Exploring treated wastewater issues related to agriculture in Europe, employing a quantitative SWOT analysis

Wednesday, August 31, 2016

Exploring treated wastewater issues related to agriculture in Europe, employing a quantitative SWOT analysis

Summary

This journal article uses a quantitative SWOT analysis for the SuWaNu research project funded by the “EU Commission FP7 framework.” The objective of the study is to identify impediments and factors for the application of sustainable water treatment and options for nutrient reuse in the EU.

1. The methodological framework used in this study is a quantitative Delphi method supplemented by a concluding SWOT analysis. The survey sampled 25 experts who are keenly aware of the complexities of wastewater management. These subject matter experts were selected because they were invested stakeholders in Greece and Western Macedonia. The importance of the Delphi method in this particular case is that it is the first time the method is used with SWOT in a treated wastewater study. 

2. The Delphi method uses a questionnaire that uses three steps, the first being the narrowing of important factors, and the ranking and listing of said factors. Included in the first step is a Likert-type scale value to allow a ranking file to be established from 0 (strongly disagree) to 9 (strongly agree).  In step two, it was sent to the pool of experts who were told to list 6 important strengths, weaknesses, opportunities, and threats to treated wastewater reuse and rank them accordingly. The ranking is step three by use of the Likert-type scale to rank all the subsets listed in each SWOT sector. 

3. In the final analysis of the study, strengths of wastewater reuse were found in the markets, followed by economic aspects, and then legislation sector. Opportunities held high scores in technology transference; followed by economic aspects, water availability, and social aspects. For weaknesses, economic aspects followed by technical aspects, and markets held the highest scores. Finally, for threats, it was found technical aspects, social aspects, agriculture, and markets had the highest scores. 

In the conclusion, it was found that traditional farming in West Macedonia; a lack of innovation in applied agriculture technology; and a current economic crisis in Greece were major influencing factors for the results found in the study. Strengths for the future have forecasts to be in research and development capabilities to local agricultural research institutions.

Critique:

The use of the Delphi method and SWOT are combined in this study to produce a forecast, intended to increase efficiency of agriculture and wastewater reuse in Western Macedonia. This study is important because it is the first time SWOT is used in wastewater issues. The study, though vague on the subsets listed under each SWOT area for major impacts to the results, still gives a good understanding of where agriculture can be taken in Western Macedonia.

Sources

Exploring treated wastewater issues related to agriculture in Europe, employing a quantitative SWOT analysis

Karasavvoglou, A. G., Kyrkilis, D., Polychronidou, P., Michailidis, A., Papadaki-Klavdianou, A., Apostolidou, I., et al. (2015). The Economies of Balkan and Eastern Europe Countries in the Changed World (EBEEC 2015) Exploring Treated Wastewater Issues Related to Agriculture in Europe, Employing a Quantitative SWOT Analysis. Procedia Economics and Finance, 33, 367-375.

SWOT Methodology: A State-of-the-Art Review for the Past, A Framework for the Future

SWOT Methodology: 

A State-of-the-Art Review for the Past, A Framework for the Future

Summary:

In the research of Ghazinoory et. al. (2011), they undertook an exhaustive survey of SWOT literature till the end of 2009.  They concluded that notable usages of the SWOT methodology were used frequently in “Health & Healthcare” and the “General Management of Companies.” Additionally, they found that SWOT analysis was most likely to be used widely in corporate, national, and regional planning. Finally, they assessed that the SWOT method was not going to be neglected in the future as long as additional analytic components were added to its overall simple usability. The article was additionally divided up into six distinct sections with independent conclusions of one another.

• In section one, the authors outlined the organization of the article in which they introduced what SWOT entails and when SWOT was first introduced to the research community.  Their intention was to aid the reader with the specific objective, “to improve our knowledge in this field,” and how to promote the future development of SWOT.

• Section two was dedicated to the collection and review of all published SWOT articles which exist in the field up to the end of 2009.  In total, 557 papers had been published and could queried in the utilized databases. Additionally, they found that between 2005-2009 54% of all published papers had been produced showing a developing demand for SWOT usage and exponential growth from the method’s origins in the early 1980s.  Finally, it was found that the U.S., U.K., and India were the top producers of SWOT literature with emphasis on “Marketing Intelligence & Planning Health Policy,” with clear distinctions between methodological papers and applied-methodological approaches.

• Section three surprisingly found that “Agriculture” was the number one field which utilized SWOT to determine future or current outcomes followed closely by Health & and Healthcare. This section also determined that in most studies where SWOT was used -- it generally was reserved for policy makers, decision making, and strategy making (or planning).

• Section four swiftly provided insight to the methodological development of SWOT, most importantly on the simplicity of the method for quantitative analysis from a coding system and the ability to mesh effectively with additional methods as furthered in section five.

• Section five systematically evaluates cases where SWOT was integrated into other methods for “improving the effectiveness of SWOT.” The authors took particular interest in Proctor (2000), Kurttila et. al. (2000) and Rudder and Louw (1998). Each of these added multi-pronged approaches to increasing the accuracy of SWOT. A Finnish team Kurttila et. al. (2000) developed Analytic Hierarchy Process (AHP) to better add to the effectiveness of strategic decision-making abilities over other traditional SWOT methodologies

•  Finally, in section six, the authors concluded in recapping on the overall key findings of their study.  They were most impressed that SWOT was not limited to the management field, but that SWOT research was found in various other scientific fields.  Also, they were fascinated that in countries like Taiwan, Thailand, Indonesia, and Malaysia that the queries for SWOT were on the rise.  Finding additionally three key trends, 1.) Integration of SWOT with other scientific techniques specifically decision making and quantifying techniques; 2.) Making intelligent SWOT by using corresponding techniques; 3.) Time dynamism of SWOT needs more attention and usually gets neglected by most of the authors.

Critique:

Ghazinoory et. al. (2011) does an effective job at surveying the extant literature, however, lacks at taking the necessary time to effectively show how new approaches attached to SWOT effectively increases forecasting accuracy as they claim. However, they did make contribution to the overall body of literature in assessing how different research fields are in utilizing the SWOT methodology and are optimistic for its analytic survival in the near term when coupled effectively with other necessary analytic dimensions.

Source:

SWOT Methodology: A State-of-the-Art Review for the Past, A Framework for the Future

Ghazinoory, Sepehr et. al. (2011). SWOT Methodology: A State-of-the-Art Review for the Past, A   Framework for the Future. Journal of Business Economics and Management, 12: 1, 24-48.

Timeline Analysis (Rating: 3.5 out of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the  articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in November 2015 regarding Timeline Analysis as a modifier specifically. This modifier was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

Timeline analysis is a modifier that can be used to generate a historical context before a decision-making process. The modifier displays events chronologically in a visual manner. Successful applications of this tool can reveal important patterns, key events, or areas on which to focus.

Strengths:

• Timeline analysis is very easy to conduct
• Can utilize unstructured data
• Easy to visually represent for all audiences
• Identifies key events, thus providing a starting point or key variables for other analytic methods
• Organizes information for further analysis

Weaknesses:

• Timeline analysis is heavily dependent on the ‘binning problem,’ or how many events are grouped into each segment
• It can be subjective depending on the topic and does not represent causation
• One must be mindful of biases (i.e. anchoring, confirmation, recency, etc.)
• Both the visual and the statistical representation can be deceptive
• Reliant on the chosen starting point, or causal event
• Once a pattern extracted from a timeline analysis, it may cause an analyst to generate causation from correlations even though that may not be the case

How-To:

This generic process for conducting timeline analysis was taken from the book, Business and Competitive Analysis: Effective Application of New and Classic Methods by Craig S. Fleisher and Babette E. Bensoussan.

1. Decide what the timeline will show: major events, market expansions, product introductions, events related to a geographic area, randomly chosen events, and so on.
2. Make a comprehensive list of events that you want to put on the timeline.  This will require you or others to gather the population of events that are associated with the subject matter being focused on.
3. Consider how you will choose events to include and exclude from the final timeline.  Not all events will be of equal importance in developing an understanding of the evolution of a firm’s decisions and/or actions.  As such, defining the rules to use for excluding and including events for consideration is important.  The ultimate criteria employed for this task is that these should be based on the client’s critical intelligence needs or topics.
4. Research and note the specific dates when the events that you want to include occurred. Making a detailed note of your source(s) is a good idea so that you can later verify dates or the details of what transpired.  As well, background documentation should be maintained in separate files for each event, should further examination or inquiries of the events be required.
5. List the chosen events in a chronology. A chronology is a sequence that starts with the earliest item and ends with the most recent one.  Make special note of the earliest and latest dates that you want to include.  This will also allow you to determine the period of time that their timeline will cover.
6. Decide what units of time you will use (days, months, quarters, years, decades, and so on) to divide the timeline into segments.  These decisions may be a matter of trial and error.  Calculate the number of segments that your timeline will have.
7. Draw a line and divide it into the number of equal segments that you figure you will need.
8. Put the dates on the appropriate segments, from left to right.
9. Using the chronology that you made of events and dates, figure out where they would fall on your timeline.  Devising a scheme for how you mark and label them is useful.  For instance, you could write certain symbols (for example, $ for acquisitions and * for alliance formations) on the timeline, attach different colored labels, or make a code that refers back to your chronology.
10. If you do not have room on your timeline to include all of your chronology, cull some of the dates or make a timeline with larger segments (for example, one timeline for events in the firm’s home country and one for events that take place outside its primary market). If your dates can be divided into two or three smaller categories or themes, try making parallel timelines with identical segment sizes.  You can then see how the theme develops, and you can also compare two or more themes at a time.

Personal Application of Technique:

We used a fictitious scenario regarding US and Russian involvement in the Syrian Conflict. A list of fictitious events were provided and students were then asked to analyze the events and determine what the relationships were and what events were important. Several students then shared their key events and we compared. Students also then discussed what the hypothetical question they believed they were answering when conducting their timeline analysis.

Below is the listed fictitious events that students analyzed:

Label	Date	Event
A	Dec-15	ISIS executes Russian officer in video
B	Jan-16	Russian troops begin to embed with Syrian Army
C	Feb-16	US commits division sized force to conflict in Syria
D	Mar-16	US and Kurdish forces drive ISIS from critical crossroads town
E	Apr-16	US and Russian ambassadors to the UN engage in heated argument over the actions of their respective militaries in Syria
F	May-16	Hezbollah is effectively driven out of Syria
G	Jun-16	US Special Forces, working with a moderate Islamic group, are killed by Russian air strike
H	Jul-16	US lead coalition declares no fly zone over Syrian airspace
I	Aug-16	US troops killed by members of the Syrian Army, equipped with the latest Russian equipment
J	Sep-16	ISIS conducts suicide bombing in Moscow, American tourists are among the dead
K	Oct-16	US F-35 shoots down Russian jet in Syrian no fly zone
L	Nov-16	US ground forces engage Russian special operations at a distance in Syria
M	Dec-16	Russia launches ballistic missiles at a US Navy vessel in the Mediterranean sea

The general question students decided they answered was what was the cause and the events leading up to the outbreak of war between Russia and the United States. In this case, the timeline analysis was conducted post-mortem, although usually timeline analysis is ideally conducted pre-mortem.

Trend Analysis and Interpretation: Key Concepts and Methods for Maternal and Child Health Professionals

By: Division of Science, Education and Analysis Maternal and Child Health Bureau
http://mchb.hrsa.gov/publications/pdfs/trendanaylsis.pdf

Summary:

Introduction:

    The public health sector have a long history of monitoring trends and rates of diseases, risk factors, and behavioral tendencies of populations in order to assess current policies and develop new ones in hopes of creating a healthier population. Public health researches usually present data using large populations over a span of many years from national data sets. Time series analysis is also used along side trend analysis to describe the relationship between a risk factor and the outcome, The example the report uses is a study between pollution and hospitalization rates of children with asthma. For the sake of this report, the writer states that the time series analysis discussed focuses on the impact of the trends on hypothesized associations. Public health decision makers are trying to shift from analyzing trends of larger populations, to a smaller group or specific geographic area that would help develop more effective policies and programs. They are also trying to examine trends in indicators of emerging health problems which by definition are only available for short periods of time. Due to this shift in focus, their trend analyses need both descriptive and statistical approaches. However, one must remember that with a smaller sample size, statistical information is often skewed thus reducing analytic confidence.
     This report also discusses the different kinds of error that can occur when taking a statistical approach to trend analysis including random error and sampling error. Also because there might be a lack of data regarding smaller samples or analyses, characterizing the overall shape of a trend could be wrong. This report argues that due to the changing interests in the public health sector, trend analysis must use new strategies when examining data.

Why Do Trend Analysis?

    Health outcomes of a population is most easily understood if their frequency and distribution are described in terms of person, place, and time. Trend analysis can use these terms. Trends may focus on:

• The overall pattern of change in an indicator over time
• Comparing one time period to another time period
• Comparing one geographic area to another
• Comparing one population to another
• Making future projections

Preparing to Analyze Trend Data

    A series of conceptual issues must be addressed before analyzing and interpreting trend data regardless of the purpose of the analysis. These issues include:

• Sample size

    In public health trend analysis is typically conducted at the ecological level meaning observations are time periods and not individuals. The fewer number of time periods, the smaller the sample size which in turn increases one's potential for error. The larger the time period, the more likely it is to precisely identify patterns.

• Presence of extreme observations or outliers

    The presence of outliers must be analyzed to determine if this incidence is a random occurrence or if it reflects the departure from a general trend which is important for trend analysis.

• Availability of numerator and denominator data

   "The accuracy of numerator and denominator information over time is also very important in insuring meaningful interpretation of trend data. If both numerator and denominator data for an indicator are available for each time period being studied, then these can be easily analyzed."

• Confounding (changes over time in factors related to the indicator of interest)

    These changes are especially important to monitor when monitoring smaller populations due to the bigger effect it would have. For example, demographic changes such as a shift in ethnic composition may be more pronounced at the county or community level than in a state or the nation as a whole. Other changes over time that are of concern when conducting trend analysis include changes in laws or public policy that could affect eligibility or access to certain programs, or even cultural trend shifts such as the reduction of substance abuse.

Analysis of Trend Data

     Tables, graphs, and statistical analysis are tools for examining trend data (graphs are especially effective as they provide a visual means of describing a pattern). The first step to assessing a trend is to plot the actual observed numbers by an appropriate time period. This step is important because it establishes the outliers and helps the observer become familiar with both the absolute and relative levels of the numbers being studied.Visual inspection of the data permits the assessment of the overall direction and shape of the trend.
     To make the visual easier to interpret, data transformation and smoothing is possible. For example, putting the rates on a logarithmic scale can 'flatten' the series of rates as seen in this example of infant mortality in Chicago:

      In statistical approaches attempt to 'smooth' data, they aim to increase the stability of the rates by diminishing their jagged appearance. Smoothing can be accomplished by using various forms of averaging, including use of multiple year rates, moving averages, and regression procedures. Public health professionals often use 'collapsing' by combining the numerators and denominators for two or three years of data rather than using the annual rates. This effect increases the stability by increasing the sample size of each point, but also results in a loss of information, leaving less points making it more difficult to portray a pattern. Here is an example of a 'smooth' and collapsed graph:

      Statistical Approaches--There are several statistical approaches in order to test significant trends or different trends:

• Chi-square test for linear trends--A chi-square test for trend can be obtained by organizing the observed data into a contingency table with one row for each time period and two columns: the first as the numerator, and the second as the denominator.
• Regression model
• Ordinary Least Squares (OLS) Regression
• can be used to model the observed series of rates
• Poisson Regression
•  can also be used to model the observed rates, collapsed data, or moving averages
• accounts for both fluctuation across time and the variability at each time point
• assumes that errors in modeled observations are independent
• Time Series Analysis
• a collection of specialized regression methods that use integrated moving averages and other smoothing techniques and have different assumptions about the error structure of the data
• Moving average refers to a complex process that incorporates information from past observations and past errors in those observations into the estimation of predicted values
• Time Series Analysis assumes errors are correlated and can diagnose the precise nature of the correlation and adjust for it

Presentation of Trend Data:

    This report gives general guidelines when presenting this information to a wider audience:

Critique:

    Although long, this report is very detailed especially when describing the different statistical approaches to trend analysis. For the sake of public health, statistical approaches are better than descriptive ones because public health topics involve larger sample sizes and populations but this is not always the case regarding intelligence questions. I think that in some intelligence cases, the why is very important in determining how a person/country might act which may be the result of a specific occurrence. For its own purposes, I think this report did an excellent job but for our purposes I believe descriptive analysis is also very important when it comes to forecasting.