Joan Ernst van Aken

Design Science and Organization Development Interventions Aligning Business and Humanistic Values

This article discusses a design science approach to organizational development (OD) resulting in some new perspectives about how OD interventions might support more effective organizational change. These relate to the way in which the formal organization is redesigned, the way this design is translated by the members of the organization into their own roles and routines, and the way in which subsequent organizational learning produces the intended performance improvement. The background, nature, and characteristics of design science and design science research are discussed, and using a design science perspective, a process model of planned change projects is presented. Drawing on a case in planned change, it is argued that a design science perspective can provide a powerful combination of the original strengths of OD in human behavior and planned change based on humanistic values on one hand and design competencies involving both humanistic and business values on the other.This article discusses a design science approach to organizational development (OD) resulting in some new perspectives about how OD interventions might support more effective organizational change. These relate to the way in which the formal organization is redesigned, the way this design is translated by the members of the organization into their own roles and routines, and the way in which subsequent organizational learning produces the intended performance improvement. The background, nature, and characteristics of design science and design science research are discussed, and using a design science perspective, a process model of planned change projects is presented. Drawing on a case in planned change, it is argued that a design science perspective can provide a powerful combination of the original strengths of OD in human behavior and planned change based on humanistic values on one hand and design competencies involving both humanistic and business values on the other.

Managing learning in informal innovation networks: overcoming the Daphne-dilemma

In this article we discuss the nature and productivity of informal innovation networks, i.e. informal collaborative arrangements between organizations engaged in product or process innovation. Such networks can be used in any phase of the innovation process, but their informal nature makes them especially suited for its fuzzy front end. We explore their potential in technology exchange and learning on the basis of a combination of organization network theory and knowledge management theory. We discuss issues in network governance and network operational management and discuss the basic dilemma – which we named the Daphne-dilemma – facing attempts to improve the productivity of informal innovation networks: too little management effort may lead to under-exploitation of their potential and poor productivity, but too much management effort may destroy their informal nature and hence their creative and explorative potential.

Reinventing the future: adding design science to the repertoire of organization and management studies

Mainstream research on organization and management is largely modeled after the natural sciences and the humanities. It aims at understanding social systems and, as such, has produced a vast knowledge base. However, this knowledge base has been criticized as fragmented and lacking relevance for practice. Two recent developments have produced the possibility of reinventing the future of organization and management studies: the increasing interest in design science research and in evidence-based management. First, we discuss how the actor perspective and solution-orientation of design science research can lead to more relevant research output. Second, we explore how the use of this research output in evidence-based management – typically via a design-oriented research synthesis – can decrease fragmentation by drawing together various strands of research and, moreover, lead to more relevant and interesting research questions, aiming at understanding as well as solution design. Adding design science research to the repertoire of organization and management studies can create a virtuous cycle toward a future in which these studies matter more than they do now.

DEVELOPING WHAT CUSTOMERS REALLY NEED: INVOLVING CUSTOMERS IN INNOVATIONS

Involving users in new product design and development is in itself not a new phenomenon. Academic research on the subject, however, is relatively new. Since this new millennium research on user involvement in innovations has multiplied, exhibiting that firms who actively involve users in their innovation process can benefit from this initiative, even though literature also warns us for relying too much and too actively on the participation of customers in innovation. However, research is mainly descriptive and explanatory of nature, and is seldom a prescription for firm managers on what to do should they decide to involve their customers in an innovation initiative. This paper addresses this omission in literature and tries to give some first building blocks for a protocol for firms that want to involve their customers in innovations. This protocol will be the result of the authors further research on literature, previously executed projects by the author, expert interviews and a design and development process, in continuation of this paper.

Design Science: Valid Knowledge for Socio-technical System Design

Information systems are socio-technical systems, i.e. complex arrangements of hardware, software, procedures, data and people. The literature provides ample design science to support the design of the technical components but much less so for the social ones. Main stream social science does not produce much design science as research in these disciplines is dominated by the paradigm of the explanatory sciences for which physics is the role model. In these disciplines there is as yet very little research on the basis of the paradigm of design sciences like medicine and engineering. The reasons for this include a fundamental methodological problem, which emerges if one wants to develop design science for the social world and which is not present in engineering and largely absent in medicine. It’s cause is human agency. However, this problem can be solved. This article will show how one can develop design science, i.e. valid knowledge produced by rigorous research to support designing, for the social world. The nature of the aforementioned methodological problem will be discussed, followed by a presentation of a research strategy by which one can overcome this problem. This strategy, objective and systematic social experimental learning, will be discussed and will be illustrated by some examples from the field of organization and management. Finally some suggestions are given on the development of design science for the social components of information systems.

Theory-informed diagnosis of business problems

Introduction This chapter concerns the second step of the problem solving cycle: the analysis and diagnosis step. Our starting point in this chapter is that the first step, the problem definition step, has been finished. Thus, in one way or another, the problem has been defined, some of its potential causes and consequences identified, the assignment and the problem solving approach determined. The purposes of the diagnosis are to validate the business problem, to explore and validate the causes and consequences of the problem and to develop preliminary ideas about alternative directions to solve the problem. At the end of the diagnostic step, students must be convinced and able to convince others (practitioners as well as academics) of the validity of the problem, its causes and its consequences. During the previous step of the problem solving cycle, students are relatively passive. In this diagnostic step the students need to be much more active: they must actively develop and execute strategies to explore and check the ideas of the organization members that came up during the definition of the problem. Students who have little practical experience in field problem solving need more guidelines than more experienced consultants. However, compared to the previous step of problem definition, considerably more activities are situation-specific, which makes it more difficult to provide general guidelines. In our experience, the diagnostic step causes many problems for students. They often do not know how to start and tend to continue the explorative character of the previous step, while part of the analysis strategy should aim at validation, instead of exploration. Thus, valid conclusions must be drawn at the end of the diagnosis, and they have to be carefully prepared. The result of the diagnostic step is a problem-oriented or problem- and process-oriented theory on the analysis subject. The explanatory and/or descriptive theory pertains to one case, and so we refer to it as an N = 1 theory. Since it is a theory, it should meet the quality standards mentioned in Chapter 13.

Problem Solving in Organizations: Problem solving projects

Introduction In Chapter 2 we saw that student field projects in business and management can be based either on the empirical cycle, aimed at developing descriptive or explanatory knowledge, or on the problem-solving cycle, aimed at developing solutions for field problems. For the remainder of this handbook we discuss the methodology for the second type of project: the problem-solving project. It is a design-oriented and theory-informed methodology. There are, of course, other problem-solving strategies, and our methodology is not necessarily the best one for each and every business or organizational problem; it has a delineated application domain (see Section 3.5). In this chapter we discuss first the foundations of this methodology and the types of field problems for which this methodology is appropriate. Then we discuss the nature of the problem-solving project, its basic set-up and the various modes in which student projects can be executed. We conclude by presenting the characteristics of professionally executed field problem-solving (FPS) projects, which are quite different from projects that aim to produce descriptive and explanatory knowledge. Rational Problem Solving This design-oriented and theory-informed methodology for business and organizational problem solving builds on the traditions of rational problem solving (Visscher and Visscher-Voerman, 2010). Systematic enquiry into problem solving, aimed at uncovering general principles, started with the work of Herbert Simon, Allen Newell and colleagues in the 1950s (see, for example, Newell and Simon, 1972). Their work strongly influenced research in cognitive science, artificial intelligence, management science and economics. A related stream of research developed on organizational decision-making, which had, in its formative years of the 1960s and 1970s, a predominantly rational orientation (see, for example, Simon, 1960). An especially representative example of this approach is the book by Kepner and Tregoe (1981). However, our design-oriented methodology differs from this rational problem-solving or decision-making approach in two significant aspects. First, we see business problem solving not as a purely technical-economic activity. The issue is not to design a smart solution, but to realize performance improvement for a certain business system. This always involves organizational change, a change in organizational roles and routines, often with accompanying changes in perceptions and attitudes. With regard to organizational change, we follow Tichy (1983), who maintains that significant (in his words ‘strategic’) planned organizational change has to be managed simultaneously in the technical, political and cultural subsystems, using, respectively, technical, political and cultural interventions.

Problem Solving in Organizations: Designs and designing

Introduction Designing is, obviously, a key activity in design-oriented problem solving. In the business and organizational field designing means social system design: you design actions or new roles, relations and processes to be executed by people who operate in organizations in systems of existing roles, relations and processes. We all have a certain understanding of engineering design. However, intuitively we feel that social system design is different. This is true, there are significant differences. At the same time there are also significant similarities. Because at present engineering design is more developed than social system design, these similarities mean that we can learn a lot from engineering design for social system design. Therefore we will give, in Sections 12.2–12.5, some general design theory for designing material artefacts. By ‘general design theory’ we mean design theory, independent of the particular artefact being designed (for further details, see van Aken, 2005b). In Section 12.6 we discuss social system design using the general design theory of the previous sections, but also showing the significant differences between material and social system design. These derive largely from the way the design is realized, but these fundamental differences also reflect on the nature of the design to be made, the way it is to be made and the ways in which the design is represented. The chapter concludes with a discussion on the paradigmatic starting points involved in social system design. These paradigmatic starting points concern conceptions on the nature of (material or social) reality, the nature of knowledge we can get on this reality and the methods we can use to acquire this knowledge (Guba and Lincoln, 2000). Designing Material Artefacts: Designs and Designing The Design The first question, then, is: what is a design? A design can be defined as a model of an entity-to-be-realized, as an instruction for the next step in the creation process. This entity can be an object or a process. The model can take various forms, such as a drawing or a set of drawings, but can also have various other forms, such as a text, a flow chart, a scale model, a computer 3D representation and so on.

Problem-solving in Organizations: Problem-solving projects in organizations

The nature of business problem-solving projects The objective of this handbook is to discuss the methodology of business-problem solving (BPS) projects, carried out by business students. Examples of such projects are: – improving the delivery performance of the spare part inventory control of a capital goods company; – developing a cost control system for a distribution centre of a postal service; – improving the performance of a recently introduced e-procurement system for a small company; – developing a decision support system for the allocation of resources to research and design projects for a small, high-tech company; – developing a system for measuring the performance of a marketing and sales department; – improving the effectiveness and efficiency of training courses for the human resources management department of a large company; – developing a system for measuring the reliability of new software in a software development department; – improving the quality control system of a production department by introducing statistical process control. Business problem-solving projects are started to improve the performance of a business system, department or a company on one or more criteria. Ultimately it should impact the profit of a company (or a comparable overall performance indicator if it is a not-for-profit-organization), but usually the actual objectives of a BPS project are of a more operational nature, related to the effectiveness and/or efficiency of operational business processes. The approaches discussed in this handbook can generally also be used for business improvement projects of a more strategic nature, although we do not discuss the additional technical-economic, political and social complexities of such projects here.

Problem-solving in Organizations: Qualitative research methods

Qualitative Versus Quantitative The literature on methodology distinguishes between qualitative and quantitative research methods. The term ‘qualitative’ does not refer to the quality of methods. Qualitative methods are those that are oriented towards the discovery of qualities of things – that is, the properties of objects, phenomena, situations, people, meanings and events. In contrast, quantitative methods are oriented towards the number or amount of these qualities. This chapter discusses a number of qualitative research methods that can be used in FPS projects. For quantitative research methods, we refer the reader to textbooks on social science methodology, such as those by Cooper and Schindler (2014) and Hair et al. (2005). Imagine marketing managers who would like to know the opinions of potential customers about a television commercial. They could ask some respondents to talk freely about their feelings with regard to the commercial, their associations, what they like about it and what they do not, and so on. Such a study would be qualitative in nature, since it aims to uncover the characteristics of people, in this case their attitude towards the commercial. The same marketing manager could also use a standardized questionnaire and ask respondents to express the degree to which they understand and like the commercial on a fivepoint scale. Such a study would be quantitative in nature, since it measures the value of a particular property in numbers. Qualitative research methods are particularly appropriate for studying people, groups, organizations and societies – for example, if you want to learn how people interpret their own situation, what their goals in life and work are and what strategies they employ to reach those goals. Such understanding is needed in most business problem-solving projects. Because there can be large and multifaceted differences between people and situations, it is often not effective to employ (only) a standardized measuring instrument. Qualitative research methods are relevant within more fields than just the social sciences. The natural and technical sciences use qualitative methods as well. Hendrik Casimir, a respected physicist and long-time director of Philips Laboratories, has stated: For although it is certainly true that quantitative measurements are of great importance, it is a grave error to suppose that the whole of experimental physics can be brought under this heading.