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Apr 24

Research tips: Consultancy skills


This post is a collection of tips I got from the doctoral training (RSSDP) on “Consultancy Skills” by Dr Caron King at Cardiff University.

  1. Consultancy = professional practices that give an expert advise to the company, a problem solving work.
  2. Begin with the end in mind (2nd of 7 habits of highly effective people by Stephen R. Covey)
     – Outcome thinking, what do you have to get done in the end.
    – Consultancy is about recognising what your clients really want for you because they are paying for your what you are doing.
    – Knowing what it is that the clients want and satisfy then.
    – Moreover to sustain the job, your jobs have to be good value for money
  3. The hardest part is how to sell your jobs. Then delivering a good value jobs will increase the chance that client will hire you again.
  4. Dedicated time = 5:4:3 (5 days fearing, 4 days with clients, 3 nights away from home)
  5. Start by working with the big companies for 3-4 years to learn how to do it.

Why a would anyone hire a consultant?

  1. Temporal resources. GBP 36,000 is the average cost of recruiting a doctoral level employee.
  2. Someone to blame – troublesome jobs
  3. My time is too valuable – too busy for the day jobs, need someone to work for
  4. Specialist knowledge – problem solving
  5. For access to their network
  6. Branding – kudos
  7. Legitimise their projects – get endorsed or forced by laws
  8. To manage something unpopular
  9. Ratify decisions – In the most cases clients know the solutions/answers but hiring consultant to support/back up their decision. Therefore, consultants are like the voices/spokespersons

How to get a consultancy jobs

  1. Networks

Consultancy process

  1. Identify the problem
  2. Clarify the problem
  3. Collect data
  4. Analyse the data
  5. Solve the problem
  6. Suggest the solution
  7. Agree the solution
  8. Implement the solution

What are the skills, knowledge and attributes we need

  1. Communication skills e.g., presentation skills
  2. Project management skills
  3. Analytical skills, both qualitative and quantitative = data analysis
  4. Research skills e.g., collect data, analyse data, conclude the result

The skills that recruit consultants (used by consultancy firms)
(The following listed skills are perfect for researchers as well!) 

  1. Communication
  2. Leadership
  3. Initiative
  4. Flexibility + Adaptability
  5. Problem solving
  6. Self awareness
  7. Commitment / Motivation
  8. Numeracy
  9. Willingness to learn
  10. Resilience
  11. Independence
  12. G.S.O.H. (Good Sense of Humour)
Key skills
  1. Listening
    (Open your mind, Listen for empathy, just gather data no interpreting or thinking at the same time) 
    Repetitive listening – repeat what you are hearing in your head as simultaneous as possible to get rid of other things.
    If taking notes, tell the clients that you will do a few, ask them to send you important information and write the whole note after the meeting. 
  2. Questioning
    Three big questions
    – Asking the questions that allow opened thinking
  3. Rapport building
    – to build trustworthy relationships
  4. Critical information seeking
  5. Challenging Assumption
  6. Pitching, elevating lift (in 90 seconds)
    HOOK technique
    – Hook -> emotion
    – Outline the problem in a short time!
    – Outcomes
    – Call To action
     

Apr 19

Useful resources for writing C++ code in Mac using Xcode


During 18-20 April 2012 I am attending the course on “C++ for beginner”. The course mainly based on Window OS. To remind myself I here posted some useful link for C++ in Mac using Xcode.

Tips

How to run an executable file

  • In Xcode, an executable file is in the product folder below the main folder (the one that contains main.cpp file) in the left-hand side section.
  • The file name is the project name (without any extension).
  • Here are the step to run the file
  1. Right click on the file -> select “Open in finder”
  2. Launch the Terminal
  3. Type “cd” in the Terminal and Drag the folder that contains the file to the Terminal
  4. Type “./” and the project name  followed by the requirement for the program

Apr 17

Open Access Peer-Reviewed Journals for Business and Management Research especially in Logistics and Supply Chain Management


Currently, the issue of Open-access academic journals is becoming a hot issues driven by The Guardian and then followed by The Economist. Personally I agree that publicly funded research should be available without any extra monetary cost. Hence I have searched for a good peer-review journals in business and management where logistics and supply chain scholars can publish their work. These will be an options to publish in the place where quality and fairness are both in place.

  1. BuR – Business Research (Germany) – Well-known editorial board in Operations and Information System e.g., Daniel R. Guide, Jr., (Pen. State U, USA, Co-editor of JOM),
  2. International Journal of Business Science and Applied Management (UK)
  3. The Electronic Journal of Business Research Methods (UK)
  4. Logistics Journal (Germany)
  5. Research Journal of Business Management (USA)
  6. M@n@gement (France)
  7. Asian Journal of Management Research (India)

Apr 17

PhD Tips: Impact and Research Communication


This posts are tips for researchers and PhD students on impact and research communication I have got from the training session at Cardiff University. The workshop was held by Ms Josie Dixon.

Why communication matters?

  • PhD is a specialist project. Hence communicating PhD research to non-specialists are challenging.
  • Funding: To apply for research grants, jobs you may need to communicate your research to non-specialists.
  • In the UK, REF (Research Excellence Framework) in 2014 is crucial for academics. Unlike REA (Research Excercise Assessment) in 2008, in REF, impact of the research has an important role apart from academic publication.
  • Non-academic publication, the process of publishing is highly mediated mostly by non-specialists e.g., publishers, librarians, marketing people (sales reps.).
  • Sometimes researchers may need to dealing with the media e.g., being interviewed by journalists.
  • Public engagement is now very important for applying for public funding. This is also very important to the universities to sustain the supports for the research in the long term.

Making the case why your research matter

  • Selling point: Not only academic ones but also non-academic, more general, ones.
  • What is the niche of your research. How did you fulfil the demand.
  • Elevating pitch. Presenting the value of your research in a short time (5 minutes)
  • Using keywords that cover all important aspects of your research
  • Balancing the “Big picture” vs. “Details” of your research.
    – Big picture = abstraction, overarching, generic. Getting audiences to understand the research in general but too much big picture could lead to lossing human interest in the detail of the research
    – Detail =  focus, specifics, concrete, human interest, story telling, using visualisation or comparing to the common/general phenomenons, problem to solution.
  • Organisation: Purposes -> Value -> Outcomes, What are the pay-off of your PhD project?
  • “So what?” Values and outcomes
  • “Who cares?” = who are the ‘stakeholders’ of your research i.e.,
    (1) Applied users: practitioners & professioners;
    (2) Public sector: communities, policy makers. Difficult to pinpoint and measure;
    (3) Academics: researchers, lecturers and students.
  • PhD thesis is an inward and backward approach that explaining the process and foundation of the research.
  • Public communication is an outward and onward approach to convey the outcomes of the research (what it yields)

Looking beyond the case study: Micro and Macro dimensions of your research

  • Micro dimension provides a specific group of your audiences. The most uniquely specific.
  • Macro offer more board audience of your research. The most board general aspect.
  • For example. My PhD thesis on
    “The impact of supply chain collaboration on firm performance in the tourism sector“.
    – Micro dimension:  Supply chain collaboration in the hotel industry in Thailand using Structural Equation Models.
    – Macro dimension: Business Management, Supply Chain Collaboration, Supply Chain Management.

Defining your contribution to the field

  • What, in a nut shell, have been discovered?
  • How will your research alter the way academic think previously?
  • Be confident about your research and the outcomes.
  • Recommended reading: Footnotes and Fancy Free by Prof. Peter Barry (In Times Higher Education)

Making the headlines

  • Telling a story about your research. Consider how to announce it for the public audience.
  • Get the energy and excitement in the statement
  • When you are writing, imagine the best day of your research when you discover something new or find significant results.
  • Exercise: Writing a headline and opening statement in 35 words. The below is mine.

Cheaper and Happier Holiday!
A cheap trip is usually not a happy one.
Now businesses can both reduce their costs and offer a better service at the same time by just sharing their business data.

Outcomes, Benefits and Impact

  • Think about what the research do for the audience.
  • Think about features and benefits, not just the features.
  • Ex. my research
    – Features: Testing a positive effect of supply chain collaboration on firm performance
    – Benefits: Firm can select the right collaborative activities by reduce cost and provide better service level

Apr 16

6 Comments

OpenMx package for Structural Equation Model in R


OpenMx (Boker et al, 2011) is claimed to be a “ free and open source software for use with R that allows estimation of a wide variety of advanced multivariate statistical models.” contributed by experts in R and SEM.

The following is my OpenMx script I used in my presentation at R useR! 2011.

##-------------------------------------------------##
##    Measuring Transaction Cost in Supply Chains  ##
##               Pairach Piboonrungroj             ##
##         R useR conference August 2011           ##
##-------------------------------------------------##

#install OpenMx
source('http://openmx.psyc.virginia.edu/getOpenMx.R')
require(OpenMx)

# load the OpenMx package into R

library(OpenMx)

# read the data into an R dataframe

hoteldata <- read.csv("http://dl.dropbox.com/u/46344142/useR2011/cleandata.csv")

# define which indicators load on each factor

indicatorsTC <- c("TC1", "TC2", "TC3", "TC6", "TC7" , "TC11" , "TC13")

indicatorsAS <- c("AS1", "AS2")

indicatorsUN <- c("UN1", "UN2")

# create a vector of all of the manifest variables

manifests <- c(indicatorsTC, indicatorsAS, indicatorsUN)

# define which indicator is to be used to scale each factor

scaleTC <- c("TC1")

scaleAS <- c("AS1")

scaleUN <- c("UN1")

# define the names of the factors

latents <- c("TC", "AS", "UN")

# define the MxModel and store it into "factorModel"

factorModel <- mxModel("TC Model",

type="RAM",

manifestVars = manifests,

latentVars = latents,

# specify the free factor loadings

mxPath(from="TC", to=indicatorsTC, free=TRUE, values=.2),

mxPath(from="AS", to=indicatorsAS, free=TRUE, values=.2),

mxPath(from="UN", to=indicatorsUN, free=TRUE, values=.2),

# scale the two latent variables

mxPath(from="TC", to=scaleTC, free=FALSE, values=1),

mxPath(from="AS", to=scaleAS, free=FALSE, values=1),

mxPath(from="UN", to=scaleUN, free=FALSE, values=1),

# specify the unique variances

mxPath(from=manifests, arrows=2, free=TRUE, values=.8),

# specify the factor variances

mxPath(from=latents, arrows=2, free=TRUE, values=.8),

# specify the path

mxPath(from="AS", to="TC", arrows=2, free=TRUE, values=.3),

mxPath(from="UN", to="TC", arrows=2, free=TRUE, values=.3),

mxPath(from="AS", to="UN", arrows=2, free=TRUE, values=.3),

# specify the mean structure

mxPath(from="one", to=c(manifests, latents), arrows=1, free=FALSE, values=0),

# attach the data to the model

mxData(hoteldata, type="raw")

)

# run the factor model

factorModelOut <- mxRun(factorModel)

# print a summary of the results

summary(factorModelOut)
<pre>

See more details about the model and the comparison with other R packages and software

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