سیستمهای تکنیک اجتماعی: به سوی یک رویکرد یادگیری سازمانی

By means of three design principles (the sociotechnical criterion, the principle of minimal critical specification and the principle of joint optimization of the technical and social system), STS as a design theory is related to four organizational performance indicators (price, quality, flexibility and innovation). As a diagnostic theory, STS helps to find contingencies between environmental demands and work design. The diagnoses result in sets of STS practices. It is argued that as long as price and quality are the only important performance criteria, STS practices have little to offer and their contributions will be only at the job level. If flexibility is of importance, STS has much more to offer, on the job level as well as the organizational level. The same is true for when innovation is a relevant indicator, in which case STS practices may also help to ‘design’ processes, such as mutual trust among workers and diversity with respect to attitudes, abilities and cognitions. It is argued that the dominant performance indicators have changed in a cumulative way from efficiency, via quality and flexibility towards innovation and learning. In accordance with these changes, the STS principles are extended with the concept of organizational learning.

In this paper, we will present the sociotechnical systems (STS) theory as a diagnostic theory and as a design theory. We define STS as an integral theory of work design and quality of working life (QWL). By means of three principles of STS, we will diagnose what kind of work design may help organizations achieve four different patterns of performance indicators. In other words, with a specific pattern of performance indicators in mind, we will depict a work design contingent on these three principles. Considering STS as a practical design theory, we will examine whether STS practices, such as job enrichment, job enlargement and self-managing teams, fit in the diagnosis or whether non-STS practices seem to be more appropriate. Our primary aim is to contribute to the development of STS theory and more specifically to extend it to the phenomenon of organizational learning. Moreover, the links we make between performance patterns and the success of STS practices may aid the understanding of previous conflicting research outcomes. In this section, we will systematically introduce our framework. First, we will describe the three diagnostic principles and following this we will consider four patterns of performance indicators. Since the early years of STS theory, many different organizational principles have been launched, which we may find, amongst others, in the works of Emery, 1969 and Emery, 1978, Herbst (1974), Cherns (1987), Pasmore (1988), and Pava (1986). We will determine the value of STS in terms of dealing with different performance indicators by means of three of these principles in particular, the sociotechnical criterion, the principle of minimal critical specification and the principle of the joint optimization of the technical and the social system. The sociotechnical criterion deals with the control of variance and states that variances should be controlled as near to their point of origin as possible (Cherns, 1987). The sociotechnical criterion was incorporated in STS from systems theory, where it was referred to as ‘the principle of requisite variety’ (Ashby, 1969). According to this principle, to manage environmental demands successfully, an organization should have enough means to transform the input of information, materials and parts into the output that it desires, that is, only variety can beat variety. The principle of minimal critical specification refers to the following: define as little as possible how a worker should perform tasks, but provide just enough directives to ensure that he or she is able to perform the task properly while still allowing for the employee’s personal contribution (Cherns, 1987 and Morgan, 1986). This refers particularly to local autonomy and decentralized control, which will result in enriched jobs and empowered workers. The joint optimization principle deals with the fact that STS endeavors to consider both the social and the technical system simultaneously. The technical system refers to the production structure, the technical equipment and to systems from the field of information and communication technology. The social system refers to human resources, job design and to the control structure. We will discuss the relevance of STS in the light of four performance indicators: price, quality, flexibility and innovation. Kumpe and Bolwijn (1994) have discussed these performance indicators and have placed them in a historical perspective. They argue that until the 1960s, price was the only leading objective and that in the 1970s, quality also became an important indicator. In the 1980s, the need for flexibility grew and in this day and age, they consider innovation to be the major value-added criterion. Nowadays, more and more organizations have to deal with highly dynamic environments and complex and dramatically changing transformation processes, making flexibility and innovation key issues for most firms (Volberda, 1996). Kumpe and Bolwijn consider these performance indicators to be cumulative, that is, first only price was the leading indicator, then price and quality, and so on. We will follow this cumulative approach and we will not detract from their historical perspective, although we think that not all of the four performance indicators are significant for every organization nowadays and that, for example, we may still find firms whose only leading competitive strength is price. In the first of the four sections that follow Section 2, we will focus on firms which operate in markets which predominantly demand low-priced goods, making efficiency the main organizational issue. In Section 3, we will discuss markets on which quality is also an important demand characteristic. In Section 4, we will look at what STS has to offer when firms seek (certain types of) flexibility. In Section 5, we will discuss organizations which face demands for innovation and customization, making the ‘learning organization’ the main issue. Besides the performance indicators above, in each section we will consider the QWL, which may be considered to be the original leading STS objective (e.g. Emery, 1969, Trist and Murray, 1993 and Pasmore, 1995). Following the argument that flexibility and innovation are key indicators nowadays and therefore deserve most intention, we will move more quickly through the sections on ‘price’ and ‘quality’ than through those concerning ‘flexibility’ and ‘innovation’. Within each section, the diagnoses pertaining to the usefulness of STS with respect to attaining specific performance indicators will be followed by STS design practices. More specifically, we will consider how the three principles may be helpful in designing jobs and organizational structures that will facilitate the realization of these performance indicators. We realize that in engineering and technology management there are many useful non-STS related manufacturing practices that have been developed to cope with specific environmental demands. Our focus, however, will be on the way in which STS practices may help to deal with each of these demands, and we will refer to some of the prominent coping strategies, especially when our diagnosis indicates that STS practices have little or insufficient to offer.

The main issue we have dealt with in this paper is the usefulness of STS as a diagnostic and design theory, one that can help managers select the appropriate practices given a particular set of performance indicators. We have done this by relating four patterns of performance indicators (efficiency, quality, flexibility, innovation) to three sociotechnical principles (the sociotecnical criterion, the principle of minimal critical specification and the principle of joint optimization). We have summarized this by means of a few keywords in Table 1. These keywords, such as ‘minimal variety’ or ‘no empowered jobs’, should not be interpreted as meaning that STS supports them, but that just applying STS principles in the case of specific performance indicators evokes such practices.In the balance of this section, we will deal with some implications of our paper and especially focus on how STS may mature further and contribute to recent and prospective developments. Before doing so, we first want to state that we confined our analyses to contingencies between demand patterns and management practices and ignored important issues such as the labor market, technological innovations, globalization, political factors and cultural differences. Therefore, the contingencies we described are non-deterministic. The same is true for STS as a design theory. In several sections of this paper we mentioned non-STS practices, some of which may nevertheless be easily incorporated in STS design applications, and which may well contribute to attaining specific performance indicators. However, we think that what makes STS different is that it emphasizes organizational performance as well as QWL in contrast to the other practices. If we consider performance indicators to be primarily an organization or management interest and QWL to be a worker interest, our arguments indicate that both these interests do not match very well if efficiency and quality are the only critical performance indicators. Moreover, in these circumstances STS practices seem to pertain mainly to the job level. When flexibility and/or innovation arises these interests seem to align much better. In the latter case, STS interventions focus on the job level as well as on the organizational level. Furthermore, in the case of innovation, STS may not only help to design structures but also help to design processes. These arguments may help to explain some of the conflicting research outcomes of STS. One may hypothesize that the reason why some studies show positive outcomes and other studies the opposite may be related to the outcomes being studied. Although we think that this is not the right place for an extensive review of former empirical research on STS, our previous research indicates such a link. In a car assembly shop where efficiency was the leading performance indicator, we found that there were hardly any possibilities of applying STS practices (Niepce and Molleman, 1996), while in a firm producing diodes, stacks and glass–metal where quality was a main issue besides efficiency, there was a need to delegate tasks such as machine set-ups, repair of simple machine breakdown, basic equipment maintenance and quality control (Balkema and Molleman, 1999). We found similar results in a firm producing industrial glass (Hut and Molleman, 1998), while in a nursing context, we found a good basis for implementing STS practices (Molleman and Van Knippenberg, 1995). Advanced medical treatment and the growing assertiveness of patients, amongst other things, have changed the work of nurses. They have to deal increasingly with the unique needs of patients, making their work much more non-routine. Finally, in a study amongst IT professionals we found the right conditions to move towards a learning organization (Stam and Molleman, 1999). The historical perspective of Kumpe and Bolwijn (1994), which we depicted in Section 1, may explain why up to the 1970s, notwithstanding several more-or-less successful implementations, STS did not result in a fundamental and lasting breakthrough into organizational and job design. From a business or management point of view, there was no profitability in abolishing simple jobs and forcing back the division of labor. An increase in efficiency or quality could be expected more through non-STS related practices. Nevertheless, this historical viewpoint indicates a promising future for STS practices. At the end of this paper, we wish to build on the perspective that the move towards flexibility and innovation is dominant (the right part of Table 1). The first point we wish to touch on is the management of worker and team characteristics, which we claimed to be important in innovative firms. The constructs we discussed, such as trust and diversity, are rather vague and complex variables which are very difficult to manage. To create trust, for example, is a time-consuming and difficult process, while trust can very easily be undermined (Hosmer, 1995 and Jones and George, 1998). Moreover, in the previous section we suggested curvilinear relations between some of these variables, such as diversity and group loyalty related to performance, so one may question where the optimum may lie. This may be further complicated when the optimum of the one variable (e.g. diversity) differs from that of another one (e.g. group cohesion), or when different variables influence performance in opposite ways. The need for diversity, for example, leads to teams whose members have different domains of knowledge, while in order to facilitate the emergence of trust, one would prefer members to share the same domain (Mayer et al., 1995). A certain level of diversity may stimulate the development of new insights and frames of reference, while at the same time it may impair trust. Another complicating factor is the dynamic side of these constructs. When people work together, trust and group cohesion may grow, but diversity may diminish and group norms may become too coercive. When is the right moment to change a team? An additional facet of dynamism is that when teams are designed and structured along such rather vague constructs as trust and diversity, there will be much leeway for each member to have his or her own interpretation of these structures. Moreover, in teams in which creative and learning processes are important, workers will influence each other’s perceptions and will be continuously involved in a cycle of experimentation and learning. Driven by creativity and improvisation, they may regularly give new interpretations to structures, work design and procedures. This means that the design and structure of teams evolves and changes continuously over time (Weick, 1993 and Moorman and Miner, 1998). In sum, we think that all these practical questions and complications should direct further research. A second issue is related to the classical STS aim of striving for higher levels of QWL. In the previous section, we concluded that employees working in innovative and knowledge creating firms will mostly have appealing jobs which are intrinsically motivating, resulting in a good QWL. The other side of the coin is that intrinsically motivated people may create their own work and continuously search for new challenges, which may make their jobs endless and the workload boundless. The feeling that the job is never done may cause stress or even burnout and a low perceived QWL. This raises another important research question on how to manage intrinsic drives and at the same time to protect workers from such pitfalls. A last question on which we can only speculate is what will come after innovation and learning and what should be the response of STS to future developments in performance indicators? We wish to touch briefly on one development. We think that the role of the customer in the processing of goods and services will change dramatically and that customer care will become a new critical performance indicator. Customer care refers to the establishment of enduring relationships in which the dialogue with the customer becomes the main issue (Edvardsson et al., 1994). It means that the customer (e.g. a university student) participates in the team as a resource, co-producer, buyer, user and as a product of the team activities itself, in the sense of being the key outcome of transformation activities (Lengnick-Hall, 1996). Questions may emerge such as ‘how to manage clients in their different and various roles?’, ‘what is the position of the client in a team?’, ‘how to develop trustful relationships when the client is both co-producer and buyer?’. We think that the research agenda in the field of STS should include projects which focus on the involvement and participation of clients in flexible teams. We will end with a final comment. An STS principle we did not deal with in this paper is ‘the principle of incompletion’, which states that the design process is an ongoing process and will never be finished (Cherns, 1987). We think that this principle is also valid for the development of STS theory itself.