“… hmmm, let's have a quick look … he opened the suitcase … holy Jesus!, he said … legs!”– W.S. Burroughs from Spare Ass Annie and Other Tales
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In examining what role autopoietic theory might play in furthering the agenda of process-based organizational research, it is worth noting that the biological notion of…
Abstract
In examining what role autopoietic theory might play in furthering the agenda of process-based organizational research, it is worth noting that the biological notion of autopoiesis and derivative concepts have already achieved limited recognition in the broad organization studies field. A perennial debate has evolved around the question of whether organizations can and/or should be considered autopoietic (see Luhmann, 1986; Zeleny & Hufford, 1992; Mingers, 1992; Robb, 1989; Kay, 2001). Beyond that, the general approach seems to involve taking some defined aspect of autopoiesis and employing this to shed light on some defined aspect of organizational life. Thus, Krogh and Roos (1998) use the concept of autopoiesis to expound, discuss, and illustrate a distinctive perspective on organizational knowledge; Luhmann (1990) and Teubner (1984) use autopoiesis to create awareness of how the circularity and self-referentiality of legal, and social systems more generally, can prevent renewal and lead to a failure in adapting to problems in society. Autopoiesis has been used to enhance our understanding of how the functioning of computers relate to the evolution of human language, thought and action, (Winograd & Flores, 1987). In management, the concept of autopoiesis has been used, largely in a metaphorical sense, to understand the firm as a living evolving system that is characterized by “flux and transformation” (Morgan, 1986). In the therapeutic professions, various writers use autopoiesis to show how circular sets of self-reinforcing conversations can create severe dysfunctions with individuals (Efren, Lukens, & Lukens, 1990), in families and in other tightly knit social groups (Dell, 1982, 1985; Hoffman, 1988; Goolishian & Winderman, 1988). Elsewhere in organization studies, Kay (1997) applies autopoiesis to the facilitation of organizational change, and Beer (1981) uses the term “pathological autopoiesis” in understanding threats to organizational viability.
Christian Fuchs and Wolfgang Hofkirchner
Maturana and Varela (1980, p. 78f) provided the following definition of autopoiesis: “An autopoietic machine is a machine organized (defined as a unity) as a network of processes…
Abstract
Maturana and Varela (1980, p. 78f) provided the following definition of autopoiesis: “An autopoietic machine is a machine organized (defined as a unity) as a network of processes of production (transformation and destruction) of components that produces the components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them and (ii) constitute it (the machine) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network.” This definition shows that for Maturana and Varela, autopoietic systems are systems that define, maintain, and reproduce themselves. The notion of machine that they employ in the definition might seem a bit misleading because we tend to think of machines as mechanistic and nonliving, but Maturana and Varela (e.g., 1987) in later publications have preferred to speak of autopoietic organizations.
Following Polanyi, this paper aims to suggest that the Industrial Revolution marked a break‐point between pre‐industrial society (characterised by integration) and industrial…
Abstract
Purpose
Following Polanyi, this paper aims to suggest that the Industrial Revolution marked a break‐point between pre‐industrial society (characterised by integration) and industrial society (characterised by differentiation).
Design/methodology/approach
As a conceptual paper, the focus is on drawing out the implications of Luhmann's application of the theory of autopoiesis to industrial society. This discussion leads to critical reflection on the state we are in and the active role we can each play in bringing about change.
Findings
Differentiation, without an overall co‐ordination and control function within society, has led to the sub‐systems (and organisations) becoming self‐serving or pathologically autopoietic. Society has a capacity for self‐observation, through such mediums as the mass media. Alarm at the apparent increasing rate of change in both social and ecological systems reported by the mass media appears to be drawing us towards a second break‐point. The outcome of this revolution, should it come about, is impossible to predict but descent into a new “dark age” is an option as is the re‐integration of economic activity with social, religious and political functions. Luhmann's autopoiesis provides a convincing explanation for how society is structured and observing the implications of this. The role of the mass media as an observing system and in bringing information about change to society's attention is emphasised.
Practical implications
The paper seeks to provide an explanation for how society is structured and demonstrate how society appears to be passively observing the implications of this. Proposals for both restructuring and the actions we, as active citizens and organisational members, can take to redress our current state are advanced.
Originality/value
The paper brings together ideas from a diverse range of fields (including autopoiesis, complexity theory, and systems) and applies them to a highly significant topic.
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Muhammad Sajid Qureshi, Ali Daud, Malik Khizar Hayat and Muhammad Tanvir Afzal
Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and…
Abstract
Purpose
Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.
Design/methodology/approach
The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.
Findings
The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.
Research limitations/implications
Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.
Practical implications
The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.
Social implications
The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.
Originality/value
We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.