviour decays over time, and is in any case used up when it triggers new behaviour in the system. Several examples of evolutionary market systems are presented which demonstrate how knowledge is constantly created and destroyed, and the problem of change, innovation and design are shown to be part of a ‘boundedly rational’ view in which imperfect search gives rise to ‘good enough’ behaviour. All of this is a radical departure from the traditional approach that falsely believe in the optimisation of designs, behaviours and profits. Complexity tells us that we must accept risk and uncertainty and work loosely, keeping our options open as much as possible.     

Can knowledge based systems be designed to counteract deskilling effects?

Recent calls in the information systems research community argue that we know intelligent systems deskill users, and future research should focus on how to design systems that do not deskill, rather than continue to examine whether the phenomenon occurs. This should be a wakeup call for public accounting firms focused on implementing restrictive audit support systems, which leads to de-skilling of novice accounting professionals. Our research focuses on redesigning knowledge-based systems to facilitate expertise development and counteract the de-skilling effects that result from use of such systems. Specifically, we manipulate the design of the system interface by providing information cues in a screen format consistent with expert knowledge representations and manipulate automatic provision versus voluntary use of explanations for users during task completion. Results show that after using the knowledge-based system to complete a series of reenacted client engagements over a three-day period, both the interface design manipulation and automatic provision of explanations had a positive effect on novice accounting professionals’ development of expert-like knowledge structures. The results of the study have important implications for the development of knowledge-based systems intended to support accounting professionals’ (and other knowledge workers’) expertise development processes.     

End-user access to multiple sources: incorporating knowledge discovery into knowledge management

The End-User Access to Multiple Sources—Eams system—integrates given information sources into a knowledge management system. It relates the world of documents with the database world using an ontology. The focus of developing the Eams system is on the acquisition and maintenance of knowledge. Hence, in both worlds, machine learning is applied. In the document world, a learning search engine adapts to user behaviour by analysing the click-through-data. This eases the personalization of selecting appropriate documents for users and does not require further maintenance. In the database world, knowledge discovery in databases (KDD) bridges the gap between the fine granularity of relational databases and the actual information needs of users. KDD extracts knowledge from data and, therefore, allows the knowledge management system to make good use of already existing company data—without further acquisition or maintenance. A graphical user interface provides users with a uniform access to document collections on the Internet (Intranet) as well as to relational databases. Since the ontology generates the items in the user interface, a change in the ontology automatically changes the user interface without further efforts. The Eams system has been applied to customer relationship management in the insurance domain. Questions to be answered by the system concern customer acquisition (e.g. direct marketing), customer up- and cross-selling (e.g. which products sell well together), and customer retention (here, which customers are likely to leave the insurance company or ask for a return of a capital life insurance). Documents about other insurance companies and demographic data published on the Internet contribute to the answers, as do the results of data analysis of the company's contracts. Copyright © 2003 John Wiley & Sons, Ltd.     

Limits to the economy of knowledge and knowledge of the economy

In this essay, we explore the view that the growth of knowledge is a complex evolutionary process. We pay particular attention to the division of knowing in a knowledge economy, to the restless nature of knowledge and to the principle of variation and selection that makes the growth of knowledge an evolutionary phenomenon. The foundations for this discussion are located in Adam Smith's suggestion that the most fundamental aspect of the division of labour is the division of knowledge, and the consequential existence of roundabout and combinatorial ways of producing knowledge. This notion of roundaboutness also connects our discussion with the writings of the Austrian school of economists. It is this school of thought that has come closest to understanding the open-ended and evolutionary nature of knowledge based economic activity. The theory of complex adaptive systems, when applied to the economic and social domain, has enabled us to confront the distinction between knowledge and the institution of social understanding. We map the difference between knowledge, which is private, and understanding, which is social. If knowledge is a characteristic of individuals, understanding then becomes an emergent phenomenon arising from the interaction between individuals in specific contexts. Understanding has boundaries it has components that possess great heterogeneity and is sustained by the connections of information flow. It is a socially distributed process whose growth is dependent on a systemic context, on the way a given set of individuals interact to share information and thus to further develop their idiosyncratic knowledge. Recognising that understanding is necessarily distributed leads to the insight that economic activity, which is necessarily social, depends on shared understandings, that is to say, correlated knowledge. This provides a clue as to the unpredictability and unevenness of knowledge accumulation, and of course the unpredictability of capitalism as a knowledge driven system.     

Robust monetary policy with imperfect knowledge

We examine the performance and robustness properties of monetary policy rules in an estimated macroeconomic model in which the economy undergoes structural change and where private agents and the central bank possess imperfect knowledge about the true structure of the economy. Policymakers follow an interest rate rule aiming to maintain price stability and to minimize fluctuations of unemployment around its natural rate but are uncertain about the economy's natural rates of interest and unemployment and how private agents form expectations. In particular, we consider two models of expectations formation: rational expectations (RE) and learning. We show that in this environment the ability to stabilize the real side of the economy is significantly reduced relative to an economy under RE with perfect knowledge. Furthermore, policies that would be optimal under perfect knowledge can perform very poorly if knowledge is imperfect. Efficient policies that take account of private learning and misperceptions of natural rates call for greater policy inertia, a more aggressive response to inflation, and a smaller response to the perceived unemployment gap than would be optimal if everyone had perfect knowledge of the economy. We show that such policies are quite robust to potential misspecification of private sector learning and the magnitude of variation in natural rates.     

The portfolio of knowledge required by industry specialist auditors

While a number of studies have shown the superior performance of industry specialist auditors, prior research has not examined the underlying knowledge categories reflective of a specialist. This study aims to identify the range of knowledge required to be a successful industry specialist auditor, and the ways in which this knowledge is and might best be acquired. A multi-method approach was employed, including the use of a free-list task to identify important categories of knowledge, and a structured questionnaire. The questionnaire examined several issues including current knowledge of specific items, extent to which specialist knowledge is reflected in the firm's support systems, and methods by which this knowledge was, and could be, acquired. Participants, designated insurance industry specialists from a Big 4 firm, identified a large range of knowledge items, and there was a large degree of consensus between the results arising from the two knowledge identification research methods utilised. The results also revealed a number of perceived deficiencies in the level of current knowledge and the extent to which specialised knowledge is reflected in support systems. Few differences were found in the knowledge requirements of the two industry sub-specialisations, life insurance, and property and casualty insurance. Except for industry and economic factors, on-the-job experience was found to be the most prevalent method of gaining industry specialist knowledge. However, there was support for greater formal training and improved firm support systems for several specific knowledge items.     

The market price of fiscal uncertainty

Recent fiscal interventions have raised concerns about US public debt, future distortionary tax pressure, and long-run growth potential. We explore the long-run implications of public financing policies aimed at short-run stabilization when: (i) agents are sensitive to model uncertainty, as in Hansen and Sargent (2007), and (ii) growth is endogenous, as in Romer (1990). We find that countercyclical deficit policies promoting short-run stabilization reduce the price of model uncertainty at the cost of significantly increasing the amount of long-run risk. Ultimately these tax policies depress innovation and long-run growth and may produce welfare losses.     

The effect of task complexity and expert system type on the acquisition of procedural knowledge: Some new evidence

Prior studies in accounting examining the effect of expert system use on procedural knowledge acquisition have reported that providing feedback in the form of rules, text explanation, or examples had no incremental effect on procedural knowledge acquisition. This finding is contrary to literature in psychology regarding the impact of feedback on knowledge acquisition. One of the reasons that the accounting literature may not show that feedback impacts knowledge acquisition relates to task complexity. Simple tasks require little processing on the part of the decision-maker and may not lead to learning effects even when feedback is provided. On the other hand, feedback coupled with complex tasks that require increased processing may help the decision-maker learn more about task completion causing knowledge acquisition to occur. This study extends previous research by examining whether task complexity and the type of feedback provided by an expert system affect the acquisition of procedural knowledge. This issue is important since procedural knowledge acquisition may differ between certain combinations of tasks and expert system types. This study manipulates both task complexity and the type of feedback provided by an expert system. The findings indicate that task complexity plays a major role in the acquisition of procedural knowledge for expert system users. Subjects in the expert system groups who evaluated the complex cases acquired a significantly greater amount of procedural knowledge than subjects who evaluated the simple cases. As predicted, there was no difference in amount of procedural knowledge acquired between subjects in the control groups regardless whether the task was simple or complex. Results also qualify findings of previous research in noting that acquisition of procedural knowledge only significantly differed between expert system users and the control group when the task was complex. These findings indicate that, of the two components, feedback and task complexity, task complexity plays the more important role in affecting procedural knowledge acquisition.     

Risk forecast as work practice: between codified and practical knowledge

If the use of meteorological data has progressively expanded in tackling different sources of risk, less developed is by contrast a reflection on how meteorological systems apply in local contexts and to what extent that locality may affect the use and the content of forecasting recipients. By focusing on a wildfire forecasting, I show how forecasting practice cannot be reduced to the implementation of meteorological devices; it rather takes shape in the articulation between the technical device and different sources of knowledge – tacit, practical and ‘profane’. This articulation work, this study gives account of, reveals some specific challenges in the introduction of forecasting systems in risk management.     

A fuzzy logic-driven multiple knowledge integration framework for improving the performance of expert systems

To maintain a high performance in an ill-structured situation, expert systems should depend on multiple sources of knowledge rather than a single type. For this reason, we propose multiple knowledge integration by using a fuzzy logic-driven framework. Types of knowledge being considered here are threefold: machine, expert and user. Machine knowledge is obtained by a back- propagation neural network model from historical instances of a target problem domain. Expert knowledge is related to interpreting the trends of external factors that seem to affect the target problem domain. User knowledge represents a user’s personal views about information given by both expert knowledge and machine knowledge. The target problem domain of this paper is one-week-ahead stock market stage prediction: Bull, Edged-up, Edged-down, and Bear. Extensive experiments with real data proved that the proposed fuzzy logic-driven framework for multiple knowledge integration can contribute significantly to improving the performance of expert systems. Copyright © 1998 John Wiley & Sons, Ltd.     

Short-sale constraints and price bubbles

Miller (1977) demonstrated that if investors have heterogeneous beliefs and short sales are restricted, trade of a security will disproportionately reflect positive information, generating a price bubble. As this intuition applies most relevantly to short intervals of trade, a question arises as to the longevity of such a bubble. In this paper, I argue that a bubble effected by short-sale constraints persists only if agents cannot distinguish between order flow caused by positive information or order flow caused by the constraints. If the constraint is common knowledge, it should have no effect on the long-term pricing of the stock. If, however, the constraint is random and unknown, a price bubble may form.     

Specific knowledge and performance measurement

I examine optimal incentives and performance measurement in a model where an agent has specific knowledge (in the sense of Jensen and Meckling) about the consequences of his actions for the principal. Contracts can be based both on “input” measures related to the agent's actions and an “output” measure related to the principal's payoff. Whereas input‐based pay minimizes income risk, only output‐based pay encourages the agent to use his knowledge efficiently. In general, it is optimal to use both kinds of performance measures. The results help to explain some empirical puzzles and lead to several new predictions.     

Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index

This paper proposes the hybrid knowledge integration mechanism using the fuzzy genetic algorithm for the optimized integration of knowledge from several sources such as machine knowledge, expert knowledge and user knowledge. This mechanism is applied to the prediction of the Korea stock price index. Machine knowledge is generated by applying neural networks to technical indicators, while expert knowledge and user knowledge are generated from the evaluations of external factors that affect the stock market. Cooperative knowledge is generated from the weighted sum of these sources using a genetic algorithm. Experimental results show that the hybrid mechanism can provide more accurate and less ambiguous results. It means that this mechanism is useful in integrating knowledge from multiple sources for an unstructured environment such as the stock market. Copyright © 2004 John Wiley & Sons, Ltd.     

Decision making using time‐dependent knowledge: knowledge augmentation using qualitative reasoning

In this paper we propose a method to enhance the performance of knowledge‐based decision‐support systems, knowledge of which is volatile and incomplete by nature in a dynamically changing situation, by providing meta‐knowledge augmented by the Qualitative Reasoning (QR) approach. The proposed system intends to overcome the potential problem of completeness of the knowledge base. Using the deep meta‐knowledge incorporated into the QR module, along with the knowledge we gain from applying inductive learning, we then identify the ongoing process and amplify the effects of each pending process to the attribute values. In doing so, we apply the QR models to enhance or reveal the patterns which are otherwise less obvious. The enhanced patterns can eventually be used to improve the classification of the data samples. The success factor hinges on the completeness of the QR process knowledge base. With enough processes taking place, the influences of each process will lead prediction in a direction that can reflect more of the current trend. The preliminary results are successful and shed light on the smooth introduction of Qualitative Reasoning to the business domain from the physical laboratory application. © 2001 John Wiley & Sons, Ltd.     

The interplay of reificative and participative processes of customer knowledge creation: An exploratory study of commercial lending

A key competency in commercial lending is the ability to generate information about customers in order to separate the credit-worthy from those who are not. Traditionally, this was performed through proximal, face-to-face interaction. Nevertheless, in order to rationalise the process and in an attempt to overcome information asymmetries, credit-scoring systems are increasingly being used. A common assumption has been that these systems are replacing proximal customer evaluations with distal ones. Some researchers have suggested, however, that proximal interaction and contextual, non-standardiseable elements will be of continued importance for assessing customers. In the light of this, the role of credit-scoring systems as a way of overcoming proximal interaction may be questioned. The aim of this paper is to explore the customer knowledge creation process in commercial lending. Departing from an approach inspired by phenomenology, this paper argues that customer knowledge is created through an interplay of proximal, embodied participation and reification. Reified knowledge generated from credit-scoring and documentation is instilled with meaning through participative knowledge created in proximal face-to-face meetings, which include discursive practices (dialogue, negotiation, joint meaning-making) as well as non-discursive practices (atmosphere, artefacts, perceptual cues).     

The General Motors' variation-reduction adviser: an example of grassroots knowledge management development

One approach to developing knowledge management systems is to seed the system in key communities of practice and then encourage its customization and spread throughout the enterprise by local (‘grassroots’) initiative. This has the benefit of worker buy-in and adaptation of the local systems to their workflows. The concept is that, in exchange for some loss of control and standardization, the grassroots systems will be used and appreciated and will grow into an enterprise-wide system. In this paper, we discuss this approach and how it is emerging in General Motors' Variation-Reduction Adviser, a manufacturing knowledgesharing and lessons-learned system. Copyright © 2003 John Wiley & Sons, Ltd.     

The high cost of accurate knowledge

Many business thinkers believe it's the role of senior managers to scan the external environment to monitor contingencies and constraints, and to use that precise knowledge to modify the company's strategy and design. As these thinkers see it, managers need accurate and abundant information to carry out that role. According to that logic, it makes sense to invest heavily in systems for collecting and organizing competitive information. Another school of pundits contends that, since today's complex information often isn't precise anyway, it's not worth going overboard with such investments. In other words, it's not the accuracy and abundance of information that should matter most to top executives--rather, it's how that information is interpreted. After all, the role of senior managers isn't just to make decisions; it's to set direction and motivate others in the face of ambiguities and conflicting demands. Top executives must interpret information and communicate those interpretations--they must manage meaning more than they must manage information. So which of these competing views is the right one? Research conducted by academics Sutcliffe and Weber found that how accurate senior executives are about their competitive environments is indeed less important for strategy and corresponding organizational changes than the way in which they interpret information about their environments. Investments in shaping those interpretations, therefore, may create a more durable competitive advantage than investments in obtaining and organizing more information. And what kinds of interpretations are most closely linked with high performance? Their research suggests that high performers respond positively to opportunities, yet they aren't overconfident in their abilities to take advantage of those opportunities.     

Do alliances promote knowledge flows?

We explore the role of interfirm alliances as a mechanism for sharing technological knowledge. We argue that knowledge flows between alliance partners will be greater than flows between pairs of nonallied firms, and less than flows between units within single firms. Using patent citations as a proxy for knowledge flows, we find results that are consistent with these expectations. We then explore how firm characteristics affect knowledge flows within alliances and find positive effects due to technological, geographic, and business similarities between partners. We use alliance data from MERIT, patent data from the USPTO, and firm data from Compustat.