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Abstract

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.

Keywords

Line and Staff Conflict ISM Human Body Organization Bio inspired

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References

Alawamleh

Popplewell

(2011). Interpretive structural modeling of risk sources in virtual organisation. International Journal of Production Research, 49(20), 6041–6063.

Alderfer

(1977). Group and intergroup relations. In Hackman

(Ed.), Improving life at work: Behavioual science approaches to organizational change (pp. 227–296). CA: Goodyear Publishing.

Attri

Dev

Sharma

(2012). Interpretive structural modelling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3–8.

Bag

Anand

(2014a). Modeling GSCM framework using ISM and MICMAC analysis. African Journal of Business Management, 8(22), 1053–1065.

Bag

Anand

(2014b). Modeling soft dimensions of FMS and their interrelationship using ism and micmac analysis. Prabandhan: Indian Journal of Management, 7(10), 43–54

Blau

Scott

(1962). Formal organization. California: Chandler Publishing Company.

Bowen

D. E.

Ostroff

(2004). Understanding HRM—Firm performance linkages: The role of the ‘strength’ of the HRM system. Academy of Management Review, 29(2), 203–221.

Brown

D. L.

(1978). Toward a theory of power and intergroup relations. In Cooper

Alderfer

(Eds), Advances in experimental social processes (pp. 20–23). New York: John Wiley & Sons.

Chen

Y. P.

Hsu

Y. S.

Yip

F. W.

(2011). Friends or rivals: Comparative perceptions of human resource and line managers on perceived future firm performance. The International Journal of Human Resource Management, 22(8), 1703–1722.

10.

Church

A. H.

Waclawski

(2001). Hold the line: An examination of line vs. staff differences. Human Resource Management, 40(1), 21–34.

11.

Dalton

(1950). Conflicts between staff and line managerial officers. American Sociological Review, 15(3), 342–351.

12.

Davar

R. S.

(2009). Personnel management & industrial relations (10th ed.). New Delhi: Vikas Publishing House.

13.

Dubey

Ali

S. S.

(2014). Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15(2), 131–143

14.

Faisal

M. N.

(2010). Sustainable supply chains: A study of interaction among the enablers. Business Process Management Journal, 10(3), 508–529.

15.

Faisal

Rahman

Qureshi

M. N.

(2011). Analysis of interaction among the barriers to total quality management implementation using interpretive structural modeling approach. Benchmarking: An International Journal, 18(4), 563–587.

16.

Griffin

Ricky. W.

(2005). Management (8th ed.). New Delhi: Dreamtech Press.

17.

Grzybowska

(2012). Sustainability in the supply chain: Analysing the enablers. Environemntal issues in supply chain management. Retrieved 20 April 2014, from http://fem.put.poznan.pl/poli-admin/publications/1176505Grzybowska.pdf

18.

Haimann

Hilgert

(1997). Supervision: Concepts and practice of management. Cincinnati: South Western Publishers.

19.

Haleem

Sushil, Quadri

M. A.

Kumar

(2012). Analysis of critical success factors of world class manufacturing practices: An application of interpretive structural modeling and interpretive ranking process. Production Planning and Control: The Management of Operations, 23(10–11), 722–734.

20.

Hodgetts

(1990). Management: Theory, process and practice. Florida: Harcourt Brace Publishers.

21.

Ilyas

R. M.

Shankar

Banwet

D. K.

(2008). Creating flex-lean-agile value chain by outsourcing: An ISM-based interventional roadmap. Business Process management Journal, 14(3), 338–389.

22.

Janis

I. L.

(1982). Groupthink. Boston: Houghton Mifflin.

23.

Jyoti, Banwet

D. K.

Deshmukh

S. G.

(2010). Modeling the success factors for national R&D organizations: A case of India. Journal of modelling in Management, 5(2), 158–175.

24.

Kedia

P. K.

, & Sushil (2013). Total interpretive structural modelling of strategic technology management in automobile industry. Proceedings of PICMET ’13: Technology Management for Emerging Technologies.

25.

Kellermanns

F. W.

Walter

Lechner

Floyd

S. W.

(2005). The lack of consensus about strategic consensus: Advancing theory and research. Journal of Management, 31(5), 719–737.

26.

Knoeppel

(2001a). Most effective type of industrial organization. In Witzel

(Ed.), Organization behaviour, 1890–1940 (pp. 68–78). NY: Perennial.

27.

Knoeppel

(2001b). The correct type of organization. In Witzel

(Ed.), Organization behaviour, 1890–1940 (pp. 1–14). NY: Perennial.

28.

Kramer

R. M.

(1991). Intergroup relations and organizational dilemmas: The role of categorization process. Organizational Behaviour, 13, 123–134.

29.

Kumar

Khan

Haleem

(2012). Enablers of technology management: An ISM approach. Proceedings of the National Conference on Trends & Advances in Mechanizal Engineering (pp. 801–180). Haryana: YMCA University of Science & Technology.

30.

Mandal

Deshmukh

S. G.

(1994). Vendor selection using interpretive structural modeling (ISM), International Journal of Operations and Production Management, 14(6), 52–59.

31.

Mumby

D. K.

(1988). Power, interests and organizational culture. In Thayer

(Ed.) Communication and Power in Organizations: Discourse, Ideology and Domination (pp. 67). New Jersy: Abex Publishing.

32.

Nossiter

(1979). A New approach toward resolving the line and staff dilemma. Academy of Management Review, 4(1), 103–106.

33.

Parikshit

Shankar

Baisya

R. K.

(2008). Analysis of interactions among the variables of supply chain performance measurement system implementation. Business Process Management Journal, 14(4), 512–529.

34.

Prasad

U. C.

Suri

R. K.

(2011). Modeling of continuity and change forces in private higher technical education using total interpretive structural modeling (TISM). Global Journal of Flexible Systems Management, 12(3–4), 31–40.

35.

Pravin

Shankar

Yadav

S. S.

(2008). Flexibility in global supply chain: Modeling the enablers. Journal of Modelling in Management, 3(3), 277–297.

36.

Qureshi

M. N.

Kumar

(2007). Modeling the logistics outsourcing relationship variables to enhance shippers’ productivity and competitiveness in logistical supply chain. International Journal of Productivity and Performance Management, 56(8), 689–714.

37.

Qureshi

M. N.

Kumar

(2008). An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pacific and Logistics, 20(2), 227–249.

38.

Ramesh

Banwet

D. K.

Shankar

(2010). Modeling the barriers of supply chain collaboration. Journal of Modelling in Management, 5(2), 176–193.

39.

Ravi

Shankar

(2005). Analysis of interactions among the barriers of reverse logistics. Technological Forecasting and Social Change, 72(8), 1011–1029.

40.

Rentsch

J. R.

(1990). Climate and culture: Interaction and qualitative differences in organizational meanings. Journal of Applied Psychology, 75(6), 668–681.

41.

Sage

A. P.

(1977). Interpretive structural modeling: Methodology for large scale systems. New York: McGraw-Hill.

42.

Sahney

Banwet

D. K.

Karunes

(2010). Quality framework in education through the application of interpretive structural modeling: An administrative staff perspective in the Indian context. The TQM Journal, 22(1), 56–71.

43.

Samuel

(2005). The political agenda of organizations. New Jersey: Transaction Publishers.

44.

Sanjay

Shankar

(2004). IT enablement of supply chains: Modeling the enablers. International Journal of Productivity and Performance Management, 53(8), 700–712.

45.

Sharma

B. P.

Singh

M. D.

Kumar

(2012). Knowledge sharing barriers: An integrated approach of ISM. Singapore: AHP.

46.

Singh

M. D.

Kant

(2008). Knowledge management barriers: An interpretive structural modeling approach. International Journal of Management Science and Engineering Management, 3(2), 141–150.

47.

Singh

M. D.

Shankar

Narain

Agarwal

(2003). An interpretive structural modeling of knowledge management in engineering industries. Journal of Advances in Management Research, 1(1), 28–40.

48.

Singh

R. K.

(2011). Developing the framework for coordination in supply chain of SMEs. Business Process Management Journal, 17(4), 619–638.

49.

Soti

Shankar

Kaushal

O. P.

(2010). Modeling the enablers of six sigma using interpretive structural modeling. Journal of Modelling in Management, 5(2), 124–141.

50.

Sushil (2005a). Interpretive matrix: A tool to aid interpretation of management and social research. Global Journal of Flexible Systems Management, 6(2), 27–30.

51.

Sushil (2005b). A flexible strategy framework for managing community and change. International Journal of Global Business and Competitiveness, 1(1), 22–32.

52.

Sushil (2009). Interpretive ranking process. Global Journal of Flexible Systems Management, 10(4), 1–10.

53.

Sushil (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87–106.

54.

Vasconcellos

(2011). Managing conflicts between line and staff in interdisciplinary R&D projects. International Research Management. Retrieved 25 April 2014, from http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780195062526.001.0001/acprof-9780195062526-chapter-14

55.

Vittal

S. A.

(2007). Linking KM effectiveness attributes to organizational performance. Journal of Information and Knowledge Management Systems, 37(2), 133–149.

56.

Vittal

S. A.

Kanungo

(2010). Modeling enablers for successful KM implementation. Journal of Knowledge Management, 14(1), 101–113.

57.

Warfield

J. N.

(1974). Structuring complex systems: Battele monograph. Columbus, OH: Battele Memorial Ins. 4.

58.

Warfield

J. N.

(1994). A science of generic design: Managing complexity through systems design. Iowa: Iowa State University Press.

59.

Warfield

J. N.

(1999). Twenty laws of complexity: Science applicability in organizations. Systems research and Behavioral Science, 16(1), 3–40.

60.

Wilkenfield

(1986). Managing staff functions in large corporation. From Ebscohost.

61.

Wilkins

Alan L.

Ouchi

W. G.

(1983). Efficient cultures: Exploring the relationship between culture and organizational performance. Administrative Science Quarterly, 28(3), 468–481.

62.

Witzel

(2002). A short history of efficiency. Business Strategy Review, 13(4), 38–47.

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

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