Line and Staff Functions in Organizations Revisited: A Bionic System Analogy Using ISM

The purpose of this article is to identify variables and their interrelationships causing line and staff conflict in organizations. Specifically it is comparing and analyzing the causes of line and staff conflict from human body perspective, assuming it to be an optimally managed organization. A generally applicable framework that establishes relationship between these variables is developed using interpretative structural modelling (ISM). The developed framework is then compared with the organization of line and staff functions in the human body, presuming that with this the real cause of the problem could be understood. In this effort, it has been found that an optimal degree of flexibility, top management involvement, equity, clearly defined roles and appropriate design and reporting relationships are the major strategies followed by human body that can help managers to eliminate conflict. It is expected that based on this comparison some ways of resolving conflict within organizations can be suggested. Besides, we can also expect that mimicking a variety of elements in human body organization can inspire business organizations to learn improvements in their design and function too.

[1]  A. Valle,et al.  Diffusion of nuclear energy in some developing countries , 2014 .

[2]  M. Qureshi,et al.  An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers , 2008 .

[3]  Nikhil Dev,et al.  Interpretive Structural Modelling (ISM) approach: An Overview , 2013 .

[4]  Alok Kumar,et al.  Knowledge Sharing Barriers: An Integrated Approach of ISM and AHP , 2012 .

[5]  Morgen Witzel A Short History of Efficiency , 2002 .

[6]  Surendra S. Yadav,et al.  Flexibility in global supply chain: modeling the enablers , 2008 .

[7]  Marc D. Street Groupthink , 1997 .

[8]  Mohd. Nishat Faisal,et al.  Sustainable supply chains: a study of interaction among the enablers , 2010, Bus. Process. Manag. J..

[9]  J. Rentsch Climate and culture : interaction and qualitative differences in organizational meanings , 1990 .

[10]  J. R Hackman,et al.  Improving life at work: Behavioral science approaches to organizational change , 1977 .

[11]  M. Dalton,et al.  Conflicts Between Staff and Line Managerial Officers , 1950 .

[12]  Sushil,et al.  Total interpretive structural modelling of strategic technology management in automobile industry , 2013, 2013 Proceedings of PICMET '13: Technology Management in the IT-Driven Services (PICMET).

[13]  Rameshwar Dubey,et al.  Identification of Flexible Manufacturing System Dimensions and Their Interrelationship Using Total Interpretive Structural Modelling and Fuzzy MICMAC Analysis , 2014 .

[14]  Sushil,et al.  Analysis of critical success factors of world-class manufacturing practices: an application of interpretative structural modelling and interpretative ranking process , 2012 .

[15]  Vivian Nossiter A New Approach Toward Resolving The Line and Staff Dilemma , 1979 .

[16]  Raymond L. Hilgert,et al.  Supervision: concepts and practices of management , 1972 .

[17]  Surajit Bag,et al.  Modeling Green Supply Chain Management framework using ISM and MICMAC analysis , 2014 .

[18]  Eduardo Vasconcellos Managing Conflicts Between Line and Staff in Interdisciplinary R&D Projects , 1990 .

[19]  Rajesh K. Singh,et al.  Developing the framework for coordination in supply chain of SMEs , 2011, Bus. Process. Manag. J..

[20]  P. Clark The Formal Organization , 1974 .

[21]  V. Anantatmula Linking KM effectiveness attributes to organizational performance , 2007 .

[22]  Sushil Interpreting the Interpretive Structural Model , 2012, Global Journal of Flexible Systems Management.

[23]  D. K. Banwet,et al.  Quality framework in education through application of interpretive structural modeling: An administrative staff perspective in the Indian context , 2010 .

[24]  Yu-Shan Hsu,et al.  Friends or rivals: comparative perceptions of human resource and line managers on perceived future firm performance , 2011 .

[25]  Frederick A. Rossini,et al.  International Research Management , 1990 .

[26]  R. Hodgetts Management: Theory, process, and practice , 1975 .

[27]  Shivraj Kanungo,et al.  Modeling Enablers for Successful KM Implementation , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[28]  Sushil Interpretive Ranking Process , 2009 .

[29]  Keith Popplewell,et al.  Interpretive structural modelling of risk sources in a virtual organisation , 2011 .

[30]  D. Bowen,et al.  Understanding HRM–Firm Performance Linkages: The Role of the “Strength” of the HRM System , 2004 .

[31]  Jyoti,et al.  Modelling the success factors for national R&D organizations: a case of India , 2010 .

[32]  Umesh Chandra Prasad,et al.  Modeling of Continuity and Change Forces in Private Higher Technical Education Using Total Interpretive Structural Modeling (TISM) , 2011 .

[33]  R. Shankar,et al.  ANALYSIS OF INTERACTIONS AMONG THE BARRIERS OF REVERSE LOGISTICS , 2005 .

[34]  John N. Warfield,et al.  Twenty laws of complexity: science applicable in organizations , 1999 .

[35]  Ravi Shankar,et al.  Modeling the enablers of Six Sigma using interpreting structural modeling , 2010 .

[36]  Dinesh Kumar,et al.  Modeling the logistics outsourcing relationship variables to enhance shippers' productivity and competitiveness in logistical supply chain , 2007 .

[37]  Surajit Bag,et al.  Modeling Soft Dimensions of FMS and Their Interrelationship Using ISM and MICMAC Analysis , 2014 .

[38]  Faisal Talib,et al.  Analysis of interaction among the barriers to total quality management implementation using interpretive structural modeling approach , 2011 .

[39]  S. Deshmukh,et al.  Vendor Selection Using Interpretive Structural Modelling (ISM) , 1994 .

[40]  Ravi Shankar,et al.  Creating flex-lean-agile value chain by outsourcing: An ISM-based interventional roadmap , 2008, Bus. Process. Manag. J..

[41]  Ravi Shankar,et al.  Modeling the barriers of supply chain collaboration , 2010 .

[42]  R. Shankar,et al.  IT enablement of supply chains: modeling the enablers , 2004 .

[43]  Katarzyna Grzybowska,et al.  Sustainability in the Supply Chain: Analysing the Enablers , 2012 .

[44]  Sushil INTERPRETIVE MATRIX: A TOOL TO AID INTERPRETATION OF MANAGEMENT AND SOCIAL RESEARCH , 2005 .

[45]  Alan L. Wilkins,et al.  Efficient Cultures: Exploring the Relationship between Culture and Organizational Performance. , 1983 .

[46]  R. Kant,et al.  Knowledge management barriers: An interpretive structural modeling approach , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[47]  John N. Warfield,et al.  A Science of Generic Design: Managing Complexity Through Systems Design , 1994 .

[48]  Ravi Shankar,et al.  Analysis of interactions among the variables of supply chain performance measurement system implementation , 2008, Bus. Process. Manag. J..

[49]  R. Shankar,et al.  An interpretive structural modeling of knowledge management in engineering industries , 2003 .

[50]  S. Floyd,et al.  The Lack of Consensus About Strategic Consensus: Advancing Theory and Research , 2005 .

[51]  A. H. Church,et al.  Hold the Line: An Examination of Line vs. Staff Differences , 2001 .