Thursday, September 15, 2016

Dr. Barbara Czarniawska - Biography and Contribution

DOB 2 December 1948



M.A. in Social Psychology, Faculty of Psychology and Pedagogics, University of Warsaw,
Poland, 1970.
Ph.D. in Economic Sciences, Central School of Planning and Statistics, Warsaw, Poland, 1976
Docent in Business Administration, Stockholm School of Economics, Sweden, 1986.


Book
(2004) Narratives in social science research. London: Sage.


Chapter
(2007) Narrative inquiry in and about organizations. In D. Jean Clandinin (ed.) Handbook of 
narrative inquiry. Mapping a methodology. Newbury Park, CA: Sage, 383-404.


http://www.gu.se/digitalAssets/1357/1357464_cv2012.pdf



 More About Narrative Inquiry

Volume 10, No. 1, Art. 30 – January 2009
Beyond the Story Itself: Narrative Inquiry and Autoethnography in Intercultural Research in Higher Education
Sheila Trahar
http://www.qualitative-research.net/index.php/fqs/article/view/1218/2653



Narrative Methods for the Human Sciences
Catherine Kohler Riessman
SAGE Publications, 13-Dec-2007 - Social Science - 264 pages


Narrative Methods for the Human Sciences provides a lively overview of research based on constructing and interpreting narrative. Designed to improve research practice, it gives a detailed discussion of four analytic methods that students can adapt. Author Catherine Kohler Riessman explains how to conduct the four kinds of narrative analysis using model studies from sociology, anthropology, psychology, education and nursing. Throughout the book, she compares different approaches including thematic analysis, structural analysis, dialogic/performance analysis, and visual narrative analysis. The book helps students confront specific issues in their research practice, including how to construct a transcript in an interview study; complexities of working with materials translated from another language; defining narrative segments; relating text and context; locating oneself as the researcher in a responsible way in an inquiry; and arguing for the credibility of the case-based approach.

Broad in scope, Narrative Methods for the Human Sciences also offers concrete guidance in individual chapters for students and established scholars wanting to join the "narrative turn" in social research.

Key Features

Focuses on four particular methods of narrative analysis: This text provides specific diverse exemplars of good narrative research, as practiced in several social science and human service disciplines.
Offers guidance for narrative interviewing: The author discusses the complexities between spoken language and any written transcript. In the process, she encourages students to be mindful of the texts they construct from dialogues among speakers.
Presents arguments about validation in case-based research: Riessman presents several ways to think about credibility in narrative studies, contextualizing validity in relation to epistemology and theoretical orientation of a study.
Explores the differences between grounded theory methods and narrative analysis: The author clarifies distinctions between inductive thematic coding in grounded theory, and other interpretive approaches, and narrative analysis.
Presents social linguistic methods for analyzing oral narrative: This text makes the approach accessible to readers not trained in social linguistics in part by providing rich examples from a number of different disciplines in the social and behavioral sciences.
Employs visual methods of analysis: Riessman takes narrative research beyond the spoken or written texts by showing how exemplary researchers have connected participants' words and images made during the research process. She also discusses other research that incorporates "found" images (in archives) in a narrative inquiry.
This text is designed as a supplement to the qualitative research course taught in graduate departments across the social and behavioral sciences, and as a core book in the narrative course.

https://books.google.co.in/books?id=V1YXBAAAQBAJ

Updated   18 September 2016,  3 November, 2014

Saturday, September 10, 2016

Employee Engagement - Research Literature Review



The concept of engagement is usually attributed to the American professor William Kahn’s views, which saw the classification of the term as “harnessing of organization members' selves to their work roles” (1990: p.694). Kahn proposed, that when engaged, employees apply and express themselves on
physical-, cognitive- and emotional levels. In other words, employee becomes a part of his/her job description and he/she will be able to work with full potential.

KAHN, W.A., (1990), Psychological Conditions of Personal Engagement and Disengagement at
Work, The Academy of Management Journal, Vol. 33, No. 4 (Dec., 1990), pp. 692-724,

Tuesday, August 2, 2016

Principles of Supply Chain Management



Supply Chain Management objectives:

Add value by
ß reducing working capital,
ß taking assets off the balance sheet,
ß accelerating cash-to-cash cycles,
ß increasing inventory turns,
decreasing cost of production in various firms in supply chain
improving quality in various firms in supply chain
introducing new management techniques in various firms in supply chain



The seven principles as articulated by Andersen Consulting are as follows:

1. Segment customers based on service needs.
2. Customise the Supply Chain Management network.
3. Listen to signals of market demand and plan accordingly.
4. Differentiate product closer to the customer.
5. Strategically manage the sources of supply.
6. Develop a supply-chain-wide technology strategy.
7. Adopt channel-spanning performance measures.

The original statements of Andersen Consulting Persons

Principle 1: Segment customers based on the service
needs of distinct groups and adapt the supply
chain to serve these segments profi tably.

Principle 2: Customize the logistics network to the
service requirements and profi tability of customer
segments.

Principle 3: Listen to market signals and align
demand planning accordingly across the supply
chain, ensuring consistent forecasts and optimal
resource allocation.

Principle 4: Differentiate product closer to the
customer and speed conversion across the supply
chain.

Principle 5: Manage sources of supply strategically
to reduce the total cost of owning materials and
services.

Principle 6: Develop a supply chain-wide technology
strategy that supports multiple levels of decision
making and gives a clear view of the fl ow of
products, services, and information.

Principle 7: Adopt channel-spanning performance
measures to gauge collective success in reaching
the end-user effectively and effi ciently.

http://www.scmr.com/images/01.SevenPrinciples_.pdf


The Seven Principles of Effective Supply Chain Planning

Systematic management of “master data,” including key data fields for items, customers, manufacturing resources, and suppliers
Synchronized long-term, tactical, and execution planning processes, planning horizons, and intervals for data refresh
Mature collaborative processes for both key customers and suppliers reconciling forecast, orders, and usage or sell through
Data-oriented understanding of the inputs to the forecast including forecast error, cumulative bias, lift, new products, and year-end volume variation
Intense focus on “point-of-sale” or “sell-through” data (as opposed to sales orders and “sell in”)
Disciplined product lifecycle management process bridging the gap between product development and supply chain
A continuous improvement approach to understanding consumer or user behavior.
http://www.apics.org/sites/apics-blog/think-supply-chain-landing-page/thinking-supply-chain/2015/05/20/the-seven-principles-of-effective-supply-chain-planning



Understanding the Concept of Elasticity in Supply Chain Relationships:  An Agency Theory Perspective Maryam Zomorrodi  and  Sajad Fayezi 

ASIAN JOURNAL OF MANAGEMENT RESEARCH
2010
P.1: Relational governance mechanisms based  on  trust, commitment  and information sharing  significantly influence the relationship elasticity of cooperating parties.
P.2: Contractual  governance mechanisms based  on  risk/reward  sharing  and  relationship­ specific investment significantly influence the relationship elasticity of cooperating parties.
http://www.ipublishing.co.in/ajmrvol1no1/EIJMRS1035.pdf

Innovation Generation in Supply Chain Relationships: A Conceptual Model and Research Propositions

Journal of the Academy of Marketing Science.
Volume 32, No. 1, 2004,  pages 61-79.

Proposition 1a:The greater the extent of buyer-seller interaction, the greater the generation of incremental innovations in supply chain relationships
Proposition 1b:The greater the extent of buyer-seller interaction, the greater the generation of radical innovations in supply chain relationships.

Proposition 2a:The greater the IT adoption and integration between the buyer and seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 2b:The greater the IT adoption and integration between the buyer and seller, the lesser the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 3a:The greater the asymmetry in input commitment between the buyer and the seller, the lesser the impact of interaction on the generation of incremental innovations in supply chain relationships.

Proposition 3b:The greater the asymmetry in input commitment between the buyer and the seller, the
greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 4a: The lesser the asymmetry in attitudinal commitment between the buyer and the seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 4b: The lesser the asymmetry in attitudinal commitment between the buyer and the seller, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 5a: The greater the competence trust between the buyer and the seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 5b: The greater the competence trust between the buyer and seller, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 6a: The greater the goodwill trust between the buyer and seller, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships
Proposition 6b: The greater the goodwill trust between the buyer and the seller, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 7a: The greater the tacitness of technology associated with an innovation, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships
Proposition 7b: The greater the tacitness of technology associated with an innovation, the greater the impact of interaction on the generation of radical innovations in supply chain relationships

Proposition 8a:The greater the stability of the final consumer demand, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.

Proposition 8b:The lesser the stability of final consumer demand, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Proposition 9a: The greater the network connections of the buyer and seller within an industry group, the greater the impact of interaction on the generation of incremental innovations in supply chain relationships.
Proposition 9b: The greater the network connections of the buyer and the seller across industry groups, the greater the impact of interaction on the generation of radical innovations in supply chain relationships.

Sunday, July 10, 2016

Statistical Optimization in High Dimensions - A Research Paper



Huan Xu, Constantine Caramanis, and Shie Mannor

Received: January 2014
Accepted: March 2016
Published Online: July 5, 2016

The paper deals with optimization problems whose parameters are known only approximately, based on noisy samples. In large-scale applications, the number of samples one can collect is typically of the same order of (or even less than) the dimensionality of the problem.

Three algorithms are proposed to address this setting, combining ideas from statistics, machine learning, and robust optimization.

The key ingredients of to the algorithms are dimensionality reduction techniques from machine learning, robust optimization, and concentration of measure tools from statistics.

http://pubsonline.informs.org/doi/abs/10.1287/opre.2016.1504

Sunday, June 19, 2016

Why Some Bosses Bully?



https://hbr.org/2016/06/why-some-bosses-bully-their-best-employees


Social Dominance Orientation (SDO)

Social Dominance Theory postulates that some people have more of a tendency toward “social dominance orientation” (SDO) than others.


Predictors of abusive supervision : Supervisor perceptions of deep-level dissimilarity, relationship conflict, and subordinate performance. / Tepper, Bennett J.; Moss, Sherry E.; Duffy, Michelle K.
In: Academy of Management Journal, Vol. 54, No. 2, 01.04.2011, p. 279-294.
https://experts.umn.edu/en/publications/predictors-of-abusive-supervision(7ebef711-d96b-4b97-b323-e9dd8a0a2d06).html