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Trust within the US-EU Fresh Grapefruit Supply Chain

Permanent Link: http://ufdc.ufl.edu/UFE0022759/00001

Material Information

Title: Trust within the US-EU Fresh Grapefruit Supply Chain
Physical Description: 1 online resource (92 p.)
Language: english
Creator: Dahl, Ellnor
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: ahp, analytical, b2b, bussiness, chain, commerce, e, electronic, eu, european, exports, fresh, grapefruit, hierarchy, international, process, supply, trust, union
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Business transactions around the world have transitioned from personal business relationships, to electronic-business (e-business) structures, also referred to as e-commerce. As new technologies are adopted, so are new un-researched problematic situations that directly affect the safety and reliability of the world food supply. Our primary objective of this research was to identify key factors of trust that are currently found within business-to-business (B2B) relationships along the US-EU fresh grapefruit supply chain. Because of the complexity of identifying the numerous and subjective elements of trust, the Analytical Hierarchy Process (AHP) will provide the foundation for analysis. This method involves interviewing fresh grapefruit exporters to the EU and collecting pairwise rankings of which elements of trust are more important. This should ultimately aid in sustaining an international flow of safe, high-quality food as agricultural B2B relationships transition into the realm of e-commerce.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ellnor Dahl.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: House, Lisa O.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-06-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022759:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022759/00001

Material Information

Title: Trust within the US-EU Fresh Grapefruit Supply Chain
Physical Description: 1 online resource (92 p.)
Language: english
Creator: Dahl, Ellnor
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: ahp, analytical, b2b, bussiness, chain, commerce, e, electronic, eu, european, exports, fresh, grapefruit, hierarchy, international, process, supply, trust, union
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Business transactions around the world have transitioned from personal business relationships, to electronic-business (e-business) structures, also referred to as e-commerce. As new technologies are adopted, so are new un-researched problematic situations that directly affect the safety and reliability of the world food supply. Our primary objective of this research was to identify key factors of trust that are currently found within business-to-business (B2B) relationships along the US-EU fresh grapefruit supply chain. Because of the complexity of identifying the numerous and subjective elements of trust, the Analytical Hierarchy Process (AHP) will provide the foundation for analysis. This method involves interviewing fresh grapefruit exporters to the EU and collecting pairwise rankings of which elements of trust are more important. This should ultimately aid in sustaining an international flow of safe, high-quality food as agricultural B2B relationships transition into the realm of e-commerce.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ellnor Dahl.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: House, Lisa O.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2009-06-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022759:00001


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1 TRUST WITHIN THE US-EU FRESH GRAPEFRUIT SUPPLY CHAIN By ELLNOR McKENZIE DAHL A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2008

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2 2008 Ellnor McKenzie Dahl

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3 To my family and beloved husband. Through Gods grace, they have been the greatest support system on earth.

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4 ACKNOWLEDGMENTS First and foremost I would lik e to thank God for the many blessings He has placed in my life coupled with the challenges He placed in my path so that I may gain a greater appreciation for those blessings. I thank my parents for their years of undying support and love. Their constant encouragement and limitless imagination placed the seed of adventure in my soul so that I may never be afraid to venture past the visible horizon. I would like to give sp ecial thanks to my three sisters who have done a marvelous job of keeping me grounded during my two year jour ney through graduate school. Having all completed their collegiate goals, they served as a constant reminder that the important things in life are not defined by ones academic success, bu t by the character traits one develops along the way. Because of them, I have a deep appreciation for everything the words perseverance and logic embody. To my husband Cody, I give my sincere love and gratitude for keeping me on my toes. There is never a dull moment in our home as they are typically filled with impromptu song: regardless if one is attempting to derive the relative weighted priorities of the analytical hierarchy process. To the Food and Resource Economics department, I would like to extend a warm appreciation for four great years of academic stimulation and professional preparation. The faculty and staff within the department have prov ided me with the inspiration and motivation to consistently strive to exceed the expe ctations of those whom you are surrounded. Dr. Lisa House has been a great mentor a nd friend during my journey through the Food and Resource Economics Department. The advisement she has pr ovided during both my undergraduate and graduate work will forever serv e me well. One moment in particular will stick with me for the rest of my life. When exploring the possibility of graduate school, I

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5 doubted my ability to complete the thesis requi rement of the Master of Science degree. However, I can still remember the day she told me, Ellie, you write a thesis one chapter at a time. It was this quote that I referenced frequently during the many sleepless nights and the endless to-do list that enabled me to calm down and focus on the one chapter at hand. One individual brought a much-appr eciated positive perspective to the table. I would like to thank Dr. Michael Gunderson. His enthusiasm for agricultural economics and passion to best serve his students has been a source of encour agement when the road ha s gotten a little bumpy. The fire in his sole for economics is unc ontainable for that I am very thankful. In closing I would like to extend my great est appreciation towards the United States Department of Agriculture, Cooperative Resear ch Education and Extension Service for their support of the National Needs Fellowship program. Albert Einstein once said, Imagination is everything. It is the preview of lifes coming attractions The USDA National Needs Fellowship Award exceeded my wildest imaginati ons, but has given me the opportunity to prove to myself that if others have faith in my ab ility, maybe I should too. As a result, by completing my Masters of Science degree in Food and Resource Economics I have been able to parallel my passions for both academia and agriculture. This has ultimately resulted in me being better prepared to serve the agricultural community as a lifelong endeavor.

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6 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........8 LIST OF FIGURES.........................................................................................................................9 LIST OF ABBREVIATIONS........................................................................................................ 11 ABSTRACT...................................................................................................................................12 CHAPTER 1 INTRODUCTION ..................................................................................................................13 Problem...................................................................................................................................13 Market Background................................................................................................................14 General US Exports......................................................................................................... 14 General US Exports to the EU......................................................................................... 16 Grapefruit Exports........................................................................................................... 17 Research Objective............................................................................................................. ....18 2 LITERATURE REVIEW.......................................................................................................22 Factors of Organizational Trust.............................................................................................. 22 Interorganizational Trus t and Relationships...........................................................................25 Cross Cultural Interorganizational Trust................................................................................ 28 3 THEORETICAL FRAMEWORK..........................................................................................32 Analytical Hierarchy Process................................................................................................. 32 Structuring Complexity................................................................................................... 33 Trust Hierarchy................................................................................................................34 Measuring on a Ratio Scale.............................................................................................36 Nominal....................................................................................................................36 Ordinal......................................................................................................................36 Interval.....................................................................................................................37 Ratio.........................................................................................................................37 Consistency.............................................................................................................. 38 Synthesis..........................................................................................................................39 Calculation of AHP............................................................................................................. ....39 Pairwise Comparisons.....................................................................................................39 Deriving Weighted Ratios........................................................................................41 Deriving the Consistency Ratio................................................................................ 43 Role of AHP in Supply Chain Management...................................................................44

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7 4 DATA COLLECTION........................................................................................................... 53 Firm Selection................................................................................................................. ........53 Data Collection Process..........................................................................................................53 Data.........................................................................................................................................56 5 EMPERICAL MODEL RESULTS........................................................................................ 61 Results.....................................................................................................................................61 Cumulative Results............................................................................................................. ....62 Acknowledging the Consistency Ratios .................................................................................63 6 SUMMARY, CONCLUSIONS, AND IMPLICATIONS...................................................... 82 Summary.................................................................................................................................82 Conclusions.............................................................................................................................83 Implications................................................................................................................... .........84 Future Research Needs...........................................................................................................85 LIST OF REFERENCES...............................................................................................................87 BIOGRAPHICAL SKETCH.........................................................................................................92

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8 LIST OF TABLES Table page 3-1 Scale of relative importance............................................................................................... 49 3-2 Random consistency index................................................................................................ 52 4-1 Evaluation of the 1st hierarchy level..................................................................................58 4-3 Evaluation 2 with respect to seller.................................................................................59 4-4 Evaluation 2.2 with respect to relationship with seller.................................................. 59 4-5 Evaluation 2.3 with respect to reliability of the seller.................................................... 60 4-6 Evaluation 3 with respect to market............................................................................... 60 5-1 Cumulative averaged results for all 10 firms..................................................................... 70 5-2 Firm A results............................................................................................................. .......72 5-3 Firm B results............................................................................................................. ........73 5-4 Firm C results............................................................................................................. ........74 5-5 Firm D results............................................................................................................. .......75 5-6 Firm E results............................................................................................................. ........76 5-7 Firm F results............................................................................................................. ........77 5-8 Firm G results............................................................................................................. .......78 5-9 Firm H results............................................................................................................. .......79 5-10 Firm I results............................................................................................................ ..........80 5-11 Firm J results............................................................................................................ ..........81

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9 LIST OF FIGURES Figure page 1-1 Categories of total US agriculture exports.........................................................................191-2 Total US agricultural exports............................................................................................. 191-3 Categories of US agricultural exports to the EU............................................................... 201-4 Top two exporting destinations for fresh fruit going to the EU......................................... 201-5 Top two exporting destinations fo r processed fruit going to the EU................................. 213-1 Structured decision hierarchy............................................................................................473-2 Trust hierarchy...................................................................................................................483-3 Sample matrix for pairwise comparison............................................................................ 493-4 Completed sample matrix.................................................................................................. 493-5 Synthesizing judgment ratios............................................................................................. 503-6 Normalized matrix.............................................................................................................503-7 Relative preferences....................................................................................................... ....503-8 Multiplication of priority vector........................................................................................ 513-9 Totaling of priority vector multiplication.......................................................................... 513-10 Determining max............................................................................................................513-11 Consistency index......................................................................................................... .....513-12 Consistency ratio......................................................................................................... .......514-1 Pairwise graphi cal judgment tool....................................................................................... 584-2 Visualization of applie d graphical judgment tool.............................................................. 585-1 Completed decision matrix from firm A: top hierarchy level............................................ 675-2 Synthesizing judgment ratios from firm A: top hierarchy level........................................ 675-3 Normalized matrix from fi rm A: top hierarchy level......................................................... 685-4 Relative preferences of fi rm A: top hierarchy level........................................................... 68

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10 5-5 Multiplication of priority vector for firm A: top hierarchy level....................................... 685-6 Totaling of priority vector fo r firm A: top hierarchy level................................................ 685-7 Determining max for firm A: top hierarchy level........................................................... 695-8 Consistency index for firm A: top hierarchy level............................................................. 695-9 Consistency ratio for firm A: top hierarchy level.............................................................. 69

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11 LIST OF ABBREVIATIONS AHP Analytical hi erarchy process B2B Business to business B2C Business to consumer CI Consistency index CR Consistency ratio E-commerce Electronic commerce EU European Union HVP High value product Lambda RCI Random consistency index US United States

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12 Abstract of Thesis Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TRUST WITHIN THE US-EU FRESH GRAPEFRUIT SUPPLY CHAIN By Ellnor McKenzie Dahl December 2008 Chair: Lisa House Major: Food and Resource Economics Business transactions around the world ha ve transitioned from personal business relationships, to electronic-busine ss (e-business) structures, also referred to as e-commerce. As new technologies are adopted, so are new un-resear ched problematic situations that directly affect the safety and reliability of the world f ood supply. Our primary objective of this research was to identify key factors of trust that are currently found w ithin business-to-business (B2B) relationships along the US-EU fresh grapefruit supply chain. Because of the complexity of identifying the numerous and subjective elements of trust, the Analytical Hierarchy Process (AHP) will provide the foundation for analysis This method involves interviewing fresh grapefruit exporters to the EU and collecting pairwise rankings of which elements of trust are more important. This should ultimately aid in sustaining an internati onal flow of safe, highquality food as agricultural B2B relationships transition into the realm of e-commerce.

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13 CHAPTER 1 INTRODUCTION Problem As new technologies drive innovation to ge nerate greater busine ss-to-business (B2B) efficiencies, business transactions around the globe will con tinue their transition from the traditional relationship based interactions to wards more electronically dependent business communications. This form of elect ronic business is referred to in the literature as e-business or electronic commerce (e-commerce). The infiltration of e-commerce within inte rnational supply chai ns has sparked many unanswered questions. In order to maintain an affordable and cost effective world food supply, it is essential to understand the elements that are currently affecting modern, international agricultural supply-chains. Wh ile e-commerce is currently no t the dominate form of B2B transactions within the worlds food supply, it is important to notice the direction in which agricultural trade is moving in the hopes of la ying the ground work for a successful e-business transition in the future. Little research has been conducted regarding cross-cultural, B2B trust development. As the world is increasingly becomi ng an international marketplace, it is important to recognize the cross-cultural partnerships that are forming as a result. Heffernan stated that trust has been identified as an essential element to maintaini ng successful international business alliances. This is due to heightened levels of uncertainty and ri sk as a partners culture, values, and goals may be very different within global markets (Lane 1998) The literature indicates more research is needed to understand the critical element of trust within the world of B2B transactions. As world economies become more integrated, th e global market becomes larger and stronger as trade and policy links grown between countri es (Edmondson 2008). Along this thread, the

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14 European Commission recognizes th e European Union (EU) as one of the major trading regions for United States (US) agricultural products. Ma intaining a safe, affordable, and reliable food supply is a top priority for both the EU and the US. As the US is a key supplier of many food products to the EU, the success of the international supply chains connecting the two political regions is critical. Furthermore, as trust has b een identified as the key element in maintaining cross cultural, B2B relationships, re search is needed to identify th e key elements of trust within this trading arena (European Union 2006). Once thes e elements are identified, it is predicted that economic efficiencies will be captured as partic ipants become better equipped to meet their business alliance needs while being prepared to transition towards the avenue of e-commerce (European Union 2006). Market Background General US Exports Population growth and general economic perf ormance drives global demand for food and agricultural products, which lays the foundation for trade and US exports (USDA 2008c). Edmondon (2008) found that agricult ural exports play a significan t role in both the farm and nonfarm economy through the effects on employment, purchasing power, and income He states that in 2006 each export farm dolla r generated an additional $1.65 in domestic business activity. As a result, the $71.0 billion earned in agricu ltural exports stimulate d an additional $117.2 billion in general economic ac tivity in 2006 (Edmondson 2008). Farm purchases such as fuel, fertilizer and other necessary production inputs for export commodities caused economic stimulation within manufacturing, trade a nd transportation sect ors (Edmondson 2008). Agricultural export production also provides the need for 841,000 full-time jobs with 482,000 of those jobs existing in the nonfarm sector (E dmondson 2008). The connection is clear. US agricultural exports are a vital contributor to the overall health of the general economy.

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15 Brook (2006) reports that over the past five year s, the values of agri cultural exports from the US have been on the rise hitting record leve ls. Increased demand in Canada and Mexico are primarily responsible for the renewed growth within agricultural expo rts (Brooks 2006). Figure 1-1 shows all major agricultural prod ucts being exported from the US over the past five years. The largest area of exports has consistently been cereal product s. These types of products include major cereals such as barley, millet, oat and triticale, as well as pseudo cereals that include buckwheat, amaranth and quinoa (Seibe l 2006). These products currently compose 23% of total US agricultural exports and have trad itionally been the larg est export product (USDA 2008c). Since the 1990s, high value products (HVP) me ats, poultry, live animals, oilseed meals, vegetable oils, fruits, vegetables and beverages have been on the rise due to an increase in world population and income These products have become key players for US exports. As all US exports have been on the rise, HVPs have in creased at a faster rate than bulk products wheat, rice, coarse grains, oilseeds, cotton and tobacco and as a result repr esent the majority of US agricultural exports (USDA 2008c). Oil seed products represented 14% of total US agricultural exports in 2007. Most of the US oil seed production is being exported to Canada and Mexico (USDA 2008c). Within the meat category, a large portion of poultry products are being exported to the Russian Federation and Mexico. Japan, Mexico and Canada are the largest importers of US red meat products (USDA 2008c). However, Brooks ( 2006) states that meats, as a general category, are a relatively small percentage of total US expor ts due to disease outbreaks and related trade restrictions. The cattle and beef sectors have been affected th e most by these restrictions (Brooks 2006).

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16 Brooks (2006) indicates that fr uits, nuts, fish and vegetables, combined, contribute 14% of total US agricultural exports. The US has b een recognized as providi ng high quality nuts for snacks and confectionaries on the world market (Brooks 2006). The top nut export would be almonds representing 70% of total producti on followed by walnuts (Brooks 2006). Fresh grapefruit is the number one fruit product bei ng exported accounting for nearly 40% of sales (Brooks 2006). Fish and vegetabl es contribute a total of 7% to the total US agricultural exports and top products include fish roes, pacific salmon and potatoes (Brooks 2006, USDA 2008c). General US Exports to the EU The US and EU account for the largest bilate ral trade alliances in the world when both goods and services are considered together (European Union 2008). Due to the significant volume of trade between the two political regi ons, there is a high leve l of interdependence between the two economies (Europ ean Union 2008). Together, they account for about 40% of the worlds trade (Ex ternal). The largest per centage of trade between the US and EU comes from the trade of machinery and vehicles (E uropean Union 2008). Among the member states, the United Kingdom and Germany are the two largest importers of US goods and services (European Union 2008). However, when evaluating US agricultural e xports, the EU is no longer the number one trading partner, but does remain a primary ma rket for several products produced in the US (USDA 2008c). Figure 1-2 shows the total amount of US agricu ltural exports as well as the share going to the EU. In 2007 the US exported a value of $89.9 billion, while the EU imported $8.7 billion equaling 9.7% of total US agricultural exports (USDA 2008c ). In the same year, the EU ranked fourth in total agricultural US imports by US dollar values (USDA 2008d). Figure 1-3 shows the major agricultural commodities being exported to the EU over the past five years. As in the world market, HVPs are increasing at a rapid rate for exports going to

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17 the EU. Fruits and nuts are again the largest tr ade area within the specialty HVP crops. The top two fresh fruit products being exported into th e EU are grapefruit and apples (USDA 2008d). Within the processed fruit sector the top products are prunes a nd raisins (USDA 2008d). Walnuts in various forms and processed states ar e the top nut products be ing exported to the EU (USDA 2008d). Cereal expor ts to the EU represent 13% of to tal US exports to the EU. Note that cereals to the EU are not as dominate as th at found within the world market, but is still a significant export market for US cereals. Figure 1-5 and Figure 1-6 show the top two importing countries for each fruit product grapefruit, apples prunes, raisins sent to the EU. One fact worth mentioning is the nearly non -existent export of meat products to the EU. This is a direct result of the trade restrictions mentioned earlier due to disease outbreaks and production practices. Grapefruit Exports Florida is the largest produ cer of grapefruit within the US. During the 2007-2008 season, 1,127 tons of grapefruit were produced in Florida, with total US grapef ruit production equaling 1,556 tons (USDA 2008a). The second largest US producer of grapefruit is Texas, whose crop in 2007-2008 totaled 256 tons (USDA 2008a). Wh en looking at Florida grapefruit production, St. Lucie and Indian River counties account fo r 69% of the states to tal production during the 2006-2007 season (USDA 2007). These counties are located on the eastern co ast of Florida and are found within the Indian River region of citrus production (USD A 2007). During the 20062007 growing season, the Indian River growing region was responsible for 71.7% of total grapefruit production in Florid a (USDA 2007). The 2006-2007 season statistics are being used as the updated citrus summary for the 2007-2008 pr oduction year has yet to be released by the USDA.

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18 In 2008, $124,842,000 of fresh grapefruit was exported from the US throughout the world (USDA 2008d). Top 2008 export destinations in descending order are: Japan; France; Canada; and the Netherlands (USDA 2008d). In 2008, $34,792,000 of fresh grapefruit was exported from this US to the EU (USDA 2008d.) This value places fresh grapefruit as the number one fresh fruit product going from the US to the EU. Research Objective The primary objective of this study is to identify the elements of trust that exist along the US-EU fresh grapefruit supply chain. Fresh grapefruit is the num ber one fresh fruit product being exported from the US to the EU and for this reason is the focus of this research. In order to collect the needed resear ch data, fresh grapefruit exporters wi ll be interviewed to identify which objects of trust are most important. In order to establish a vector of relative importance, weighted pairwise comparisons of the trust elements will be collected through one-on-one interviews. As more business alliances are tr ending towards the use of e-commerce to conduct daily activities, this analysis will be used in further research that will ultimately aid in this transition. Secondary objectives for this research include: Identify the structure and size of the fresh gr apefruit supply chain from the US to the EU. Identify key players for one-on-one interviews. Identify various market challenges within the fresh grapefruit US-EU supply chain.

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19 Figure 1-1. Categories of total US ag riculture exports (Source: USDA 2008b) Figure 1-2. Total US agricultura l exports (Source: USDA 2008b)

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20 Figure 1-3. Categories of US agricultura l exports to the EU (Source: USDA 2008b) Fresh Fruit:Grapefruit (19%) $50,609.000:France: 32% Netherlands: 31%Apples (6.6%) $42,393,000:United Kingdom: 83% Finland: 4.8%%% of total Exports to EU Figure 1-4. Top two exporting destinations for fresh fruit going to the EU (Source: USDA 2008b)

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21 Processed Fruit:Grapes, Dried (37%) $78,699,000United Kingdom: 43% Germany: 23%Plums, Dried (43%) $75,927,000Germany: 38% Italy: 16%% of total Exports to EU Figure 1-5. Top two exporting destinations for processed fruit going to the EU (Source: USDA 2008b)

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22 CHAPTER 2 LITERATURE REVIEW The topic of trust within organizational st udies has generated in creased interest among researchers (Mayer, Davis, and Schoorman 1995). When looking at strategic B2B management, the development of trusting relationships has been identified as a key element within the increasingly competitive global market (Huff and Kelley 2003). For this relationship development to happen, both the organization and its team members must be both trustworthy and trusting (Huff and Kelley 2003). This section of the research will review the elements that have been identified within the literature for orga nizational, interorganizational and international trust development. Factors of Organizational Trust Due to the ambiguous nature the term trust embodies, it is importa nt to have a clear understanding of how this research defines trust pr ior to outlining the elem ents of B2B trust. Mayers (1995) definition is the most co mmonly accepted within the B2B and e-commerce literature (Grabnew-Krauter and Kalusc ha 2003). He defines trust as, the willingness of a party to be vulnerable to the actions of another pa rty based on the expectati on that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party (Mayer, Davis, and Schoorman 1995). A party, or organization, is composed of indivi duals, which leads to the first form of trust relevant to B2B transactions. This form is called propensity to trust, which is defined as a trait that leads to a generalized expectati on about the trustworthiness of others (Mayer, Davis, and Schoorman 1995). Mayer (1995) indicates that this form of trust is an individual personality trait that can be influenced by societ ys trust levels. Research indi cates that people naturally have inborn trust dispositions which aid in their accepta nce of trust and perceived reduction of risk.

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23 Individuals with greater propensit y to trust are naturally more tr usting regardless of any other variable found within the developmen t of trust. As a result, an individuals propensity to trust plays a critical role in the overall level of trus t found within an organization (Mayer, Davis, and Schoorman 1995). This factor will play an inte resting role when conducting par ticipant interviews within the grapefruit supply chain. Often tim es there is one decision maker for an agricultural firm whose innate propensity to trust determines the overall trusting nature of the firm. This is only relevant when looking at a firm to be the trusting party (trustor), not the party to be trusted (trustee) (Mayer, Davis, and Schoorman 1995). When firm A expects firm B to provide a prom ised level of quality and quantity of product or service, each member of firm B plays a role in establishing trustworthy credibility within the industry. This form of trust is called internal trust, which is defined as the climate of trust within an organization, defined as positive expectations that i ndividuals have about the intent and behaviors of multiple organizational members based on organizational roles, relationships, experiences, and interdependencies (Shockley-Zalabak et al 2000) Behavioral consistency among members of a firm is important for trus t to be established within the organization (Brenkert 1998) and aids in the development of a credible reputation among other firms. If an individual or firm acts inconsistently there would be a lack of general trustworthiness to the firm (Brenkert 1998). As a result, when internal trust is established teamwork, leadership, goal setting and performance apprai sals improve (Jones and Geor ge 1998; Mayer, Davis, and Schoorman 1995; McAllister 1995). Once the firms overall propensity to trust and internal trust elements have been defined, the final factor of B2B trust becomes relevant; exte rnal trust. This form of trust is defined as

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24 the extent to which organizational members have a collectively held trust orientation toward a partner firm (Zaheer, McEvily, and Perrone 1998.). For example, how doe s firm A know if Firm B has a high level trustworthin ess or if Firm B is simply misleading firm A to believe it is trustworthy? From the literature, we know that trust is the willingness to take risk (Mayer, Davis, and Schoorman 1995), and t rustworthiness is when an ex change partner is worthy of the trust of others (Barney and Hansen), but how does this solve the dilemma between Firm A and Firm B? Mayer, Davis, and Schoorman (1995), and Saunde rs et al. (2004) ha ve identified three primary factors of trustworthiness that can serve as indicators to Firm A regarding Firm Bs trust credibility. Ability, benevolence and integrity are the most commonly used terms to define trustworthiness due to th e unique perspective from which the trustor (Firm A) can evaluate the trustee (Firm B) (Mayer, Davis, and Schoorman 1995). Mayer, Davis, and Schoorman (1995) use the term ability to evaluate a specific skill set in which a party claims to be proficient. The scope of the specific skill set is important as a trustee may be very knowledgeable in one range of abilit y, which is an indicator of trust within that domain (Mayer, Davis, and Schoorman 1995). Ho wever, the party does not inherit universal trust as it may have very little experience or education regarding a different skill set (Mayer, Davis, and Schoorman 1995 .). This indicates that trust is discipline specific (Zand 1972). The second identified basis for trust developm ent is benevolence, which is defined as the extent to which a trustee is believed to want to do good to the trustor, aside from an egocentric profit motive (Mayer, Davis, and Schoorman 1995.). This factor of trustworthiness acknowledges the possibility of opportunistic behavi or from the trustee, but reduces the threat due to the perception of positive B2B intentions from the trustee toward the trustor (Mayer,

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25 Davis, and Schoorman 1995). In other words, benevolence is the inverse of a trustees likeliness to lie and serves as a key factor to the esta blishment of trust (Mayer, Davis, and Schoorman 1995) Finally, integrity factors into trust by incorporating th e trustors perception regarding the trustees ability to maintain acceptable principles (Mayer, Davis, and Schoorman 1995). By adhering to some set of principl es, one defines their personal inte grity, but if a trustees personal integrity does not align with the trustors personal principles, the trustee lacks moral integrity in the eyes of the trustor (McFall 1987). These thr ee factors combined serve as a strong indication to the trustee regarding the trustworthiness of the trustor. The three forms of trust found within B2B organizational studies have been identified alongside indicators or trustee trustworthiness and it is now important to outli ne how these variables are app lied within B2B supply chains. Interorganizational Trust and Relationships Swaminathan Jayashankar, and Tayur (2003) define a supply chain to be a set of e ntities involved in the design of new products and servic es, procuring raw materials, transforming them into semi finished and finished products, and delivering them to the end customer. A set of entities or firms must work together throughout the supply chain in order to meet their core functions as identified by Salin (2000). She first states that supply chains are to be responsive to consumer needs and secondly, they are to effici ently transform and transport goods and services to consumers (Salin 2000). In order for firms to meet consumer demand with supply, they must be able to work together effici ently. Trust is not only a critic al element within organizational management, but has also been identified within supplier literature and channel literature as an important factor (Heffernan 2004). Throughout B2B supply chains some relationshi ps are well established and long term while others are new and underdeveloped. Heffe rnan (2004) identifies a five-stage B2B

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26 relationship lifecycle that examin es exactly how relationships ar e developed and terminated. The five stage process: pre-relati onship stage, early inte raction stage; relationship growth stage; partnership stage; relationship e nd stage has been mentioned by seve ral other researchers as well (Ford 1982; Frazier1983; Dwyer, Schurr, and Oh 1987; Borys and Jermison 1989; Larson 1992; Millman and Wilson 1994; Palmer and Bejour 1994; Wilson, 1995; Ford, Gadde, Hakansson, and Snehota 2003). Pre-relationship stage. The pre-relationship stage refers to the activities that occur prior to the development of any form of a relati onship (Heffernan 2004). As reported by Mayer, Davis, and Schoorman (1995), it is this stage of th e relationship where some form of basic trust must be established before a relationship is pursued. Heffernan (2004) outlines a four step process. First, a business change occurs that requires the development of a new relationship. Newly established interorganizational need prompts the search for potential B2B partners. This is the point within the lifecycle where reputation (Saunders et al. 2004) a nd the firms ability to perform become critical within the supply chain (Mayer, Davis, and Schoorman 1995). Next, is the formation of a list of potential business partners and the final se t is the selection of the best potential firm (Heffernan 2004). Finding the appropriate partner is a critical step in the relationship development process (Wilson 1995), and once a firm has been selected, the relationship transitions into the earl y interaction stage (Heffernan 2004). Early interaction stage. The next stage of the lifecycle, the early interaction stage, is where the style and structure of the B2B relations hip is negotiated (Ford, 1982). High levels of uncertainty exist due to the lack of experience the firms have regarding each others business culture (Heffernan 2004). At this point Ma yer, Davis, and Schoor man (1995), state that as a relationship begins to develop, the trustor may be able to obtain data on the trustees integrity

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27 through third-party sources and observati on, with little dire ct interaction. This is where integrity is suggested by Mayer, Davis, and Sc hoorman (1995), to play the largest role in the development of trust due to the lack of benevolence the trustor has towards the trustee. As the relationship develops, the trustor wi ll be able to gain insight rega rding the intention of the trustee (Mayer, Davis, and Schoorman 1995). If a stro ng sense of benevolence is perceived by the trustor, the role of benevolence will positively impact the development of trust (Mayer, Davis, and Schoorman 1995). However, it is during this stag e of the lifecycle that the relationship is the most fragile due to the inexperience each organi zation has with one another and can be easily terminated prior to further development (Heffernan 2004). Relationship growth stage. In this stage, the relations hip is undergoing construction. There is a high level of intera ction and engagement between th e partners as mutual learning towards the specifics of the relationship are be ing established (Heffernan 2004). The exposure each firm gains from one another aids in the reduction of uncerta inty between the two organizations (Ford, 1982). Each firm is focused on learning the others business culture, standards, and identifying any adaptions needed for the partnership to be a success (Heffernan 2004). This stage, coupled with the first two, is where the creation of trust is most crucial (Wilson 1995; Jap 2001). Partnership stage. The partnership stage is the fourth stage along the lifecycle and is the point where the relationship is at its most mature point (Fort, et al 2003). The B2B alliances have developed a stable partnership where the learning that occurr ed in the relationship growth stage has proven beneficial as each organizatio n is now equally important to each other (Heffernan). The establishment of equal importa nce is the foundation for an implicit or explicit expression of maintaining a committed B2B relationship (Heffernan 2004).

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28 Relationship end stage. When the purpose of the re lationship no longer exists, the relationship end stage identifies how the partners uncouple the relationship (Heffernan 2004). A relationship can be terminated at any time duri ng the lifecycle, but the relationship end stage only occurs when the reason for the rela tionship no longer exists (Heffernan 2004). The B2B lifecycle outlines the natural progre ssion of relationship and trust development within new B2B alliances. Time is an important factor as trading partners may initially be familiar with a firms reputation, however time is the only way for a trading partner to demonstrate its integrity and predictability (Saunders et al. 2004). It is necessary to identify and understand the trust creati on process as the research indicate s that trading pa rtners throughout any supply chain are only willing to adopt new bus iness alliances and transaction commitments when trust is present (Saunders et al. 2004). Cross Cultural Interorganizational Trust A topic worth mentioning is the variation found in trust and trust developm ent when discussing international business alliances. Social norms di rectly affect how and if trust will develop among potential partners (Doney,et al. 1998 .). Lane (1998) identifies trust as extremely important for competing firms in foreign market s due to the enhanced uncertainty and possible differences found within each partners culture, values and business goals (Lane 1998). Doney et al. (1998) state that Since each cultures collective progr amming results in different norms and values, the processes trustors use to deci de whether and whom to trust may be heavily dependent upon a societys culture. As a result, it is important to understand how trust is developed in cross-cult ural B2B relationships. Huff and Kelley (2003) indicated that few aut hors have formally linke d the characteristics found within collectivist and indi vidualist societies with trust, while many scholars have associated high levels of trust among collec tivists, and low among indi vidualist societies.

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29 Dunning (1997) states that firms which are best able to id entify and reconcile (cultural) differences, or even exploit them to their gain, are likely to acquire a noticeable competitive advantage in the marketplace The purpose of this review is to identify some of the key social differences among cultures that can be associ ated with the development of trust. Individualism and collectivism describes how an individual and the pr evailing society as a whole relate (Hosfstede 1980a). Hofstede defines individualism and collectivism as: Individualism implies a loosel y knit social framework in which people are supposed to take care of themselves and their immediate families only, while collectivism is characterized by a tight social framework in which people distinguish between in-groups and out-groups; they expect their in-group to look after them and in exchange for that they feel they owe absolute loya lty to in (Hofstede 1980b). Within the literature the implied perceptions th at trust is high in co llectivist societies and low in individualist societies (Huff and Kelley 2003) has been substant iated by key cultural differences. Many argue the reason for high levels of trust among collectivists is due to their interdependent world view, and emphasis on nur turing relationships, while individualist do not hold the same perspective of the world and re lationships (Triandis 1989,1995; Chen, Xiao-Ping Chen, and Meindl 1998; Hofstede 1980a, 1980b). The primary example of a collect ivist society is that of Ja pans (Huff and Kelley 2003). Within Japans culture there is an emphasi s on achieving a form of harmony (Sullivan and Peterson 1982) where warmth, cooperation, sharing and fellowship are focused upon. Theoretically, once harmony has been achieved, th e development of trust within both personal and professional relationships wi ll occur (Huff and Kelley 2003), which is a major goal of the Japanese social structure (Hazama 1978 The literature does indicate that certain characteristics of the collectivists culture do prevent the development of trust (Huff and Ke lley 2003). One reason why the development of trust may be impaired is the distinct differe nce between those members of in-groups and out-

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30 groups (Triandis 1995). Collectivis ts prefer to be involved with in-group members and tend to be relatively ineffective with stra ngers. In general, when compared to individualists, collectivists use avoidance behavior, manipulate and compete with out-group me mbers more extensively then individualist do (Huff and Kelley 2003). This in -group preference reduces the possibility of trust development outside of group boundaries (Yamagis hi 1998a, 1998b). The desire for collectivists to belong to a group is learned through society an d aligns with their intrinsic desire to place group interests above personal in terests (Huff and Kelley 2003). However, in general, once an individual has been accepted as an in-group member, the level of trust found within this culture is much higher than that found within individualists societies (Huff and Kelley 2003). Yamagishi found that within several studies that in the Japanese culture, their dedication to joint monitoring and social sanctioning reduces the opportunity for free riding which results in co mmunity commitment to an organization or network. This is the basis for their preference to working with in-group members only but also explains why the Japanese culture in particular has been identifie d as a trusting society. In general, the Japanese firms have been praised fo r their lower transaction costs than US firms and are able to capture greater relati onal rents due to their cultural e nvironment that cultivates trust, goodwill and cooperation (Dore 1983; Dyer 1996; Hill 1995; Sako 1991). When researching individualist societies, t ypical countries include that of the United States, Canada, Germany and Australia (Huff and Kelley 2003). W ithin this form of society, individual ties are very loose wh ere people are primarily responsible to look after themselves and perhaps the interest of their immediate familie s (Hofstede 1983). A great deal of freedom is given to individuals within th e society (Hofstede 1983). Due to the loose relationships found among individuals, firms rarely trust each other (Huf f and Kelley 2003). This refers back to how

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31 an individuals propensity to trus t is directly influenced by societ ys trust levels (Mayer, Davis, and Schoorman 1995). Within individualist soci eties, Doney et al (1998) asserts that relationships are weak, trust is low and motives tend to be calculative. As a result, individualistic firms require greater levels of c ontractual guarantees to re duce the risk of B2B transactions. This ultimately causes higher tr ansaction cost for indi vidualist firms when compared to collectivist firms (Dyer and Singh, 1998) The key differences found within collectivists and individualist social structures is important within the scope of this research due to the number of countries that are key players within the US fresh grapefruit s upply chain. With Japan and th e EU being the top two offshore destinations for fresh, Florida grapefruit, th e existence of cultural differences should be acknowledges. Lenartowi cz (1999) states that Understanding the nature and influences of culture is central to international business. This becomes even more crucial as the EU is made up of twenty-seven unique countries with each po ssessing some degree of either a collectivist or individualists social structure.

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32 CHAPTER 3 THEORETICAL FRAMEWORK Analytical Hierarchy Process The Analytical Hierarchy Process (AHP) is widely docum ented and accepted as a decision making method when analyzing multi-criteria situations (Forman and Gass 2001). Founded at the Wharton School of Business by Thomas Saaty (Saaty, 1996), the process serves three basic functions: structuring complexity, measuring on a ratio scale, and s ynthesizing information (Forman and Gass 2001). The process allows decisions makers to model a complex problem in a hierarchical structure showing the relations hips of the goal, objectives (criteria), subobjectives, and alternatives (Forman and Selly2001). Due to the complexity of identifying the numerous elements of trust, the AHP method provides the foundation fo r this research. The ability of the AHP to enhance decision making during the choice acknowledged within the literature; however, its ability to be ap plied to all areas of probl em solving that include evaluation and measurement is not as recognized (Forman and Selly 2001). Forman and Gass (2001) wrote that, any situation that requires structuri ng, measurement, and/or synthesis is a good candidate for application of the AHP. For this reason, AHP is identified as the preferred method to evaluate the broadly defined concept of tr ust. As the most important elements of trust within international food chains are researched, AHP will allow for the application of data, experience, insight, and intuiti on in a logical and thorough way (Forman and Selly 2001). The basis for the AHP has been described as a pie chart. Each wedge represents an objective that contri butes to the overa ll goal of the process; a succe ssful decision. The objective wedges are further broken down into smaller porti ons to represent sub-objectives. Once the lowest levels of sub-objectives have been iden tified, they are divided into alternative wedges. Each alternative wedge represents the proporti on it contributes to the sub-objective. By

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33 prioritizing wedges, it can be determined how mu ch each alternative cont ributes to the overall decision (Forman and Selly 2001). To understand the structure of analysis, the three basic func tions of AHP must first be introduced. They are as follows: Structuring Complexity When evaluating multi-criteria decision, Forma n and Selly (2001) state the importance of structuring complex decisions into a hierarchy of importance. In order to achieve an accurate and unbiased decision, Forman and Selly (2001) accept the results of numerous experiments (Martin 1973; Miller 1956) that have shown the human brain to be limited in both its discrimination ability and short term memory. They conclude that if a human attempts to accurately evaluate anything above 7-9 variables at once, they w ould be unable to maintain an accurate comparison. By structuring multi-criteria s ituations into groups of alternatives, with less than 7-9 variables in each level, the human br ain is able to overcome its limitations to gain accurate and consistent decisions. The following example demonstrates the importance of structuring multi-criteria decisions. A professor would like to evaluate each stude nts participation throughout a semester on a grading scale of 0 to 100. There are 50 students in the classroom. Accuracy and consistency are often lost in the subjective nature of classroom participation. Martin (19 73) and Miller (1956) have proven that without accurate ly structuring the many criteria that contribute to the overall goal of the evaluation (a students participati ons grade), large inconsistencies are inevitable throughout the evaluations. They state that afte r about 7 to 9 students, the internal grading scale is impossible to remain consistent due to the proven limitations of the brain (Martin 1973; Miller 1956). Once the 50th student is evaluated, confusion among ranking will have occurred. As a result, the grades are impossible to defend obj ectively. For example, if student A receives a

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34 participation grade of 98, student B receives a gr ade of 94 and student C receives a grade of 49, did student A really participate twice as much as student C? What does a score of 98 really mean? Did they really not give 2% of the tota l participation required? What did student A do differently from student B to have receive a 4.3% higher grade? When evaluating students, professors want to remain objective, but due to brain limitations, many times evaluations such as the ex ample given above become very subjective and difficult to substantiate. However, when eval uating such complex problems that include both qualitative and quantitative elements, Saaty (1996 ) has shown that struct uring the criteria into groups of 9 factors or less can aid in obtaining reasonable consistency. Saaty states in his 1982 book that when looking at multi-criteria situations, AHP allows the decision maker to structure the problem as a hier archy. The first level of the hierarchy is to identify the overall goal of the decision process. From this point, he explains the development of sub-clusters based on identified obj ectives and criteria important to the overall decision goal. Decision makers continue to derive subclusters of related topics and criteria by decomposing preferences until the most general of factors is reached (Saa ty 1996). Once the structural complexity of the situation has been broken down in to subclusters, subsubcl usters, etc., the result is a decision tree (Saaty 1996). An example of a structured decision hi erarchy can be found in Figure 3-1. Forman and Se lly have stated that This hierarchical arrangement has been found to be the best way for human beings to cope with complexity. Trust Hierarchy The structure of the AHP within this research is a hierarchy of trus t shown in Figure 3-2. The hierarchy was developed by a group of Europ ean researchers whose focus is to determine B2B trust elements for food quality and food sa fety. Their research was the catalyst for determining the elements of trust within th e fresh grapefruit export market to the EU.

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35 In order to structure the complexity of B2B trust elements for food quality and food safety, the trust hierarchy has been structured to include five levels. Figure 3-2 outlines the bottom three levels as they are the most extensive in de tail. The first level states the objective of the decision tree which is to identify elemen ts of trust from the perspective of a buyer who is in the early stages of finding an international supplier. The second level of the hierarchy identifies three objectives of trust, which includes the product, seller and market environment The following level identifies numerous di mensions of trust with regard to the specified objective in the higher level. Each dimension number ed 1.x is in relation to the sellers product those dimensions numbered 2.x are in relation to the seller and those numbered 3.x are in relation to the market environment This level is the first level to be shown in Figure 3-2. Immediately below the listed dimensions of trust are sub-dimensions. The bo ttom level of the trust hierarchy outlines a wide variety of sources of trust found within each dimensions and sub dimensions of trust (Oosterkamp 2007). An example of how to follow the design of the trust hierarchy an be explained by following the path from top to bottom when evaluating the product being sold. From the explanation above it is known that from Figure 3-2 each criteria on the far left of the table numbered 1.x is in relation to the trust objective, product. From 3-2 there are 5 criteria outlined to contribute to the overall product objective. The criteria are : reputation, specification, inspection, certification and price/performance ratio To the right of the outlined criteria in Figure 3-2 is a list of many alternatives that contribute to the id entified criteria. For instance, if one was to follow the trust hierarchy from product to reputation there are 3 alternatives that contribute to the reputati on of a product. Those alternatives, as identified in Figure 3-2 are: intrinsic qualities, trade brand of the product, and region of origin. By following the trust

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36 hierarchy one level at a time, it is easily seen how the dimensions of trust have been structured from the top of the hierarchy throughout the bottom. Measuring on a Ratio Scale The second of the three major functions of the AHP is the im portance of ratio-scale measures, as stated by Forman and Selly (2001) In order to understand the value ratio-scale measures play within the AHP process, we mu st first define the f our accepted scales of measurement. Stevens (1946) iden tifies the scales of measuremen t to include nominal, ordinal, interval, and ratio-scales of measure. In th e order given, Forman a nd Selly (2001) recognize each scale as having the same properties as the on e behind it, plus a few. For example, ratio measures have the same properties as interval, ordinal and nominal measures, but interval measures do not have the same properties as ratio measures (Forman, Selly 2001). Further explanation will outline the prope rties of each measure and de monstrate the importance of utilizing the ratio scale within AHP. Nominal Nominal numbers are p laced on the lowest leve l of scale in terms of meaning conveyed. They are very simply numerical representations for names. They give no information regarding the ordering of the numbers and are for informa tional purposes only. An example of a nominal measure is a zip code. The numbers found in the zip code imply nothing mo re than the area in which you live (Forman and Selly 2001). Ordinal Forman and Selly (2001) define ordinal num bers to imply an order or ranking am ong variables that could be increasi ng or decreasing in order. For example, a ranking of universities based on student enrollment could place the university with the highest or lowest enrollment as #1. Regardless if the list is ascending or descen ding, there is no indicatio n of how much larger

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37 or smaller one university is compared to anothe r. When looking at ordinal measures the only information known is that one ranking is above another (Forman, Selly 2001). Interval Interval numbers contain the measure of bot h ordinal and nomi nal measure as well as providing information regarding the intervals between elements (Forman, Selly 2001). An example given by Forman and Selly (2001) is that of race horses. If the winning horse finished 15 links ahead of the second place horse we know the interval by which the first placed horse won, but we do not know if this was an impressive win. The example goes on to explain that if the horse had won by 15 links in a mile race, the win would have been really strong when compared to if the horse won by the same distance in a 2 mile race. In this situation it would be important to know the ratio of time between the first and second place horse in order to determine the strength of the win. Ratio Ratio measures incorporate all of the elem en ts described above, while also having the properties of ratios. Measures on a ratio scale indicate th at equivalent ratios are considered equal (Forman, Selly 2001). A good example of such measures is observing the difference in temperature between the Fahrenheit and Kelvin scale. Because the Fahrenheit measure does not have a ratio property it is incorrect to conclude that a temperat ure of 80 degrees is twice as warm as 40 degrees. However, when using the Kelv in scale to evaluate such a difference in temperature, the inference would be correct due to the ratio property within the Kelvin scale (Forman, Selly 2001). Because the ratio-scale provides more informati on than interval or ordinal measures, it is the foundation of the AHP process (Forman, Gass 2001). As previously outlined, the AHP allows for both objective and subj ective decisions. Forman and Ga ss (2001) state that this is

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38 done by eliciting pairwise relative comparisons that produce dimens ionless ratio-scale priorities. Ratio-scale measurements are important because as interviewees are asked to estimate the relative importance, preference, or likelihood depending on what is being evaluated the relative judgments produced fall along a dimensionless ratio-scale of priorities for which no scale exists (Forman, Gass 2001). The properties found within ratio-scale measures allow for individual judgments to be made throughout the decisi on tree without being based upon a set of agreed decision standards (Forman, Gass 2001). Once the decision tree is formed, pairwise compar isons are used to derive priorities within each cluster with respect to their parent cluster (Forman, Selly 2001). These relative comparisons are used to develop a ratio scal e of importance through the hierarchy system (Forman, Selly 2001). To achieve a global priority throughout the hierarc hy, local priorities of each element within a cluster are multiplied by the global priority of the parent element (Forman, Gass 2001). The details of this process ar e outlined further in this chapter. Consistency A measurement of decision inconsistencie s is an important by-product of conducting pairwise comparisons to derive priorities (Forman, Selly 2001). As pairwise decisions are made, a measure of consistency is important in order to determine that the j udgments were not made randomly. However, just as things in life are not consistent, AHP allows for those inconsistencies (Forman, Selly 2001). A consistency ratio of 10% or less is preferred; however there are certain parameters that would constitute the acceptance of a higher inconsistency rating (Forman, Selly 2001). High ratios of inconsistency found within AHP can occur due to a number of factors. Forman and Selly (2001) state reasons such as clerical error, lack of information, lack of concentration, real world app lication and inadequate model stru cture as all potential causes for evaluation inconsistencies. Decision inconsistencies may be a result of a ny one of these factors,

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39 however, it is important to remember that obtain ing a perfect inconsistency measure of 0 not be the goal of the decision making process. Forman and Selly (2001) state that a low inconsistency is necessary but not sufficient for a good d ecision. It is possible to be perfectly consistent but consistently wrong. It is more important to be accu rate than consistent. Synthesis The final objective of the AHP is to synthe size information to aid the decision m aker in his/her selection or future forecast (Forman, Ga ss 2001). Once all pairwise comparisons have been made for each portion of the decision tree, the information is synthesized in order to obtain an overall preference (Forman, Selly 2001). From this point a ranking of the alternatives in relation to the overall goal of the decision can be ge nerated (Forman, Selly 2001), which ultimately serves as a guidelin e for the decision maker. Research shows that decisions based on intuition can be adequa te, but intuition alone is not sufficient for making complex critical decisi ons (Forman, Selly 2001). AHP allows for the decomposition of multi-criteria situations to in or der to best identify key objectives that aid in complex decision making (Forman, Selly 2001). The Analytic Hierarchy Process (AHP) is not a magic formula, or model that finds the right answer. Rather it is a process that helps decision-makers to find the best answer (Forman, Selly 2001). Calculation of AHP Pairwise Comparisons Once the complexity of the prob lem has been structured into a decision tree, the relative weights (importance) of each criterion can be ca lculated. The first step in determining the importance of each criterion is to conduct a pairwise comparison of each variable found within a group of criteria. Saaty (1982) explains the use of a matrix as the preferred form when making pairwise comparisons due to its ability to offer a framework for testing consistency, obtaining

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40 additional information through making all possible comparisons, and analyzing the sensitivity of overall priorities to changes in judgment. Saaty (1982) also includes the ability of a matrix to handle the dominating and domina ted aspects of priorities. Saaty (1982) explains the first step in the pa irwise comparison process by beginning at the top of the decision tree hier archy to choose the criterion, C which will be used in the first comparisons. Then, from the level immediately below C the alternatives to be compared: A1, A2, A3 and so on are placed into a matrix. For the pur pose of this example, let us say there are four alternatives being compared under the first selected criteria. Figure 3.2 shows the elements as they are to be arranged in matrix form. To conduct the pairwise comparisons, compare A1 in the first column to A1, A2 and so on in the top row with respect to criteria C in the upper left had corner of the matrix. Continue the comparison process with element A2 in column one and so on throughout the matrix (Saaty, 1982). When a decision maker is performing the pairwise comparisons it is important that the alternatives in the first column of the matrix be co mpared to the elements in the first row. It is also important that the comparisons be phras ed consistently throughout the evaluation. For example, one way to compare the elements in Fi gure 3-1 could be phrased: Compared to criteria C, how strongly is alternative A1 preferred ove r alternative A2? There are many ways this could be phrased depending on the relationship the elemen ts have when compared to the criteria in the next higher level (Saaty 1982), however, the key concept is to maintain consistency throughout the comparison matrix. To fill in the matrix with pairwise comparisons Saaty (1982) utilizes numbers to represent the relative importance of one element over another with respect to the identified criteria. Table

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41 3.1 outlines the numerical scale used for the comp arisons that was derived from Saatys (1982) pairwise comparison scale. The values of 1 th rough 9 and their reciprocal values are identified with their given definitions to be used during th e pairwise comparison process. When comparing one element of the matrix with itself, the comparis on must be given a value of 1. As a result in Figure 3-3 the diagonal of the matrices has been completed with the value of 1 (Saaty 1982). From this point, the pairwise comparisons are completed by assigni ng each comparison a numerical values the scale in table 3-1. Once a pair of alternatives have been compared once, the reciprocal value is then used for the comparison of the second alternative to the first. For example, in Figure 3-3 in the comparison between ( A4, A3) the reciprocal values of the assigned judgment to comparison ( A3, A4) should be used (Saaty 1982). Deriving Weighted Ratios The following exam ple will be used to clarify the AHP process and its ability to evaluate subjective data. Suppose a student must make th e decision of which university to attend based on three alternatives each unive rsity athletic program, academic strengths and cost of tuition. To build the decision matrix the criteria, Unive rsity Satisfaction, is placed in the upper lefthand corner with the three factors that will contribute the decision of which university to is listed in the top row and far left column (Figure 3-4) By completing the matrix, the factor most important to the student in regards to U niversity Satisfaction will be identified. To complete the matrix the student was as ked: How much more important is the universitys athletic program when compared to it s academic strength and cost of living? This was consistent throughout the matr ix, while keeping in mind that the diagonal values of 1 were already assigned and the second set of evaluates are assigned the reciprocal value of the first evaluation. As a result, in this sample matrix, th ere were only three pairwise comparisons to be made. The pairwise comparisons made indicate that the student be lieves the athletic program to

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42 be moderately less important important than acade mic strength with a rating of 1/4 (Table 3-1), while the athletic program compared to the cost of living is equally important-moderately less important. The third comparison shows the student to believe that academic strengths when compared to the cost of living is more impor tant. By completing the pairwise comparison matrix, the decision maker is able to derive independent ratio scale priorities as opposed to randomly assigning weighted values to each (Forman, Selly 2001). Saaty (1982) explains the next step in the AHP to be synthesizing the judgments in order to get an overall estimate of prioritie s regarding which factors are more important to the student in terms of overall university satisfaction. To begin, add the values in each column (Figure 3-5). Then divide each value in each column by the sum of that column to normalize the matrix (Figure 3-6). The normalization of the matrix is an important factor as it allows for true comparisons among elements. Saaty (1982) concludes the final step in the process of obtaining a percentage of relative preference is to calcula te an overall average over the rows by summing the values in each row of the normalized matrix and dividing by the number of entries (Figure 3-7). Figure 3-6 yields the percentage of overall re lative preference when comparing the athletic program (13%), academic strength (67.6%) and cost of living (19.2%) within a particular university. It can be concluded that academic strength is three times as important to overall university satisfaction when compared to the cost of living and athletic program. The athletic program is only slightly less important when compared to the cost of living element When looking at a simple example of a decisi on hierarchy such as determining university satisfaction with only three alternatives the relative priorities of the matrix are also considered the overall priorities for the entire decision tree as there are only two leve ls in the hierarchy. However, for more complex decisi on hierarchies, additional calculations are needed in order to

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43 compute the overall priorities for each sub clus ter in respect to the entire hierarchy. The calculation is simple, yet provides an overall picture to which crit eria are most important within the hierarchy. To compute the ov erall priority rating, the relative rating of each alternative is multiplied by the priority percentage of the corresponding criteria. The highest level of the decision hierarchy is the only situa tion where the relative priorities ar e also the overall priorities. This is because of the fact that there are no priority percentages of criteria above that level. By calculating the overall prio rities, the relative priorities will be normalized throughout the hierarchy to develop a global priority rati ng for the entire decision three (Saaty 1982). Through this synthesis of information a ratio of relative and overall priorities were identified, but the issue of consistency must be addressed in order to confirm that the comparisons were not made randomly. Deriving the Consistency Ratio The CR is an important factor of the AHP. Si nce each set of criteria are placed into a matrix of alternatives to be evaluated agains t, it is important to have some measure of consistency throughout the decision process. The CR serves only as an indicator of regularity throughout the decision process wh ile a consistency ratio of 0 should not be the goal of the pairwise evaluations (Saaty, 1982) The purpose of the consistenc y ratio is to warn of random judgments, not to guarantee perfect ly consistent evaluations. When looking at the university example, the percentage of relative preference has been calculated, but the decision process has not been test ed for consistency. In order to calculate the consistency ratio, the percentages of relative pr eferences (13, 67.6, and 19.2) serve as the priority vector of the three alternatives with respect to overall university satis faction (Saaty 1982). To begin the consistency index calculation, mu ltiply each column of the original decision matrix by the corresponding priori ty value (Figure 3-8). Once the priority vector has been

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44 multiplied throughout the decision matrix, sum the va lues in each row (Figure 3-9). From this point one begins to solve for lambda ( ) max. This is done by dividi ng the values in the column of row totals (Figure 3-9) by the corresponding value in the priority vector. An average is then found of the three entries (E quation 3-1) in order to co mplete the calculation of max. Lambda max is then inserted into the consistency index (CI) formula (Donegan et al) found in Equation 3-2 (Saaty 1982). The final step in solving for the consistency ra tio is to insert the CI into the consistency ratio (CR) formula (Donegan et al) in order to determine the overall consistency of the decision matrix. In that formula the CI is divided by the random consistency index (RCI) which is provided in Table 3-2. S aaty provides this index as a give n in his 1982 book publication. If the overall CR is less than 10%, the pairwise compar ison judgments are considered to be consistent (Equation 3-3) (Saaty 1982). Role of AHP in Supply Chain Management The primary reason for selecting the AHP as th e theoretical foundation for this research is its ability to struc ture the complexity of possible decision alternatives, give quantitative measurements of pairwise comparisons which result in the synthesis of new information to aid in the decision making process (Forman and Gass 2001). B2B supply chain participants are often f aced with many choices that are followed by some form of a decision. As a result, the AHP process can be applied within the world of organizational supply chain management. Akar te, Surendra, and Ravi (2001) conducted a study evaluating the supplier selection process by usin g the AHP method within the automotive casting sector. They believed that suppliers within the s ector were no longer being selected due to price, but rather the overa ll capability of the supplie r to meet the buyers needs. In order to meet supplier selection criter ia of both qualitative and quantitative function, AHP is the decision

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45 method of choice as it allows for both forms of crit eria to integrate to aid in the synthesis of a decision (Akarte, Surendra, and Ravi 2001) Within their work, Akarte, Surendra, and Ravi (2001) begin their AHP analysis of selecting an automobile casting supplier by structuring the complexity of the problem. Through research and industrial inves tigation, they identif ied four groups of criteria: Product Development Capability, Manufact uring Capability, Quality Capab ility, and Cost and Delivery. They further structured these four identified criteria into the third level of the hierarchy which provides detailed subclusters of criteria derived from the variables in level 2. After the completion of the decision tree, Akarte, Surendra, and Ravi (2001) utilize the 1-9 scale of AHP to assign relative weights to each of the criteria using pairwise comparisons. They follow the levels of the decision hierarchy to assign the relative weights. They first evaluate the four criteria derived from the ma in objective, and then evaluate the sub-criteria in level three. Within their research, both object ive and subjective data are bei ng evaluated, which require two performance measurement calculations (Akarte, Su rendra, and Ravi 2001) Objective criteria such as total cost and pr oduction accuracy are evaluated depending on if a maximum or minimum value of the criterion is most preferred (Akart e, Surendra, and Ravi 2001). If a maximum value is desirable (such as maximum part size capability), the performance measure is calculated by normalizing the values If the minimum value of the criterion is preferred (such as the lowest co st supplier must have the highes t preference), the reciprocal of the values is first taken and then normalized to calculate the performance measure. When conducting a subjective criteria evaluation, the relative performance measure of each sub-cluster criteria is calculat ed by quantifying qualitative ratings through pairwise comparisons. Akarte, Surendra, and Ravi (2001) give the exam ple of rating the cas ting complexity of a

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46 supplier with qualitative values of low, medium, high and very high. By conducting a pairwise comparison of all of the sub-cr iteria found under a certain criteria, quantifiable weights are given to qualitative measures. Once the quantifiable performance values are determined, they too are normalized to ensure consistency among objective a nd subjective criteria (Akarte, Surendra, and Ravi 2001) After all of the identified criteria have been evaluated, the overall sc ore of the supplier is obtained by the sum of the product of the performan ce measure of the supplie r for each criterion. Akarte, Surendra, and Ravi (2001) then identify the supplier with the highest overall score to be considered the most suitable business partner. The ultimate result of utilizing the AHP process is the synthesis of a quantifiable data to aid in the decision of selec ting casting supplier. Akarte, Surendra, and Ravi (2001)then go on to create a web-based decision support system for casting supplier evaluation and buyersupplier interaction. This transition into the field of e-commerce is important to the greater goal of this research by es tablishing the foundation of supplier selection based solely on e-commerce communications. However, the key ideas pulled from the work of Ak arte, Surendra, and Ravi (2001) is the use of AHP directly within the discipline of supply chain management and supplier se lection. From their work, the application of AHP within the grapefruit export supply chain w ith the purpose of identifying trust elements is substantiated.

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47 Figure 3-1. Structured decision hierarchy

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48 Figure 3-2. Trust hierarchy (Source: Oosterkamp 2007)

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49 C A1 A2 A3 A4 A1 1 0 0 0 A2 0 1 0 0 A3 0 0 1 0 A4 0 0 0 1 Figure 3-3. Sample matrix for pairwise comparison Table 3-1. Scale of relative importance Data Entry Value Definition 9 Absolutely dominating 8 Much more important absolutely dominating 7 Much more important 6 More important more important 5 More important 4 Moderately more important more important 3 Moderately more important 2 Equally important moderately more important 1 Equally important 1/2 Equally important moderately less important 1/3 Moderately less important 1/4 Moderately less important less important 1/5 Less important 1/6 Less important much less important 1/7 Much less important 1/8 Much less important absolutely inferior 1/9 Absolutely inferior University Satisfaction AP AS CL Athletic Program (AP) 1 1/4 1/2 Academic Strength (AS) 4 1 5 Cost of Living (CL) 2 1/5 1 Figure 3-4. Completed sample matrix

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50 University Satisfaction AP AS CL Athletic Program (AP) 1 1/4 1/2 Academic Strength (AS) 4 1 5 Cost of Living (CL) 2 1/5 1 Sum the values in each column Column Total 7 1.45 6.5 Figure 3-5. Synthesizing judgment ratios University Satisfaction AP AS CL Athletic Program (AP) 1/7 .25/1.45.5/6.5 Academic Strength (AS) 4/7 1/1.45 5/6.5 Cost of Living (CL) 2/7 .2/1.45 1/6.5 Divide each entry in a column by the column total. Figure 3-6. Normalized matrix University Satisfaction AP AS CL Relative Preference Athletic Program (AP) .143 .172 .077 .130 Academic Strength (AS) .571 .689 .769 .676 Cost of Living (CL) .286 .138 .153 .192 Sum the values across each row and divide by the number of entries in each row Figure 3-7. Relative preferences

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51 University Satisfaction AP (0.13) AS (.676) CL (.192) Athletic Program (AP) 1 .25 .5 Academic Strength (AS) 4 1 5 Cost of Living (CL) 2 .2 1 Figure 3-8. Multiplication of priority vector University Satisfaction AP (0.13) AS (.676) CL (.192) Row Total Athletic Program (AP) .13 .169 .096 .395 Academic Strength (AS) .52 .676 .96 2.16 Cost of Living (CL) .26 .135 .192 .59 Figure 3-9. Totaling of priority vector multiplication .395.133.04 2.16.683.17 .59.193.11 max3.043.173.119.32 3.11 33 Equation 3-1 max3.113 .055 12 n CI n Equation 3-2 .055 9.48%10% .58 CI CR Consistent RCI Equation 3-3

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52 Table 3-2. Random consistency index Matrix Size 1 2 3 4 5 6 7 8 9 10 RCI 0.0 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

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53 CHAPTER 4 DATA COLLECTION In order to identify the elements of trust th at exist along the US -EU fresh grapefruit supply chain, one-on-one interviews were conducted with every fresh grap efruit exporter shipping to the EU from the state of Florida. During the interviews, exporters we re asked to conduct a series of pairwise comparisons that were derived from a developed trus t hierarchy. The criteria and alternatives of the trust hierarchy were formulat ed into a macro-enabled spreadsheet that allowed the interviewees to adjust a computer generated bar diagram to indicate th e appropriate ratio of importance one alternative possessed over another. An in depth discussion of each of the mentioned data collection elem ents can be found below. Firm Selection In order to obtain the m ost accurate inform ation from the one-on-one interviews, an indepth investigation was conducted to identify all Florida exporters of fresh grapefruit to the EU. From a list of 45 licensed citrus exporters provi ded by the Florida Depart ment of Citrus (2008), 10 are currently exporting fresh grapefruit directly to the EU. From the remaining 45 licensed exporters, 7 are curre ntly exporting fresh grapef ruit to the EU by brokering their fruit to one of the 10 direct exporters. Of the remaining licensed exporters, they are either not exporting fresh grapefruit or not participati ng in the European market. All 10 direct exporters of fresh grapefruit to the EU are locate d in the Indian River citrus region of the state and were primarily centered in the Vero Beach area of Florida. Due to the reasonable size of the population, a census survey of all 10 exporters was conducted. Data Collection Process Interviews were conducted with a pe rson with in each firm that would be knowledgeable about European buyers. The interviews averaged 30-45 minutes in length. The participants

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54 answered general questions rega rding the size and type of comp any they worked for before conducting the pairwise comparisons This was to get a general idea of how large a player the company was in the European market for fresh grapefruit. Each interviewee was then asked to evalua te the pairwise comparisons by taking the perspective of what they felt thei r European buyers of fresh grapefru it would prefer. In order to uphold Saatys emphasis on maintaining consistency within the phrasing of the question, as mentioned earlier, each participant was asked the same question. Depending on which trust objective was being evalua ted, interviewees were asked: In regards to your product which alternative do you feel your European buyers prefer? Your product was interchanged with your company, and market environment. Also to maintain consistency, in the event that a participant questioned the definition of an alternative duri ng the comparison the trust hierarchy was used for clarification (Figure 3-2). By definition, the hierarchy is decomposed into clusters and subclusters that contribute to the level above it. As a result, if a participant asked for the applied definition of inspection with regards to the product the definition given was found in the level immediately below inspection which includes: 1.3.1 Physical examination of product, 1.3.2 Laboratory analysis of product sample and 1.3.3 visit to Production site. From this point, interviewees were asked to conduct the pairwise comparisons by utilizing a bar graph as shown in Figure 41. The priority judgments were assigned by moving the middle bar in the direction of the preferred objective. The degree to which the participant moved the bar served as an indication to how much more an objective wa s preferred over another by automatically assigning a numeric value that co incides to Saatys (1982) scale of relative importance (Table 3-1). By utilizing this met hod, interviewees were allowed to assign visual weights to subjective material. Th e data collected during the interviews is comparable due to the

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55 use of ratio-scale measures. As a result, each participant develops a unique ratio-scale of measure as they assign judgments to each comparison. For example, participant A may be more extreme in their judgments while participant B may be much more conservative in their assessment, however their results could be th e same as each intervie wee has inadvertently developed a unique ratio-scal e of measure that can be normalized and compared. Each level of the trust hierarchy was input into a pairwise co mparison matrix and participants judgments were ente red into each matrices by using the bar tool. Figure 4-2 shows this concept. Participants only saw the gra phical judgment tool during the interview. The comparison matrices and calculations were all behind the scenes in the programming of the macro-enabled spreadsheet. Figure 4-2 shows a situation where only three pairwise comparisons need to be made in order to complete the trust objective matrices. This coincides with Saatys (1982) use of reciprocal values in the second comparison of alternatives. As a result, the programming of the spreadsheet electronically filled in the lower portion of the matrix with the reciprocal values of the first judgments. The final element of the data collection pr ocess was acknowledging the consistency ratio. As each participant evaluated a pairwise comparison group, the consistency ratio was simultaneously being calculated with each judgmen t. The consistency ratio was only complete once all judgments had been made for the identif ied criteria. As AHP does allow for a CR of 10% or less, the spreadsheet indicated if the judgments fell within this accepted range. The consistency measure was on the screen for partic ipants to see, however the measure was not pointed out to participants, as it is not the goal of the comp arison process to obtain perfect consistency.

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56 After all comparisons had been made for each section of the decisi on, participants were asked to review their judgments in order to make sure their answers were as they intended. In several interviews, once the participant was asked to review their answers, the slight changes they made did alter the CR to decrease from a rejected level to an accepted level. However, in several situations, even after the participant wa s asked to review their judgments, the CR would still indicate an unacceptable level of consiste ncy. These inconsistencies do not render the answers invalid, as referred to in Chapter 3, bu t reasons for these will be discussed later. As a final measure to ensure th e interviewees agreed with th eir indicated preferences, the calculated relative priorities were reviewed. If at a ny point the participants did not agree with what the final calculations of the pairwise comp arisons, the sub-cluster in question was revisited. The interviewees were able to re-evaluate their decisions in an attempt to reduce the chance of human error when using the macro-enabled spre adsheet. Only during two interviews did the interviewee ask to revisit a portion of the evalua tion. In both instances the participant accidently slid the decision bar in the opposite direction they intended. This wa s the last step in insuring the data collected was as accurate as possible. Data Tables 4-1 through 4-6 outline the inform ation ga thered during the interviews. The tables are arranged according to the trust hierarchy (F igure 3-2). The pairwise comparison numbers coincide with the numerical values assigned in Figure 3-2, keeping in mind that the dimensions numbered 1.x are in relation to the sellers product, those dimensions numbered 2.x are in relation to the seller and those numbered 3.x are in relation to the market environment. The priority values coincide with S aatys (1982) scale of relative im portance (Table 3-1) in decimal form. The last row of each table indicates the CR fo r each firm in relation to that hierarchy level.

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57 Note that in Table 4-4 the CR is inapplicable because only one pairwise comparison is being made. For example, in table 4-1 each firm is iden tified on the top row with their assigned determined judgments for each pairwise comparison below. In the far left column the pairwise comparison being made is identified in accordance to the identified trust hierarchy (Figure 3-2). From table 4-1, Firm D placed a value of 8.4 when evaluating the product compared to the company. Firm D placed a value of 8.3 when evaluating the product compared to the market environment and a value of 0.13 was given when comparing the company to the market environment. The definitions of the given values can be found in Table 3-1.

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58 Object 1 Object 2 Figure 4-1. Pairwise gr aphical judgment tool Trust Objectives placed into matrix Buyer Trust P S M Product of the Seller (P) 1 Seller (S) 1 Market Environment (M) 1 Trust Objectives converted into the Graphical Judgment tool. Product Seller Product Market Environment Seller Market Environment Figure 4-2. Visualization of a pplied graphical judgment tool Table 4-1. Evaluation of the 1st hierarchy level Pairwise Comparisons Firm A Firm B Firm C Firm D Firm E Firm F Firm G Firm H Firm I Firm J 1 2 1 0.71 9 8.4 7 0.12 1.1 1 3 2.6 1 3 0.42 0.59 9 8.3 3.3 1 6 5.6 1 2.4 2 3 0.5 0.59 9 0.13 6.9 1 1 5.6 0.24 4.2 CR .10 .10 > .10 > .10 > .10 > .10 > .10 .10 .10 > .10

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59 Table 4-2. Evaluation 1 w ith respect to product Pairwise Comparisons Firm A Firm B Firm C Firm D Firm E Firm F Firm G Firm H Firm I Firm J 1.1 1.2 .28 1.4 0.11 6.1 0.42 5 5.3 1 0.5 7.2 1.1 1.3 0.19 0.37 0.11 0.26 0.36 3 0.18 1 0.5 7.2 1.1 1.4 0.2 0.36 0.11 0.26 0.36 3 0.18 1 0.5 7.2 1.1 1.5 0.2 1 0.11 0.13 0.29 5.3 0.19 5.6 0.23 4.2 1.2 1.3 3.6 0.48 1.1 1 3.2 1 0.19 5.7 2.2 0.16 1.2 1.4 3.4 0.4 0.11 0.26 3.2 1 0.11 5.7 0.56 0.16 1.2 1.5 0.31 0.3 0.11 0.13 3.4 3 0.19 1 0.29 0.11 1.3 1.4 2.9 0.5 1 0.23 0.4 0.3 0.19 0.18 0.5 2 1.3 1.5 0.38 1 0.11 0.12 1 1 0.19 0.19 0.27 0.36 1.4 1.5 0.48 2.5 1 0.16 0.21 0.3 6.1 5 0.28 0.31 CR > .10 .10 > .10 > .10 > .10 > .10 > .10 >.10 .10 > .10 Table 4-3. Evaluation 2 w ith respect to seller Pairwise Comparisons Firm A Firm B Firm C Firm D Firm E Firm F Firm G Firm H Firm I Firm J 2.1 2.2 0.59 0.25 9 0.21 7 0.31 1 5.4 1 4.2 2.1 2.3 0.56 1.5 1 0.14 7 0.42 0.19 0.25 1 0.17 2.1 2.4 0.53 1.5 9 1 7 1 1 1 1 4.5 2.1 2.5 0.36 0.5 1 0.19 7.1 0.48 0.11 3 2.6 0.14 2.2 2.3 2.2 1 0.11 6.2 4.6 4 1 0.21 1 0.23 2.2 2.4 1 2 9 6.4 5.5 4 5.5 0.91 1 5.5 2.2 2.5 0.56 2 0.11 1 5.1 1 1 2.4 3.3 0.21 2.3 2.4 1 1.6 9 6 7.9 4 1 1 1 6.1 2.3 2.5 0.45 1 0.11 1 7 3.1 1 2.6 2.6 1 2.4 2.5 0.53 0.59 0.11 0.18 0.18 0.31 0.11 2.6 2.8 0.16 CR .10 .10 > .10 > .10 > .10 > .10 > .10 > .10 .10 > .10 Table 4-4. Evaluation 2.2 with resp ect to relationship with seller Pairwise Comparisons Firm A Firm B Firm C Firm D Firm E Firm F Firm G Firm H Firm I Firm J 2.2.A 2.2.B 4.7 1.9 0.19 8.6 7.8 5.2 5.3 5.8 5.1 7.5 CR -not applicable

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60 Table 4-5. Evaluation 2.3 with respect to reliability of the seller Pairwise Comparisons Firm A Firm B Firm C Firm D Firm E Firm F Firm G Firm H Firm I Firm J 2.3.A 2.3.B 2.6 3.3 1 0.14 0.13 5.7 0.19 1.1 0.25 0.3 2.3.A 2.3.C 2.7 0.56 4.8 8 8 2.1 5.9 3.7 0.45 5.6 2.3.B 2.3.C 2.8 1 4.7 7.9 7.9 2 5.9 3.8 3 4.3 CR > .10 > .10 .10 > .10 > .10 > .10 >.10 .10 .10 .10 Table 4-6. Evaluation 3 w ith respect to market Pairwise Comparisons Firm A Firm B Firm C Firm D Firm E Firm F Firm G Firm H Firm I Firm J 3.1 3.2 7.2 6 6.4 8.4 9 5.3 9 3.8 1.8 0.37 3.1 3.3 7.2 4.3 6.5 8.3 9 4.8 9 3.5 1 0.34 3.2 3.3 5.5 1.2 1 0.13 1 4.9 0.19 0.26 1 0.31 CR > .10 .10 .10 > .10 .10 > .10 > .10 > .10 .10 > .10

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61 CHAPTER 5 EMPERICAL MODEL RESULTS Results By using the macro-enabled spreadsh eet as the data collection tool, the calculations needed to derive the weighted priorities were running be hind the scenes as part icipants entered their comparison judgments. Much like a SAS programmi ng model, once a data set has been obtained and the necessary series of ma thematical equations have be en programmed, the econometric model is run in order to obtain the results. The macro-enabled spreadsheet method of data collection coupled with the mathematical analysis of the AHP a llowed the mathematical results of the model to be instantaneous. To demonstrate this point an in-depth explor ation of how the data collected from firm A during the pairwise comparisons in the first level of the decision hierarchy were transformed into the solutions of the AHP. Figur e 4-3 shows how the trust objectives were converted into a matrix and then presented to interviewees in the macro-enabled spreadsheet Figure 5-1 provides the data collected from firm A top hierarchy le vel as a completed decision matrix. Figures 52 through 5-6 and Equations 5-1 through 5-3 then follow the outlined mathematical procedure for calculating the relative preference vector, co nsistency index and the consistency ratio as outlined in Chapter 3. The rela tive preference values in Figure 5-4 match the figures found in Table 5-2. The acceptable CR in Equation 5-3 aligns with the CR indicator in Table 4-1. This mathematical process was repeated for each node of the decision tree within the macro-enabled spread sheet. The results of each interview have been summari zed and organized into data tables below. Each table (Tables 5-2 through 5-11) indicates the relative priority percenta ge of each alternative

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62 when compared to the correspondi ng criteria as well as the over all priorities for each trust objective its sub-cluster with in the decision hierarchy. Cumulative Results When looking at the overall resu lts of the 10 firms, the cumu lative findings give a strong indication to which elements of trust are the m ost important to buyers of fresh grapefruit in the EU when looking to make the buy decision from US producers. Table 5-1 provides the results of all 10 firms averaged together and the range of each comparison to gain a cumulative perspective of the collected results. When evaluating the first level of the decisi on hierarchy, when asked to compare between the product the selling company and the market environment 5 of the 10 firms ranked the product as the most important object of trust to Eu ropean buyers. Another firm evaluated the product criteria to have equal pr iority preferences as the selling company. Overall, when the results were averaged together the product was given an overall weig hted priority of 47.79% (Table 5-1). In the second level of th e hierarchy, the results of the sub-cluster beneath the product criterion were diverse. Four of the firms placed pric e/performance as the top contributor to the success of the overall product criteria. When averaged together, price/performance was given an overall priority of 14.78% (Table 5-1) within the product cluster. The remainder of the firms were split in their priority rankings between certification, reputation and specification. Referring back to the first level of the hier archy, it is worth noting that three of the 10 firms interviews placed the market environment as the number one trust indicator to European buyers. This is mainly due to the sub-alternative of private control institutions. For the purposes of the interview, private control institutions were defined to be things such as ISO certification and EurepGap. They are institutions that contri bute to the market environment of the product,

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63 but are not required by the domestic government to be exported. Nine of the 10 firms ranked this sub-alternative as the number one co ntributor to the importance of the market environment to buyers in the EU. This is primarily due to consumer preferences regarding chemical residues on fresh fruit within the European market. Various private control institutions serve as indicators to buyers in the EU that the product meets a higher standard than is placed on it by the USDA, which is considered a public legal institution. The selling company criterion was ranked second in overall importance with an overall averaged priority of 28.07% (Table 5-1). Within the level immediately below it, the alternatives of competence to solve problems and relationship with the buyer and reliability were all relatively close in overall averaged rankings An interested fact worth mentioning is when evaluating the third level of th e hierarchy below the criterion relationship with the buyer 9 of the 10 firms indicated that the relationships with individuals were the most important factor in regards to the overall relationship with the buyer Table 5-1 indicates the averaged relative priority percentage to be 76.68%. This is a strong indication that European buyers will follow an export sales manager from company A to company B if a sounds relationship was developed between the individuals ma king the B2B transactions. Overall, the 10 firms interviewed place a high relative priority rating on their product as the number 1 indicator of trust in the eyes of European buyers w ho are seeking new exporters of fresh grapefruit from the US. Of the alternatives that contribute to th e success of the overall product, price/performance was ranked as the most important. Acknowledging the Consistency Ratios The issue of consistency must be addressed. Wh en evaluating the data collected, Firm I was the only firm to obtain and consistency rati o of 10% or less in each of the 6 comparison matrices. However, this does not indicate that the data gathered from the remaining 9 firms is to

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64 be deemed unacceptable. Forman and Selly stat e that under certain circumstances higher levels of inconsistency can be accepted. Satty (1982) also concludes that when looking at a CR over 10%, the judgments may be somewhat r andom and should perhaps be revised. None of the literature reviewed provides a cl ear-cut indication of wh en to accept and when to reject data based on the CR. However, Fo rman and Gass (2001) do provide an example where if the inconsistency is 40 or 50%, it can be c oncluded that something is wrong. Because each set of results were reviewed and approved by participants, it is not concluded that any of the comparisons observed were made randomly. Ma ny of the CRs reported in Chapter 4 (42%) were only slightly over the acceptable level of consistency (<20%), and will be accepted. Due to the fact that each participant ag reed with the results their co mparisons generated, it is not applicable to reject the judg ments. However, the remainder of the unacceptable CRs were above 20% and serve as an indication that some thing was wrong. Based on the listed causes of high CRs outlined by Forman and Selly (2001), some rationalization can be provided. Forman and Selly (2001) first list clerical error as being a potential cause for inconsistency. This is not thought to be the primary cause of gather ed levels of inconsistency as each participant was asked to review his/her answ ers prior to moving on to the next portion of the evaluation. However, there is no way of determining if the individual didnt catch their clerical mistake while reviewing the provided judgments. Form an and Selly (2001) state that this form of error can be very difficult to detect in many computer analyses. Lack of Information is the second recognized cause of inconsistency by Forman and Selly (2001). They state that if one has little or no information about the factors being compared, then judgments will appear to be random and a high inconsistency ratio will result. Because each individual interviewed is a key player within the export market of fresh grapefruit to Europe

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65 and has extensive hands-on experien ce, it is not thought that this reason of inconsistency can be applied to the gathered data set. However, due to the fact that each interviewee was taking the perspective of they felt their Euro pean buyers prefer, there is a possi bility that this factor could play a role in skewing CRs to an unacceptable level. The third possible explanation provided by Fo rman and Selly (2001) regarding high CRs is the lack of concentration of the interviewee. Because e ach interview was done in person and participants were observed while completing the comparisons, it is concluded that each person was focused on producing accurate data. However, a lack of concentration could be applicable when considering the level on concentration given towards applying the given definitions of each criteria and alternative within the trust hierarch y. This is point is made due to the confusion many participants had with altering their perceive d definition of a term to the one used for the purposes of the interview. One prime example of this is th e confusion generated with the market environment objective For the purposes of the study, the alternatives found within the market environment cluster were identified as private control institutions informal institutions and public legal institutions. Private control institutions are defined as institutions such as EurepGap. Informal institutions are defined as things fou nd within the environment of the exporting country that no one could control-such as social norms, gove rnment stability, and exchange rates. Public legal institutions are defined as government agencies that provide the legal regulations for products being grown and exported from the US such as the USDA. When these definitions were given, each participant asked for them to be repeated several times and several struggled with ignoring their pe rsonal definition of a m arket environment in order to apply the one utilized by the trust hierarchy. For this reason, it is thought that several of

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66 the high inconsistency ratios could be due to part icipants utilizing their personal definition of a market environment as opposed to actively using the one provided. The fourth possible explanation given by Form an and Selly (2001) to explain high CRs is that the real world is not always consistent. This could definitely be applied within the realm of this research as several of the participants explained their frustration with the pairwise comparisons by stating, it depends. The world fresh grapefru it supply, exchange rates, production quality, and demand all play continuous ly changing roles in the exporting of fresh grapefruit to Europe. The constantly changing co nditions of the market can potentially alter the perceived preferences of European buyers. For this reason, each participant was asked to apply judgments in accordance to what the market most commonly reflects, however, many of the participants struggled with establishing one perceived preference over the other. Inadequate model structure is the final reason of high CRs given by Forman and Selly (2001). They state that a decision hierarchy should be developed where each alternative can be comparable within the magnitude of the given AHP scale (1-9). If the hierarchy is not constructed properly the decisions maker will be forced to ma ke extreme judgments throughout the pairwise comparison, which will result in a hi gh CR. This model structure used is believed to be correctly structured, howev er the explanation of extreme judgments can be applied to the data gathered from firm C. Three of the six comparison matrices completed by firm C utilized the extreme judgment of 9 or 1/9 in every comparison. It is not concluded th at this is due to an inadequate model, but an extreme sense of judgmen ts as each individual is allowed to develop a unique ratio-scale of measure is applicable. The answers given by firm C did reflect transitivity where A is greater than B and B is greater than C, however in such situatio ns the calculations of AHP result in high CRs.

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67 In summary, the data collected during the 10 interviews will be accepted regardless of the inconsistency ratio. The primary reason for accept ance is that each firm agreed and approved the results of their interview coupled with the fact that no clear li ne is given in the literature regarding when to accept or reject data base d on a high CR. The sec ondary reason to accept the data is based on the in-depth analysis of th e reasons given by Forman and Selly and their application within this data set. Buyer Trust P S M Product of the Seller (P) 1 1 .42 Seller (S) 1 1 .50 Market Environment (M) 2.4 2 1 Figure 5-1. Completed decision matrix from firm A: t op hierarchy level Buyer Trust P S M Product of the Seller (P) 1 1 .42 Seller(S) 1 1 .50 Market Environment (M) 2.4 2 1 Sum the values in each column Column Total 4.4 4 1.92 Figure 5-2. Synthesizing judgment ratio s from firm A: top hierarchy level

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68 Buyer Trust P S M Product of the Seller (P) 1/4.4 1/4 .42/1.92 Seller (S) 1/4.4 1/4 .50/1.92 Market Environment (M) 2.4/4.4 2/4 1/1.92 Divide each entry in a column by the column total. Figure 5-3. Normalized matrix fr om firm A: top hierarchy level Buyer Trust P S M Relative Preference Product of the Seller (P) .227 .25 .219 .232 Seller (S) .227 .25 .260 .246 Market Environment (M) .545 .5 .521 .522 Sum the values across each row and divide by the number of entries in each row Figure 5-4. Relative preferences of firm A: top hierarchy level Buyer Trust P (0.232) S (.246) M (.522) Product of the Seller (P) 1 1 .42 Seller(AS) 1 1 .50 Market Environment (M) 2.4 2 1 Figure 5-5. Multiplication of priority vect or for firm A: top hierarchy level Buyer Trust P (0.232) S (.246) M (.522) Row Total Product of the Seller (P) .232 .246 .219 .697 Seller(AS) .232 .246 .261 .739 Market Environment (M) .557 .492 .522 1.571 Figure 5-6. Totaling of priority vect or for firm A: top hierarchy level

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69 max.697.2323.00 3.003.013.019.02 .739.2463.01 3.01 93 1.571.5223.01 Equation 5-1 max3.013 .003 12 n CI n Equation 5-2 .003 .52%10% .58 CI CR Consistent RCI Equation 5-3

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70 Table 5-1. Cumulative averaged results for all 10 firms Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 47.79% 47.79% Range (14% to 77.8%) 1.1 Reputation 18.2% 8.69% (2.2% to 55.7%) 1.2 Specification 16.21% 7.74% (2.8% to 40%) 1.3 Inspection 11.79% 5.63% (3.3% to 22%) 1.4 Certification 22.94% 10.96% (8.4% to 54.1%) 1.5 Price Performance 30.93% 14.78% (12.1% to 59.8%) 2. Selling Company 28.07% 28.07% Range (6% to 57.7%) 2.1 Capability 18.8% 5.27% (4.8% to 56.7%) 2.2 Relationship with the buyer 23.16% 6.5% (5.4% to 44.9%) 2.2.A Relationship with individuals 76.68% 4.98% (15.6% to 89.6%) 2.2.B Relationship with Companies 23.31% 1.51% (10.4% to 84.4%) 2.3Reliability 23.3% 6.54% (13.7% to 39%)

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71 Table 5-1. Continued Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 2.3.A Adequate communication 34.92% 2.28% (13.2 to 63.3%) 2.3.B Deliveries 50.56% 3.3% (19.5% to 75.7%) 2.3.C Financial situation 14.52% .95% (4.8% to 39.1%) 2.4 Reputation of selling company 10.04% 2.81% (2.1% to 22.9) 2.5 Competence to solve problems 24.71% 6.94% (5.6% to 48.7%) 3. Market Environment 24.15% 24.15% Range (4.2% to 52.3%) 3.1 Private Control Institutions 64.71% 15.63% (14.2% to 81.8%) 3.2 Informal Institutions 15.06% 3.64% (4.6% to 27.1% ) 3.3 Public legal institutions 20.22% 4.88% (5.9% to 59.2%)

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72 Table 5-2. Firm A results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 23.1% 23.1% 1.1 Reputation 4.5% 1.0% 1.2 Specification 27.9% 6.4% 1.3 Inspection 17.3% 4.0% 1.4 Certification 11.7% 2.7% 1.5 Price Performance 38.6% 8.9% 2. Selling Company 24.6% 24.6% 2.1 Capability 10.6% 2.6% 2.2 Relationship with the buyer 21.8% 5.4% 2.2.A Relationship with individuals 82.5% 4.4% 2.2.B Relationship with companies 17.5% 0.9% 2.3Reliability 15.4% 3.8% 2.3.A Adequate communication 55.5% 2.1% 2.3.B Deliveries 29.7% 1.1% 2.3.C Financial situation 14.8% 0.6% 2.4 Reputation of selling company 18.4% 4.5% 2.5 Competence to solve problems 33.8% 8.3% 3. Market Environment 52.3% 52.3% 3.1 Private Control Institutions 75.5% 39.5% 3.2 Informal Institutions 18.5% 9.7% 3.3 Public legal institutions 5.9% 3.1%

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73 Table 5-3. Firm B results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 24.1% 24.1% 1.1 Reputation 12.8% 3.1% 1.2 Specification 9.6% 2.3% 1.3 Inspection 22.0% 5.3% 1.4 Certification 36.3% 8.7% 1.5 Price Performance 19.3% 4.6% 2. Selling Company 30.1% 30.1% 2.1 Capability 15.2% 4.6% 2.2 Relationship with the buyer 33.5% 10.1% 2.2.A Relationship with individuals 65.5% 6.6% 2.2.B Relationship with companies 34.5% 3.5% 2.3Reliability 19.3% 5.8% 2.3.A Adequate communication 39.3% 2.3% 2.3.B Deliveries 21.6% 1.3% 2.3.C Financial situation 39.1% 2.3% 2.4 Reputation of selling company 11.8% 3.6% 2.5 Competence to solve problems 20.2% 6.1% 3. Market Environment 45.8% 45.8% 3.1 Private Control Institutions 71.7% 32.9% 3.2 Informal Institutions 14.2% 6.5% 3.3 Public legal institutions 14.1% 6.4%

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74 Table 5-4. Firm C results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 77.8% 77.8% 1.1 Reputation 2.2% 1.7% 1.2 Specification 8.3% 6.4% 1.3 Inspection 12.4% 9.7% 1.4 Certification 30.4% 23.6% 1.5 Price Performance 46.8% 36.4 2. Selling Company 18.0% 18.0% 2.1 Capability 25.3% 4.6% 2.2 Relationship with the buyer 5.4% 1.0% 2.2.A Relationship with individuals 15.6% 0.2% 2.2.B Relationship with companies 84.4% .08% 2.3Reliability 18.5% 3.3% 2.3.A Adequate communication 45.4% 1.5% 2.3.B Deliveries 45.1% 1.5% 2.3.C Financial situation 9.5% 0.3% 2.4 Reputation of selling company 2.1% 0.4% 2.5 Competence to solve problems 48.7% 8.8% 3. Market Environment 4.2% 4.2% 3.1 Private Control Institutions 76.3% 3.2% 3.2 Informal Institutions 11.9% 0.5% 3.3 Public legal institutions 11.8% 0.5%

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75 Table 5-5. Firm D results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 75.4% 75.4% 1.1 Reputation 9.1% 6.8% 1.2 Specification 4.5% 3.4% 1.3 Inspection 7.4% 5.6% 1.4 Certification 19.3% 14.5% 1.5 Price Performance 59.8% 45.1% 2. Selling Company 6.0% 6.0% 2.1 Capability 4.8% 0.3% 2.2 Relationship with the buyer 44.9% 2.7% 2.2.A Relationship with individuals 89.6% 2.4% 2.2.B Relationship with companies 10.4% 0.3% 2.3Reliability 20.5% 1.2% 2.3.A Adequate communication 20.0% 0.2% 2.3.B Deliveries 75.1% 0.9% 2.3.C Financial situation 4.9% 0.1% 2.4 Reputation of selling company 4.4% 0.3% 2.5 Competence to solve problems 25.6% 1.5% 3. Market Environment 18.7% 18.7% 3.1 Private Control Institutions 76.9% 14.4% 3.2 Informal Institutions 4.6% 0.9% 3.3 Public legal institutions 18.5% 3.5%

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76 Table 5-6. Firm E results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 67.9% 67.9% 1.1 Reputation 7.0% 4.8% 1.2 Specification 40.0% 27.1% 1.3 Inspection 13.4% 9.1% 1.4 Certification 14.8% 10.1% 1.5 Price Performance 24.8% 16.8% 2. Selling Company 23.7% 23.7% 2.1 Capability 56.7% 13.4% 2.2 Relationship with the buyer 21.3% 5.1% 2.2.A Relationship with individuals 88.5% 4.5% 2.2.B Relationship with companies 11.4% 0.6% 2.3Reliability 13.7% 3.2% 2.3.A Adequate communication 19.5% 0.6% 2.3.B Deliveries 75.7% 2.5% 2.3.C Financial situation 4.8% 0.2% 2.4 Reputation of selling company 2.7% 0.7% 2.5 Competence to solve problems 5.6% 1.3% 3. Market Environment 8.4% 8.4% 3.1 Private Control Institutions 81.8% 6.9% 3.2 Informal Institutions 9.1% 0.8% 3.3 Public legal institutions 9.1% 0.8%

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77 Table 5-7. Firm F results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 14.0% 14.0% 1.1 Reputation 46.2% 6.5% 1.2 Specification 15.5% 2.2% 1.3 Inspection 10.0% 1.4% 1.4 Certification 13.9% 1.9% 1.5 Price Performance 14.9% 2.0% 2. Selling Company 57.7% 57.7% 2.1 Capability 8.6% 5.0% 2.2 Relationship with the buyer 39.3% 22.6% 2.2.A Relationship with individuals 83.9% 19.0% 2.2.B Relationship with companies 16.1% 3.7% 2.3Reliability 26.0% 15.0% 2.3.A Adequate communication 63.3% 9.5% 2.3.B Deliveries 19.5% 2.9% 2.3.C Financial situation 17.2% 2.6% 2.4 Reputation of selling company 6.8% 3.9% 2.5 Competence to solve problems 19.3% 11.1% 3. Market Environment 28.4% 28.4% 3.1 Private Control Institutions 69.0% 19.6% 3.2 Informal Institutions 22.8% 6.5% 3.3 Public legal institutions 8.2% 2.3%

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78 Table 5-8. Firm G results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 55.3% 55.3% 1.1 Reputation 6.2% 3.4% 1.2 Specification 2.8% 1.5% 1.3 Inspection 12.6% 7.0% 1.4 Certification 54.1% 29.9% 1.5 Price Performance 24.3% 13.4% 2. Selling Company 28.5% 28.5% 2.1 Capability 8.0% 2.3% 2.2 Relationship with the buyer 22.9% 6.5% 2.2.A Relationship with individuals 84.1% 5.5% 2.2.B Relationship with companies 15.9% 1.0% 2.3Reliability 22.6% 6.4% 2.3.A Adequate communication 23.3% 1.5% 2.3.B Deliveries 69.9% 4.5% 2.3.C Financial situation 6.8% 0.4% 2.4 Reputation of selling company 7.9% 2.3% 2.5 Competence to solve problems 38.5% 11.0% 3. Market Environment 16.2% 16.2% 3.1 Private Control Institutions 79.5% 12.9% 3.2 Informal Institutions 5.0% 0.8% 3.3 Public legal institutions 15.5% 2.5%

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79 Table 5-9. Firm H results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 45.9% 45.9% 1.1 Reputation 27.6% 12.7% 1.2 Specification 35.2% 16.2% 1.3 Inspection 3.3% 1.5% 1.4 Certification 21.7% 10.0% 1.5 Price Performance 12.1% 5.5% 2. Selling Company 45.9% 45.9% 2.1 Capability 23.2% 10.6% 2.2 Relationship with the buyer 10.4% 4.8% 2.2.A Relationship with individuals 85.3% 4.1% 2.2.B Relationship with companies 14.7% 0.7% 2.3Reliability 39.0% 17.9% 2.3.A Adequate communication 45.3% 8.1% 2.3.B Deliveries 42.9% 7.7% 2.3.C Financial situation 11.8% 2.1% 2.4 Reputation of selling company 19.9% 9.2% 2.5 Competence to solve problems 7.5% 3.4% 3. Market Environment 8.2% 8.2% 3.1 Private Control Institutions 62.2% 5.1% 3.2 Informal Institutions 10.8% 0.9% 3.3 Public legal institutions 27.0% 2.2%

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80 Table 5-10. Firm I results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 41.5% 41.5% 1.1 Reputation 10.7% 4.4% 1.2 Specification 15.3% 6.3% 1.3 Inspection 8.2% 3.4% 1.4 Certification 18.8% 7.8% 1.5 Price Performance 47.0% 19.5% 2. Selling Company 12.5% 12.5% 2.1 Capability 22.6% 2.8% 2.2 Relationship with the buyer 23.7% 3.0% 2.2.A Relationship with individuals 83.6% 2.5% 2.2.B Relationship with companies 16.4% 0.5% 2.3Reliability 22.6% 2.8% 2.3.A Adequate communication 13.2% 0.4% 2.3.B Deliveries 62.3% 1.8% 2.3.C Financial situation 24.5% 0.7% 2.4 Reputation of selling company 22.9% 2.9% 2.5 Competence to solve problems 8.2% 1.0% 3. Market Environment 46.0% 46.0% 3.1 Private Control Institutions 40.0% 18.4% 3.2 Informal Institutions 27.1% 12.5% 3.3 Public legal institutions 32.9% 15.2%

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81 Table 5-11. Firm J results Relative Priorities Overall Priorities Criteria Level 1 Level 2 Level 3 1. Product 52.9% 52.9% 1.1 Reputation 55.7% 29.5% 1.2 Specification 3.0% 1.6% 1.3 Inspection 11.3% 6.0% 1.4 Certification 8.4% 4.5% 1.5 Price Performance 21.7% 11.5% 2. Selling Company 33.7% 33.7% 2.1 Capability 13.0% 4.4% 2.2 Relationship with the buyer 8.4% 2.8% 2.2.A Relationship with individuals 88.2% 2.5% 2.2.B Relationship with companies 11.8% 0.3% 2.3Reliability 35.4% 12.0% 2.3.A Adequate communication 24.4% 2.9% 2.3.B Deliveries 63.8% 7.6% 2.3.C Financial situation 11.8% 1.4% 2.4 Reputation of selling company 3.5% 1.2% 2.5 Competence to solve problems 39.7% 13.4% 3. Market Environment 13.3% 13.3% 3.1 Private Control Institutions 14.2% 1.9% 3.2 Informal Institutions 26.6% 3.5% 3.3 Public legal institutions 59.2% 7.9%

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82 CHAPTER 6 SUMMARY, CONCLUSIONS, AND IMPLICATIONS Summary As B2B transactions are shifting from tradit ional, one-on-one business relationships, to electronically based tran sactions, agricultural exporters face many challenges that must be overcome in order to make a smooth transition. Trust has been identified as a key element within B2B transactions and plays an even more important role within th e realm of e-commerce. Just as business to consumer (B2C) transactions have transitioned into th e world of e-commerce, the focus of this research is to discover the ke y elements of trust that play an important role within the world of international agricultural B2B relationships. Due to the strength of the US to EU agricultu ral trade region, fresh grapefruit exports from the US to the EU are the focus of this thesis. The EU is the trade destination of interest as the US is one of their key agricultural suppliers. Fresh grapefruit has been the crop of choice for this research as it is the number one fresh fruit produ ct being exported from the US to the EU. As this research focuses on fresh grap efruit going to the EU, the larger go al of this research topic is to begin the identification process of key trust elements playing a ro le within all major US to EU supply chains. It is the aim that if the key el ements of trust can be successfully identified between the US EU trade stream, a safe, affordable and maintainable food supply can be sustained throughout the EU and US as B2B tr ansactions trend towa rds the world of ecommerce. In order to collect such data, the AHP was the research tool of choice due to its well documented ability aid in the decision making of complex, multi-criteri a situations (Forman, Gass 2001). Due to the complexity and qualitati ve nature of trust, the AHP provides the

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83 necessary theoretical framework to best identif y the key elements of trust within the US-EU fresh grapefruit supply chain. The AHP pairwise comparisons obtained through one-on-one in terviews provide quantifiable data points of per ceived trust priorities to be cumulatively analyzed among the census population of fresh grapefruit exporters. Through this process the key elements of trust within the US-EU export supply chain of fresh grapefruit have been identified. Conclusions From the overall priority rankings gathered from the completion of the AHP, it is concluded that the product trust objective is per ceived as the top trust indicator to European buyers looking to purchase fresh grapefruit from a new supplier in the US. Of the characteristics that contribute to what defines the product being sold, the price performance ratio was the most important factor to EU buyers that is perceived from US exporters. This indicates that when US exporters are looking for European buyers, th ey are most likely to focus on the priceperformance ratio of their product. This inform ation can be taken one step further in the implementation of international ebusiness websites. It would be expected exporters would want to provide information that they have a srong price-performance ratio. The collected data also provides strong indi cators towards the importance of developing strong personal relationships with individuals not necessarily companies. This provides a direct correlation to an individua ls propensity to trust, which has been identified as the first form of trust found within B2B transac tions. This form of trust deal s directly with an individuals general trustworthiness towards others. This serves as another indication that firms are composed of individuals who make important B2 B transaction decisions based directly on their propensity to trust.

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84 Within the realm of e-commerce, this conslu sion also serves as a spring board for the possible implementation of one-on-one video communication technology within an exporting companys website. This is a simple example of how personal relationships, or the sense of a personal relationship, can be conveyed via the in ternet. Through inovation, both the needs of European buyers and the efficiencies gained through the uses of B2B e-commerce can be succesfully merged. The final conclusion which deserves further explanation is the important role private control institutions play within the US to EU fresh grapefruit supply chain. Table 5-1 shows the averaged relative priority to be 64.71% a nd the overall priority equaling 15.63%. These percentages serve as a clear indi cator that although the market environment as a whole might not be the most important contributor of trust to EU buyers, private control institutions are very important. This data coincides with what fresh grapefruit expor ters casually verbalized during the one-on-one interviews. The importance of the private control institutions is primarily due to consumer preferences of European buyers as they are very specific regarding acceptable levels of hazardous substances on fresh fruit. This is also something that could very easily be transferred to the forum of e-commerce. Just as in the B2C, e-business websites provide seals of privacy control and identity theft prevention, the agri cultural export sector co uld provide electronic indicators to buyers that their products meet certain private cont rol institutions requirements. Implications The implications of this research shou ld serve as a starting point for further research within the realm of e-commerce and the world food supply. From an economic perspective it is the goal of agricultural economist to aid in overall effective allo cation of resources in order to maintain the worlds food supply. This research provides the foundation for transitioning from traditional B2B alliances to a modern, e-commerce form, of international business transactions. By laying

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85 the foundation to identifying what trust elements are currently c ontributing to the success of international food supply chains, it is the hope that efficiencies can be gained by conducting more international B2B affairs via the internet. Beyond the interests of application within the realm of e-commerce, the data collected and presented in this research can provide valuable information to all players of the US-EU fresh grapefruit supply chain. Producers now are focu sing on (and can expect their competitor to focus on) the price-performance ratiowhile meeting the requireme nts of such private control instittuions as EurepGap and ISO 9000. By di seminating the conslusions of this research, producers will be better able to meet the need s of exporters who demands are being drvien by European consumer tastes and preferences. Rega rdless of the transistion towards the B2B use of e-commerce, the results of this reasearch are valid and can aid in gaining greater demand driven effenciencies throughout the existing fresh grapefruit supply chain. Future Research Needs To assist in the transition towards the use of e-commerce within international B2B trade, more supply chains must be analyzed in order to gain a broader picture of the presented model. Each industry is unique which may result in a change of the priority rating s within the AHP. By conducting an analysis similar to the one presen ted in this research within numerous supply chains as well as directly interviewing buyers throughout the world, a greater understanding of the macro international supply chai n trust picture can be derived. Coupled with this is the need to interview fres h grapefruit buyers in Europe directly. This will provde the final dimension needed in order to obtain a complete picture of -trust within the US-EU fresh grapefruit supply chain. This will also provide the opportunity for the results from each side both the buyers and the sellers to be compared. If the conclusions are synchronized, it can be concluded that US exporte rs have the knowledge needed to be meet the

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86 needs of European buyers. However, if the resu lts vary furture work should focus on educating domestic exporters on what atributes European buye rs of fresh grapefruit are looking for in an exporter. Through the sharing of information, fresh grapefruit exporters in the US will have a direct understanding of what Eur opean buyers expect in purchase d product. This information can be passed throughout the supply chain so that the product, the selling company, and the degree of the market environment that is in the control of US exporters, can parallel the demands of European buyers.

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87 LIST OF REFERENCES Akarte, M. M., N.V. Surendra, B. Ravi, and N. Rangaraj. 2001. Web Based Casting Supplier Evaluation Using Analytical Hierarchy Process. The Journal of the Operational Research So ciety 52.5:511-522. Barney, Jay B., and Mark H. Hansen. 1994. Tru stworthiness as a Source of Competitive Advantage. Strategic Management Journal 15:175-190. Brenkert, George G. 1998. Trust, Morality and International Business. Business Ethics Quarterly 8(2):293-317. Borys, B. and Jemison, D.B. 1989. Hybrid Arra ngements as Strategic Alliances: Theoretical Issues in Organizational Combination. Academy of Management Review 14:234-49. Brooks, Nora. 2006. Measuring the Importance of Exports to U.S. Agriculture. Amber Waves 5. Washington DC: U.S. Department of Agri culture, Economic Research Service. Chen, C.C., Xiao-Ping Chen, and James R. Me indl. 1998. How can Cooperation be Fostered? The Cultural Effects of I ndividualism-Collectivism. Academic Management Review 23(2): 285-304. Donegan, H.A. F.J. Dodd, and T.B.M. McMaster. 1992. A New Approach to AHP DecisionMaking. The Statistician 41(3):295-302. Doney, Patricia M., Cannon J., and Mullen M. 1998. Understanding the Influences of National Culture on the Development of Trust. The Academy of Management Review 23(3):601620. Dore, R. 1983. Goodwill and the Spirit of Market Capitalism. British Journal of Sociology 34(4): 459-482. Dunning, John H. 1997. Micro and Macro Organizational Aspects of MNE and MNE Activity. International Business : An Emerging Vision. Columbia, SC: University of South Carolina Press. Dwyer, F.R., Paul H. Schurr, and Sejo Oh. 1987. Developing Buyer-Seller Relationships. Journal of Marketing 51(2): 11-27. Dyer, J. H. 1996. Does Governance Matter? Keiretsu Alliances and Asset Specificity as Sources of Japanese Competitive Advantage. Organizational Science 7:649-666. --and H. Singh. 1998. The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage. Academic Management Review 23(4): 660679.

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88 Edmondson, William. 2008. U.S. Agricultural Trade Boosts Overall Economy. Outlook: A Report from the Economic Research Service. FAU-124. Washington DC: U.S. Department of Agriculture, April. European Union Delegation of the European Co mmission to the United States. EuroStat New Release. 2008. Facts and Figures on the European Union and the United States. Stat/07/57. Washington DC. European Union, European Commission. 2006. Building Trust for Quality Assurance in Emerging E-Commerce markets for food chains. Proposal Numb er: 43056. Description of Work. Florida Department of Citrus. 2008. Licensed C itrus Dealers; Exporte rs. Lakeland, Florida, March. Ford, D. 1982. International Marketing and Purchasing of Industrial Goods: An Interaction Approach. Wiley, New York, NY. ---., Lars-Eric Gadde, Hakan Hakansson, and Ivan Snehota. 2003. Managing Business Relationships. 2nd. ed., Chichester: Wiley. Forman, Ernest H., and Mary Ann Selly. 2001. Decisions By Objectives: How to convince others that you are right River Edge, NJ: World Scientific Publishing. ---., Gass, and Saul I. 2001. The Analyt ic Hierarchy ProcessAn Exposition. OR Chronicle 49(4):469-86. Frazier, G.L. 1983. Interorganizational Exchan ge Behaviour in Marketing Channels: A Broadened Perspective. Journal of Marketing 47:68-71. Grabner-Krauter, Sonja., and Ewald A. Kaluscha 2003. Empirical resear ch in on-line trust: a review and critical assessment. Science Direct 58:783-812. Hazama, H. 1978. Characteristics of Japanese Style Management. Japanese Economic Studies 6:110-173. Heffernan, Troy. 2004. Trust formation in cross-cultural business-to-bu siness relationships. Qualitative Market Research: An International Journal 7(2): 114-125. Hill, C.W.L. 1995. National Institutional St ructures, Transaction Cost Economizing, and Competitive Advantage: The case of Japan. Organizational Science 6:119-131. Hofstede, G. 1980a. Cultures Consequences: International Differences in Work-Related Values. Beverly Hills, CA. ---. 1980b. Motivation, Leadership, and Organizatio n: Do American Theories Apply Abroad? Organizational Dynamics 9(1):42-63.

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89 ---. 1983. The Cultural Relativity of Orga nizational Practices and Theories. Journal of International Business Studies 14(2):75-89. Huff, Lenard, and Lane Kelley. 2003. Levels of Organizational Trust in Individualist versus Collectivist Societies: A Seven-Nation Study. Organization Science 14(1):81-90. Jap, S. 2001. The Strategic Role of the Salesforce in Developing Customer Satisfaction Across the Relationship Lifecycle. The Journal of Personal Se lling & Sales Management 21(2):95-108. Jones, G.R., and J.M. George. 1998. The Experi ence and Evolution of Trust: Implications for Cooperation and Teamwork. Academy of Management Review. 23(3): 531-546. Lane, C. 1998. Introduction: Theories and issues in the study of trust. Trust Within and Between Organizations. 1-30. Oxford University Press, Oxford, U.K. Larson, A. 1992. Network Dyads in Entrepreneur ial Settings: A Study of the Governance of Exchange Relationships. Administrative Science Quarterly 37:76-104. Lenartowicz, T. and K. Roth. 1999. A Framework for Culture Assessment. Journal of International Business Studies 30(4):781-798. Martin, James. 1973. Design of Man-Computer Dialogues. New Jersey: Prentice-Hall, Inc. Mayer, Roger C., James H. Davis, and F. David Schoorman. 1995. An Integrative Model of Organization Trust. The Academy of Management Review 20(3):709-734. McAllister, D.J. 1995. Affect and Cognition Based Trust as Foundations for Interpersonal Cooperation in Organizations. Academy of Management Journal 38.1:24-59. McFall, L. 1987. Integrity. Ethics 98:5-20. Miller, G.A. 1956. The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Information Processing. Psychological Review 63(2):91-97. Millman, T. and K. Wilson. 1994. From Key Account Selling to Key Account Management. 10th IMP Annual Conference Meeting the Challenges of New Frontiers. University of Groningen, Groningen. Mohanty, R P, and SG. Deshmukh. 1993. Use of Analytic Hierarchic Process for Evaluating Sources of Supply. International Journal of Phys ical Distribution & Logistics Management 23(3):22-29. Oosterkamp, Elsje., Gert-jan van Sprundel, and Gert Jan Hofstede. 2007. D5: Report on B2B trust elements and their typology; E-trust. U npublished research deliv erable to European Commission. Contract No. : FP6-CT-2006-043056.

PAGE 90

90 Palmer, A. and D. Bejou. 1994. Buyer-seller Rela tionships: A Conceptual Model and Empirical Investigation Journal of Marketing Management 10:495-512. Saaty, Thomas L. 1982. Decision Making for Leaders, The Analytical Hierarchy Process For Decision a Complex World ---. 1980. The Analytic Hierarchy Process. Pittsburgh: RW S Publications. Sako, M. 1991. The Role of Trust in Ja panese Buyer-Supplier Relationships. Ricerche Economich 45:449-474. Salin, Victoria. 2000. Information Tec hnology and Cattle-Beef Supply Chains. American Journal of Agricultural Economics 82(5):1105-1111. Saunders, Carol., Yu Andy Wu, Yuzhu Li, an d Shawn Weisfeld. 2004. Interorganizational Trust in B2B Relationships. AMC International Conference Proceeding Series 60:272279. Seibel, Winfried., Okkyung Kim Chung, Dorian Weipert, and Seok-Ho Park. 2006. Cereals and Cereal Products. Encyclopedia of Agriculture Shockley-Zalabak, P., Ellis, Kathleen, Winogr ad, and Gaynelle. 2000. Organizational Trust: What it Means, Why it Matters. Organizational Development Journal 18:35-48. Stevens, S. S. 1946. On the theory of scales of measurement. Science 103:677-680. Sullivan, J., and R. B. Peterson. 1982. Factors asso ciated with Trust in Japanese-American Joint Ventures. Management International Review 22:30-40. Swaminathan, Jayashankar M., and Sridhar R. Tayur. 2003. Models for Supply Chains in EBusiness. Management Science, 49(10):1387-1406. Triandis, H.C. 1989. The Self and Social Behavior in Differing Cultural Contexts. Psychological Review 96(3):506-520. ---. 1995. Individualism and Collectivism. Boulder, CO: Westview. U.S. Department of Agriculture. 2007. 2007 Citrus Summary. National Agricultural Statistic Service, Washington DC, September. U.S. Department of Agriculture. 2008a. C rop Production. National Agricultural Statistic Service, Washington DC, June. U.S. Department of Agriculture. 2008b. Foreign Ag ricultural Trade of the United States. Top 15 U.S. Export Destinations, by Fiscal Year, U.S. Values. Economic Research Service, Washington DC, Last accessed April, 2008. U.S. Department of Agriculture. 2008c. U.S. Ag ricultural Trade: Exports. Economic Research Service, Washington DC, April.

PAGE 91

91 U.S. Department of Agriculture. 2008d. U.S. Tr ade Exports HS 6-Digit. Foreign Agricultural Service. Washington DC, June Wilson, D.T. 1995. An Integrated Model of Buyer-Seller Relationships. Journal of the Academy of Marketing Science 23(4):335-345. Yamagishi, M., T. Yamagishi. 1989. Trust, Commitment, and the Development of Network Structures. Paper presente d at the Workshop for the Beyond Bureaucracy Research Project, Hong Kong. Yamagishi, T. 1988. The Provisions of a Sanctioni ng System in the United States and Japan. Social Psychology Quarterly 51:256-271. ---., and M. Yamagishi. 1994. T rust and Commitment in the Unites States and Japan. Motivation and Emotion 18(2):129-166. ---., Karen S. Cook, and Motoki Watabe.1998a. Uncertainty, Trust and Commitment Formation in the United States and Japan. American Journal of Sociology 104(1):164-194. ---., Nobuhito Jin, and Allan S. Miller. 1998b. I n-group Bias and Culture of Collectivism. Asian Journal of Social Psychology. 1:315-328. Zaheer, A., B.McEvily, V. Perrone. 1998. Does trust matter: Exploring the effects of interorganizational and interpersonal trust on performance. Organization Science. 9(2):141-159 Zand, D.E. 1972. Trust and Mana gerial Problem Solving. Administrative Science Quarterly 17:229-239.

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92 BIOGRAPHICAL SKETCH Ellnor McKenzie Dahl, also known as Ellie, is a sixth-generation Florida agriculturalist. She was born in Lake Wales, and was raised in Fort Mead e, Florida. After receiving her associates degree from Santa Fe Community College, she transferre d into the Food and Resource Economics Department at the Univer sity of Florida. During her time as an undergraduate at the University, she was actively involved with the Agricultural Economics Club and was member of the 2006 award-winning Na tional Agricultural Ma rketing Association student competition team. Ellie was also hired by BayerCrop Science to participate in their Temik Monitor internship program, where she relocated to Fort My ers for the spring semester of her Junior year. In Spring 2006 Ellie graduated with honors w ith her undergraduate degree and began her graduate work within the Food and Resour ce Economics Department in Fall 2006 on a USDA National Need Fellowship award. During her two y ears as a graduate student, she traveled to professional meetings in Germany, Austria, Oregon, and California These experiences allowed Ellie to gain the international perspective sh e needed to better understand the global economy coupled with the importance of academic research. Upon graduation Ellie strives to find a career that will use her inborn fondness for agriculture and unshakable determination for making a positive impact within Floridas agricultural sector.