Survey design

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Chapter 4: Survey of KM Researchers and

provide a basis for integrating them. Those objectives can be translated into the following propositions:

1. There are four major perspectives on KM, namely information, human, computing and strategy.

a. Information perspective: knowledge is mostly seen as codified/codifiable content and transferable expertise/experience, and KM usually means to facilitate access to information, expertise and so-called best practices.

b. Human perspective: knowledge is largely interpreted as social practice and collective sense making, and KM usually means to cultivate contexts and facilitate connections that improve practice and sense making.

c. Computing perspective: knowledge is typically regarded as objective and suited to computational approaches, and KM normally means to develop systems/methods that compute knowledge and to build computational models for decision making.

d. Strategy perspective: knowledge is interpreted at the organizational level as capability or asset, and KM typically means to prioritize knowledge valuable to the organization and to design and implement strategies and processes to acquire, create, use and protect it.

2. Each perspective has a distinct combination of typical KM activities and valued individual capabilities.

3. There is a set of KM activities that is typical of all perspectives. There is also a set of individual capabilities that are valued by all perspectives.

4.2.2 Questionnaire design and testing

The main purpose of the questionnaire was to validate the four perspectives on KM and to identify the activities and capabilities associated with each of them.

Besides that, we have kept some principles in mind when designing the questionnaire. First, we aimed at respondents with long experience in KM – either as researchers or practitioners. This meant that they would be considerably busy people, with low availability and willingness to take part in the survey. Thus, we sought to keep the questionnaire as simple and easy to answer as possible: we opted for closed questions only and tried to keep their number small so that the questionnaire could be completed in around 15 to 30 minutes. And second, since we were looking for the respondents’ own understanding of what constitutes KM, we provided no definition of concepts and intentionally left statements open to interpretation. We wanted to have the respondents’ understanding of KM to become manifest in the answers given, and we sought to keep the questionnaire as comprehensive and representative of each approach as possible. However, we had to compromise between the desired comprehensiveness and the required simplicity.

The questionnaire had four major sections (reproduced in Appendix 1). The first two were the most important, exploring the typical KM activities in the first and the associated individual capabilities in the second. The first section was critical and aimed to verify the existence of distinct perspectives on KM. We listed twelve typical KM activities and asked respondents to choose six they would consider priorities in a generic KM effort. The number six was chosen because we planned to group responses into clusters. Too large a number of items and there would be too much overlap between too few clusters; too little a number and there would be too little overlap and too many clusters. Those twelve KM activities were taken from the list derived in the previous chapter; we selected the ones we judged the most relevant for KM and good representatives of each of the four perspectives (Table 4-1).

The second section of the questionnaire presented 36 individual capabilities related to KM, and respondents were asked to rate the degree of relevance of each of them for an effective performance in those six activities chosen in section one.

In the same way, the capabilities were selected from those listed in Chapter 3, seeking a comprehensive coverage of the relevant ones and a balance among those

Table 4-1: List of typical KM activities proposed in the questionnaire¹⁶

I1 Organizing codified knowledge and making it available in repositories I2 Mapping knowledge needs, users and owners, sources and flows I3 Codifying knowledge from experts, teams and experienced employees

H1 Promoting knowledge sharing and transfer (best practices, expertise directory, etc.) H2 Building teams and communities of practice

H3 Promoting creativity and learning

C1 Implementing publication and collaboration systems (portals, groupware, etc.) C2 Implementing decision support systems (business intelligence, expert systems, etc.) C3 Implementing knowledge discovery systems (search, data mining, etc.)

S1 Identifying strategic knowledge and developing strategies for KM S2 Measuring and managing intangible assets (i.e. intellectual capital)

S3 Managing innovation and knowledge creation (R&D, alliances, startups, etc.)

particularly suited to specific perspectives. The list of capabilities were grouped according to six categories – strategic, organizational, knowledge-oriented, technological, interpersonal and personal (Table 4-2). The third section was added to probe on some educational concerns and is not directly linked to the objectives of the survey. We tried to estimate to what extent an education program can develop KM competence and to what extent the concept is universal or context-specific. Finally, the fourth and last section contained questions on respondents’ characteristics, like field of study or practice, period of experience, and so on.

Table 4-2: List of typical individual capabilities proposed in the questionnaire

Strategic

S1 Understanding the organization’s environment (market, competitors, etc.) S2 Understanding the organization’s structure and core business processes S3 Identifying strategic knowledge and providing direction for KM S4 Developing approaches and strategies to advance KM practices

16 The codes in the table indicate the perspective to which the activity is more closely associated with. [In] stands for Information, [Hn] for Human, [Cn] for Computing and [Sn] for Strategy.

Those codes are for analytical purpose and were not presented to respondents.

S5 Evaluating and demonstrating results from KM initiatives

S6 Creating structures and processes for innovation and knowledge creation

Organizational

O1 Understanding the organization’s culture and behavior (beliefs, habits, etc.) O2 Promoting collaboration and creativity

O3 Managing teams and communities

O4 Developing people (coaching, mentoring, etc.)

O5 Initiating and managing organizational change (in structures, processes, etc.) O6 Managing projects, from planning to execution

Knowledge-oriented

K1 Understanding the varied aspects of knowledge and its processes K2 Finding, organizing and distributing relevant knowledge

K3 Mapping knowledge needs, sources and flows, owners and users K4 Designing and managing knowledge repositories

K5 Codifying experience and expertise K6 Assessing and measuring knowledge Technological

T1 Understanding the technological infrastructure existing in the organization T2 Understanding available KM technologies

T3 Using available KM technologies effectively

T4 Assessing needs and recommending KM technologies T5 Developing and implementing KM technologies T6 Administrating and maintaining KM technologies

Inter-personal

I1 Communicating effectively in a variety of situations I2 Leading, influencing and gaining support

I3 Building relationships inside and outside the organization I4 Collaborating and working in teams

I5 Negotiating and solving conflicts I6 Handling politics and power relations

Personal

P1 Strongly believes in KM P2 Initiative and pro-activeness P3 Creativity and inventiveness

P4 Willingness to reflect and learn from experience P5 Perseverance and resilience

P6 Trustworthiness and accountability

The questionnaire was extensively discussed with our supervisor and two colleagues. Other colleagues have provided feedback in several occasions. Along the process, we have carried out some major changes in design and many

revisions in the layout and wording of questions. We conducted a pilot test with four potential respondents and made several minor adjustments after the feedback obtained.

4.2.3 Sampling and data collection

The questionnaire was sent out to 84 KM researchers and practitioners. As mentioned before, we aimed at more experienced researchers and practitioners, since they are presumably more knowledgeable about the breadth and depth of the KM field. We also favored individual contact through personalized messages, instead of posting the questionnaire openly, in order to improve both quality and rate of response. We selected potential respondents by convenience: we targeted well-known authors of research papers on KM, faculty members and lecturers of KM degree programs, and high-profile practitioners – speakers in KM conferences, professionals featured in industry publications and prominent participants in KM mailing lists.

We balanced the sample between representatives from academia and industry, and also from each of the four perspectives on KM. Naturally such judgment about potential respondents is subjective and can be disputed, but we made our best effort to keep the sample evenly represented. Individual messages were sent to 84 potential respondents in early March and a follow-up message was sent two weeks later. Some participants forwarded the questionnaire to their acquaintances, and we cannot tell how exactly how many people were reached by it. From the initial 84 contacts, we received 36 responses, which means a response rate of 43%.

Other 17 responses were received from forwarded questionnaires, providing 53 responses in total. Next, we present the results obtained.