For a macro economist, such as myself, it is refreshing to read articles from other fields from time to time. Below I list the links to papers that received best papers Emerald prizes in soft sciences in 2005-2006. Some of them are insightful indeed:

How successful companies challenge conventional wisdom about the limits to growth

findings:

“The major finding were: that limits to growth are often self-imposed and, as such, can be overcome; firms with the will to be successful growers can break free of perceived constraints related to size, industry boundaries and geographic neighborhood; and despite the widely held belief that mergers and acquisitions inherently destroy value for the acquirer, companies that learn to become successful growers use MandA strategies effectively”.

We could probably extend this reasoning to the entire countries, speed limits are self-imposed indeed. I would be more careful with MandA analogy, though.

An integrated framework for visualising intellectual capital

findings:

“To the client organisation, the ICVC framework proved beneficial in that it enabled senior management to visualise their knowledge resources and how these contribute to organisational value creation. To the project team, the ICVC framework facilitated the identification of organisational knowledge management gaps, highlighting weaknesses in the client organisation’s utilisation of its knowledge resources. The framework provides a structured approach for investigating organisations’ ICMMR practices and locating and analysing these within a strategic context. “

Fast surfing, broad scanning and deep diving: The influence of personality and study approach on students’ information-seeking behavior

findings:

“Three information-seeking patterns – fast surfing, broad scanning and deep diving – emerged from the statistical analyses. Fast surfing could be related to a surface study approach and emotionality, as well as to low openness to experience and low conscientiousness. Broad scanning was linked to extraversion, openness, and competitiveness, whereas deep diving was a search pattern typical of analytical students with a deep and strategic study approach”

Classifying web metrics using the web quality model

findings:

“In this work, a global vision of web metrics is provided. Concretely, it was found that about 44 percent of metrics are related to “presentation” and that most metrics (48 percent) are usability metrics. Regarding the life cycle, the majority of metrics are related to operation and maintenance processes. Nevertheless, focusing on metrics validation, it was found that there is not too much work done, with only 3 percent of metrics validated theoretically and 37 percent of metrics validated empirically. “

Adoption of behaviour: predicting success for major innovations

findings:

“Initial results are encouraging and suggest that the approach may provide qualitatively better results than the existing methods when applied to major innovations”.

If you never heard of memetics take a look at this article.

Making sense of e-commerce as social action

findings:

“Demonstrates that e-commerce gives rise to increasing competition among the dealers, decreasing prices and migration of competition to price, decreasing profitability of the average dealer, and erosion of traditional sources of competitive advantage. Moreover, e-commerce emancipates and empowers vehicle purchasers while reducing the power of automobile dealers”.

Capitalizing on the internet opportunity

findings:

“Managers view the internet positively as it will reduce customer service costs and allow firms to tighten relationships with customers. The positive potential outweighs the negative potential of increased competition and new pricing models. However, not all will benefit. “

Countering negative country-of-origin with low prices: a conjoint study in Vietnam

findings:

“The results show that price cuts by the Korean brands do little to attract customers away from the higher perceived quality Japanese brands. Perceived quality differentials are too large to gain most customers who buy Japanese brands at any realistic level of price cuts” .

I agree. For example the biggest problem that Korean cars have is poor image. They break less often than most Japanese and German cars, but who wants to be seen in Hyundai. Maybe next James Bond movie should be sponsored by Hyundai. “Hi, my name is Bond. James Bond. I drive Hyundai.” How does it sound? Weird.

Modeling consumer satisfaction and word-of-mouth: restaurant patronage in Korea

findings

“Key findings include the ability of the consumer service value scale to account for utilitarian and hedonic value, the role of functional and affective service environment components in shaping consumer satisfaction and future patronage intentions and the relative diagnosticity of positive affect. “

I am adding above link to show, that some people just cannot communicate without making things incomprehensive. I read key findings several times and could not understand what is means (and I have MA in math and PhD in economics). So I read the paper. The basic message is that they asked 300 people whether the liked their dinner or not, and why. The conclusion is that restaurant should look nice to make people feel good. If I got something wrong please someone correct me.

Doing knowledge management

findings:

“The critical analysis indicates that the use of tools and methods associated with KM does not imply that interventions using them are KM interventions, and most “KM projects” are probably interventions of other types. The analysis also illustrates a pattern of intervention that can serve as the basis of a long-term systematic strategy for implementing KM. “

It is a very useful paper I need to read in full later. It does show that many people think they implement knowledge management strategies, while in fact they do not. If you do not have time to read the fiull paper below I include Copernic 25% summary (form Copernic Summarizer, it usually does a good job in getting key points)

Copernic 25 percent summary:

“Purpose — Knowledge management (KM) as a field has been characterized by great confusion about its conceptual foundations and scope, much to the detriment of assessments of its impact and track record.

The purpose of this paper is to contribute toward defining the scope of KM and ending the confusion, by presenting a conceptual framework and set of criteria for evaluating whether claimed KM interventions are bona fide instances of it or are interventions of another sort.

Design/methodology/approach — Methods used include conceptual evaluation and critique of a variety of types of “KM interventions” and presentation of a detailed analysis of an unambiguous case (The Partners HealthCare case) where KM has been successful.

Findings — The critical analysis indicates that the use of tools and methods associated with KM does not imply that interventions using them are KM interventions, and most “KM projects” are probably interventions of other types.

The analysis also illustrates a pattern of intervention that can serve as the basis of a long-term systematic strategy for implementing KM. Originality/value — This is the first detailed examination of whether KM is really being done by those who claim to be doing it.

It should be of value to all those who think about the scope of organizational learning and KM, and who care about unbiased assessments of its performance.

It is a natural function in human organizations, and it is being done all of the time in an informal distributed way by everyone undertaking activity in order to enhance knowledge production and integration tasks.

But whether formal interventions claiming the label “KM” are bona fide instances of KM practice is another matter entirely.

To answer that question, we need to have clear, non-contradictory ideas about the nature of knowledge, knowledge processing, and KM.

And to have those, we need to get beyond the notion that we can do KM by just doing anything that may have a positive impact on worker effectiveness while calling that thing “KM”.

And when we undertake KM projects, we must evaluate the contributions of our interventions to the quality of knowledge processing and knowledge outcomes.

That calls for tough, precise thinking about knowledge processing, knowledge, and the impact on these that our interventions are likely to have.

The nature of KM as a type of activity or a set of processes In an earlier “Viewpoint” in TLO (Firestone and McElroy, 2004a) we presented a three-tier framework (see Figure 1) of business processes and outcomes (also see McElroy, 2003; Firestone, 2003a; Firestone and McElroy, 2003a, b), distinguishing operational business processes, knowledge processes, and processes for managing knowledge processes.

For example, if a knowledge manager changes the rules affecting knowledge production, then the quality of knowledge claims may improve.

The context of KM: CASs, DECs, and learning What is the conceptual context of this three-tier conceptualization of KM?

The three types of processes distinguished in the three-tier framework occur within complex adaptive organizational systems that are characterized by distributed continuous learning and problem solving, self-organizing, and emergent phenomena produced by dynamic processes of interacting autonomous agents that are non-deterministic in character (Holland, 1998).

These phenomena include social, geo-physical, economic, and cultural conditions, and also social network effects presented to individuals in the form of transactions directed at them by other decision makers who collectively constitute the emergent network pattern (see Figure 2) of the organizational CAS (Firestone and McElroy, 2003a, Chs 2 and 4).

Decisions are part of a sequence of cognitive operations that have been described in the literature in slightly varying terms, using many names (e.g. the organizational leaning cycle (Ackoff, 1970), the experiential learning cycle (Kolb and Fry, 1975; Kolb, 1984), the adaptive loop (Haeckel, 1999), and others).

We call it the Decision Execution Cycle (DEC), which includes planning, acting, monitoring, and evaluating behaviors (Firestone, 2000a).

Figure 3 illustrates the phases of DECs.

This is the single-loop learning (SLL) of Argyris and Scho ¨n (1974).

In addition, DECs play a key role in initiating and performing double-loop learning (DLL) (Argyris and Scho ¨n, 1974) — learning of new knowledge (in the form of general predispositions and rules, and specific knowledge) that requires problem solving and is not just a matter of perception or direct apprehension or comprehension.

During monitoring and evaluating, the individual determines the degree to which results match the expectations accompanying decisions, and when mismatches occur, the seriousness of the mismatch from both the factual and evaluative perspectives (see Figure 4).

gap is what we mean by a “problem”, and recognition of it is what we mean by “problem recognition”.

Among the results of error elimination is knowledge, which we will discuss briefly below.

However, DECs may also form patterns of interpersonal collaboration, cooperation, and conflict, and these patterns may also integrate into knowledge processes.

When they do, we can differentiate between problem formulation, developing alternative solutions, and error elimination, on the one hand, and problem claim formulation, knowledge claim formulation, and knowledge claim evaluation in order to distinguish the individual level of knowledge processing from the interpersonal and collective levels, respectively.

Knowledge integration is about syst em-level knowledge claims being communicated from one part of the distributed organizational knowledge base (DOKB), the configuration of previously produced knowledge claims, beliefs and belief predispositions in the organization (Firestone and McElroy, 2003a) (see Figure 5), to another.

Through the DOKB, both knowledge claims and belief phenomena are accessible in varying degrees to individual decision makers in DECs, within both the business processing environment, and the knowledge and KM processing environments.

The knowledge life cycle (KLC) and the business processing environment The clouds in the figure illustrate the ubiquity of DOKB content in the various processes.

We have also used arrows from the primary DOKB cloud to illustrate its influence on all processes, but are limited to showing its universal influence in two dimensions, while at the same time showing the breakdown of primary knowledge processes into sub-processes and other details in the figure.

Since Figure 6 focuses on a process view, it glosses over the lower DEC level of analysis.

Figure 7 makes it clear that the match/mismatch process occurs in DECs and not simply at the higher level of business processes.

This point is very important for our later analysis of the Partners HealthCare case.

Information is a non-random structure within a system, indicating future interactive potentialities, either originating along with it, or acquired or developed by it in the course of its interacting with and responding to its environment and the problems generated by that interaction (Bickhard, 1999).

Note that this definition does not require correspondence between information and the environment.

Nor does it assert that information is encoded in some simple cause-and-effect fashion, but leaves room for emergent information in the context of interaction with the environment.

All of these are undoubtedly important, but the most important aspect of information is whether its influence on behavior enhances the ability of the system using it to adapt.

Evolution provides such correspondence by selecting for those life forms that fit the environmental constraints in which they live.

Errors in genetic information are eliminated over time by the environment, along with the organisms that contain them (Popper, 1987).

Since the most important aspect of information is correspondence with reality, the most important measures of information networks are those that evaluate this correspondence.

Thus, the most important measures we can develop describing knowledge claim (information) networks are measures that help us to evaluate knowledge claims, and that brings us to “knowledge”.

Tested, evaluated, and surviving structures of information in physical systems that may allow them to adapt to their environment (e.g. genetic and synaptic knowledge).

Are KM practitioners “doing KM”, or are they “practicing” KM by helping fields or techniques such as IT, CM, CRM, data warehousing, social network analysis, storytelling, CoPs, data mining, quality management, human resources, and “knowledge” cafe´s to “colonize” it?

KM has been subject to it from the beginnings of the discipline, when it was frequently characterized as being about “delivering the right information to the right people at the right time”, through use of the right IT tool.

Thus, KM was viewed as an activity that encompassed deploying the right IT tool in the enterprise and, often, using it to “manage knowledge” as characterized above.

In that spirit, data warehousing, data mining, business intelligence (BI) and online analytical processing (OLAP), business performance measurement (BPM), CRM, enterprise resource planning (ERP), collaboration management, groupware, search and retrieval applications, CM, semantic network/text mining applications, document management, image management, e-conference applications, e-learning applications, expertise locators (Yellow Pages), best practices database applications, and enterprise information portals (EIPs), have all been characterized as KM tools, and projects involving the deployment and use of one or another of these tools have been characterized and reported as KM projects.

Third, in our view, the association of the idea of “KM intervention” with any of the above tools is frequently an instance of “conceptual drift”, mistaking KM for other forms of activity.

Such drift is harmful to KM because, ultimately, it confuses the record of KM performance and therefore prevents an evaluation of KM based on that performance.

More recently, social network analysis (SNA) (Cross and Parker, 2004) is being used to discover the structure of relationships in existing communities, as well as the existence of clusters of social relationships that can form the nuclei of new communities not yet self-organized.

Another technique that has been popular is the knowledge cafe ´ (Isaacs, 1999), a technique in which participants circulate among multiple small interactive groups carrying on a discussion of a selected topic and sharing their knowledge over the course of a day.

Additional techniques include “knowledge” auditing and mapping, value network analysis (Allee, 2003), Group decision-making processes, influence network analysis, various quality management techniques, and, of course, cultural analysis.

Whether such an intervention is a bona fide KM intervention depends on whether it is a policy, program, or project targeted at enhancing knowledge processing and through knowledge processing, knowledge outcomes, and ultimately business decisions and processes.

In other words it depends on whether, and on the extent to which, the intervention fits the pattern expressed in the three-tier framework (see Figure 1), and is targeted at the KLC (see Figure 6) as compared with the extent to which it fits the pattern characteristic of other forms of management activity.

These considerations suggest that we apply the following criteria in deciding the question of whether an intervention is a KM intervention or something else: (1) Is the intervention aimed at having an impact on problem recognition in DECs and business processes, on the KLC, or on some aspect of KM itself?

If the intervention is aimed at some aspect of knowledge integration in the KLC, or the DOKB itself, does it incorporate a way of telling the difference between knowledge and information so that its impact is aimed at knowledge integration and not just at information integration?

The simple idea behind this type of solution is that the quality of decisions will improve if “best practices” are captured, made available to knowledge workers, and reused by them.

One of us has distinguished these two types of portals sharply since early in 1999 (Firestone, 1999).

Portals, like best practices systems, do not provide a way of distinguishing information from knowledge.

As a consequence, any support they provide for integration functions such as broadcasting, sharing, teaching (through e-learning applications), and search and retrieval, is restricted to information, rather than knowledge, integration.

Nor do portals generally provide targeted support for problem recognition, or for individual and group learning, or for knowledge claim evaluation.

Nor do they provide targeted support for any of the KM activities distinguished in criterion 7 above.

There are the remaining possibilities that portal applications provide the required support for information acquisition and for knowledge claim formulation.

But in the area of information acquisition, portal applications have shortcomings in the extent to which they support search results that are specifically relevant to problems.

Although search technology has improved substantially since portals originated in 1998, it is widely recognized that they do not provide results that are sufficiently targeted on problems without a great deal of continuous interaction between humans and the portal.

Moving to knowledge claim formulation, many portal interventions focused on CM or collaborative capabilities do not provide support for idea management, semantic networking, formal modeling, simulation, or other techniques supporting alternative formulations.

However, portals with strong structured data analysis/on-line analytical processing/business intelligence capabilities support knowledge claim formulation including the specification of alternative claims.

These types of portals support knowledge processing and therefore interventions that deploy such portals are, indeed, KM interventions.

In brief, while portals provide a wide range of generalized support for information processing and management, portals focused on content management provide little specific support for knowledge processing as outlined in the criteria mentioned earlier.

It is not impossible for portals to provide support in many of these areas, and hence for KM interventions based on portals to enhance knowledge processing.

All it requires is that portal interventions incorporate portlets targeted at enhancing KLC functions.

Turning to some examples from the area of social techniques for KM interventions we have listed, we think it is also the case that CoPs, storytelling, and SNA-based interventions may or may not be KM interventions, depending on the details of the specific intervention that is planned and implemented.

Since CoP-based interventions are among the favorite initiatives of knowledge managers, we begin by asking the question, when is a CoP intervention not a KM intervention?

If the CoP intervention is aimed at enhancing knowledge sharing, but fails to provide a way of distinguishing CoP-produced knowledge from CoP-produced information, then, we claim, it is not a KM intervention but an information management (IM) intervention.

KM interventions that attempt to introduce the use of storytelling as a technique of knowledge sharing, share with CoP interventions the difficulty that they do not help to distinguish knowledge from information in what is shared.

A technique experiencing increasing popularity this year is SNA (Cross and Parker, 2004), and one well-known KM blogger (Pollard, 2004) has even suggested that KM be re-invented as “social network enablement”, meaning that KM interventions would aim at enhancing opportunities for social networks to form and thrive.

The Partners HealthCare case In July, 2002, authors Tom Davenport and John Glaser published a case study in Harvard Business Review (Davenport and Glaser, 2002) involving a KM implementation at Partners HealthCare in Boston.

The decision to invest in KM at Partners was largely driven by the cost of medical errors in healthcare, especially as reported by the Institute of Medicine (IOM) (Kohn et al., 1999) in 1998.

At Partners, medical errors, as measured by them in 1995, showed that “more than 5 percent of patients had adverse reactions to drugs while under medical care; 43 percent of those inpatient reactions were serious, life threatening, or fatal.

Of the reactions that were preventable, more than half were caused by inappropriate drug prescriptions” (Davenport and Glaser, 2002, p. 5).

Moreover, “A study of the six most common laboratory tests ordered by physicians in Brigham and Women’s surgical intensive care unit found that almost half of the tests ordered were clinically unnecessary” (Davenport and Glaser, 2002, p. 6).

He decides to treat the infection with ampicillin.

As he logs on to the computer to order the drug, the system automatically checks her medical records for allergic reactions to any medications.

She’s never taken that particular medication, but she once had an allergic reaction to penicillin, a drug chemically similar to ampicillin.

Goldzer decides to override the computer’s recommendation and prescribe the original medication, judging that the positive benefit from the prescription outweighs the negative effects of a relatively minor and treatable rash.

When the system began to remind physicians that patients requiring bed rest also needed the blood thinner heparin, the frequency of prescriptions for that drug increased from 24 percent to 54 percent.

The doctor’s level One of the purposes of the Partners’ system was to reduce errors by upgrading knowledge at the point where doctors make decisions to order tests, medications, or other forms of treatment.

Knowledge at the point of decision was to be upgraded by way of the new system’s ability to broadcast others’ knowledge to the decision maker, and also by the decision maker thereby using, or not, the shared knowledge to question his/her own decisions or actions.

In other words, from the point of view of our frameworks, the system is, in the first instance, about eliminating or reducing errors in DECs by increasing the frequency with which doctors question, critically evaluate, and recognize problems in the decisions they are contemplating.

The system is supposed to make doctors look for problems in their views, and if they find them, initiate problem solving (that is, KLCs) of their own, in the expectation that this will increase the quality of the beliefs that survive and inform their order entry decisions.

Thus, in terms of the DEC framework, when Dr Goldzer uses his previous knowledge to decide to treat Mrs Johnson’s infection with ampicillin, he acts on the decision by ordering the drug.

The system prepares to intervene in Dr Goldzer’s DEC between his action and the production of a result for him to monitor and evaluate.

There were two options for the system in this situation.

If it had not found any contra-indicating history (or other previous knowledge) related to Goldzer’s order, his order would have been processed, and the results of Goldzer’s DEC would have been the administration of ampicillin to Mrs Johnson and its downstream effects.

The option applicable to Goldzer’s actual situation, however, was that the knowledge claims in the system conflicted with his order, so the system intervened in Goldzer’s DEC and brought Mrs Johnson’s previous allergic reaction to his attention by presenting him with a knowledge claim about that as the result of his decision.

Thus, it integrated the organization’s knowledge into his DEC, and forced him to evaluate critically his belief that the right thing to prescribe for Mrs Johnson was ampicillin, against the knowledge claims it presented to him.

Before he is allowed to proceed, however, he is prompted by the system to integrate into the organizational DOKB knowledge claims and meta-claims explaining why he falsified (over-rode) the system’s knowledge claims.

The organizational perspective When we look at the order entry system from an organizational perspective, we see knowledge production being performed by committees of experts.

They evaluate what goes into the system, and the claims they approve receive the ” organization as knowledge to be integrated into order entry DECs when triggered by specific transactions.

The committees are designated authorities for knowledge production and knowledge claim evaluation at the organizational level, directed at solving the problem of medical error reduction in order entry.

They perform KLCs, evaluate, and select the knowledge claims that are formally designated as organizational knowledge, and that will be made available through the system for integration into the order entry DECs.

The system, however, works in such a way that the centralization of knowledge production in the committee is balanced by the participation of all physicians in knowledge claim formulation and evaluation in the context of their participation in the order entry system.

Partners did this because it recognized the fallibility of organizational knowledge produced by the committees, the need to involve the doctors and their knowledge in solving problems and adding knowledge claims to the DOKB, and the need to view system interventions in decisions made by the doctors, as acts of knowledge integration, intended to strengthen monitoring and evaluation and problem recognition in the DEC, rather than knowledge imposition.

In the end, the Partners’ system is stronger because it is a distributed problem-solving system, in which the committees, through the system, help the doctors to recognize that there are problems with some of their orders.

However, by sometimes insisting on their decisions and giving the committees feedback on their own reasons for doing so, the doctors, again through the system, are providing knowledge claims to the committees, as well as critical evaluations of the committees’ recommendations (i.e. their knowledge claims) to them, in the form of the reasons they provide for over-riding such recommendations.

In this regard, the Partners’ system not only injects organizational knowledge at key decision points in the order entry process, but also integrates knowledge processing functionality at the same time and place in the form of knowledge claim formulation and knowledge claim evaluation.

On the one hand, the system broadcasts organizational knowledge to the physicians and supports further information retrieval as well, while on the other it engages them in various aspects of organizational knowledge processing.

Finally, Davenport and Glaser, in their account, characterize the Partners’ system as embedding KM into the business process, or “baking specialized knowledge into knowledge work”.

The end state of the strategy is attaining a form of organization called the Open Enterprise, which, theory suggests, is an environment providing maximal support for sustainable innovation, problem solving, and adaptation.

Continuous criticism of previously generated ideas by any of its agents (knowledge claim evaluation).

In formulating a KM strategy and an associated program, one needs systematically to specify DECs and, where necessary, work flows, or business processes that can produce highly negative business outcomes if errors are made.

Identification of high risk DECs should be followed by prioritization of them according to risk, taking into account, ease and expense of intervention, and likelihood of success.

If you can, make interventions that embed new knowledge processing functionality within existing IT-based business applications supporting DECs, work flows or business processes.

This is needed to encourage questioning of previous individual-level knowledge in DECs, which, in turn, can encourage increased problem recognition, individual KLCs, and error reduction in key decisions.

Firestone, J. and McElroy, M. (2003c), Excerpt #1 from The Open Enterprise: Building Business Architectures for Openness and Sustainable Innovation, KMCI Online Press, Hartland Four Corners, VT, available at: www.dkms/com, www.macroinnovation.com and www.kmci.org Firestone, J. and McElroy, M. (2004a), “Viewpoint: organizational learning and knowledge management: the relationship”, The Learning Organization, Vol. 11 No. 2, pp. 177-84.

McElroy, M.W. (2003), The New Knowledge Management: Complexity, Learning, and Sustainable Innovation, KMCI Press/Butterworth-Heinemann, Burlington, MA. Pollard, D. (2004), “Social networking, social software and the future of knowledge management”, How to Save the World, available at: http://blogs.salon.com/0002007/2003/05/28.html#a251 Popper, K.R. (1945), The Open Society and its Enemies, Routledge & Sons, London.”

The complexity of leading

findings:

“It is not always recognised that the outcomes of leadership action are seldom predictable, that individuals may respond quite differently from the expectations of their performance, and that if the occasion so warrants the influence of the leadership role may be replaced by the authority of management – with the one in charge demonstrating competency in both roles. “

What it takes to be a leader? Can I become one? These are good questions. Above paper shows that answer can be really complicated. But yes, you certainly ‘ve got a chance, just pick your type.