Skip to content

Posts by Pairach

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

Apr 16

8 Comments

lavaan package for Structural Equation Model in R


lavaan is a package for running a “Structural Equation Models” or SEM in R. The script of lavaan is similar to Mplus, which is very comprehensive and compact. The lavaan package is developed by Yves Rosseel.

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

##-------------------------------------------------##
##    Measuring Transaction Cost in Supply Chains  ##
##               Pairach Piboonrungroj             ##
##         R useR conference August 2011           ##
##-------------------------------------------------##
# 1. Load data
hoteldata &lt;- read.csv(&quot;https://www.dropbox.com/s/fzh040xkhofozjy/cleandata.csv&quot;)
# 2. Install Package
install.packages(&quot;lavaan&quot;)
# 3. Load Package
library(lavaan)
#5. Structural Model
TC.Model &lt;- '
# latent variable definitions
cost =~ TC1 + TC2 + TC3 +TC6 + TC7   +TC11  + TC13
asset =~ AS1 + AS2
uncertainty  =~ UN1 + UN2
# regression
cost ~ uncertainty + asset
# resident (co)variance
TC1 ~~ TC3
TC6 ~~ TC13
TC11 ~~ TC13
TC13 ~~ AS1
'
fitTC &lt;- sem(TC.Model, data = hoteldata)
summary(fitTC, standardized = TRUE, fit.measures=TRUE)
# Coefficients
# coef(fitTC)
#More indices
# Fit indices e.g. CFI
fit.indices &lt;- inspect(fitTC, what = &quot;fit&quot;)
fit.indices[&quot;cfi&quot;]
# Calling Modification Indices
# inspect(fitTC, what = &quot;mi&quot;)

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

« Older Entries
Newer Entries »