Adoption of E-Marketing by Direct Market Farms in the Northeastern U.S. Alexander G. Baer and Cheryl Brown

Transcription

1 Adopton of E-Marketng by Drect Market Farms n the Northeastern U.S. Alexander G. Baer and Cheryl Brown Alexander G. Baer s graduate research assstant and Cheryl Brown s assstant professor n the Dvson of Resource Management at West Vrgna Unversty, P.O. Box 6108, Morgantown, WV , Cheryl.Brown@mal.wvu.edu Selected paper prepared for presentaton at the Amercan Agrcultural Economcs Assocaton Annual Meetng, Long Beach, CA, July Ths research was supported by a grant from the U.S. Department of Agrculture Northeast Regon SARE (Sustanable Agrculture Research and Educaton) program. Abstract: Many farms have begun operatng webstes n order to promote ther busnesses. Ths study used data from a survey of farms n the northeastern U.S. for the year 2004 n order to dentfy characterstcs of farmers, farms, and farm busnesses assocated wth webste adopton. Followng a technology adopton framework, a Probt model of webste adopton was estmated to dentfy sgnfcant relatonshps. Some sales locatons and product types, advertsng dversty, hgh speed Internet connectons, and gross farm sales were found to be sgnfcantly related to webste adopton. Key Words: drect marketng, e-marketng, probt, technology adopton, webste Copyrght 2006 by Alexander G. Baer and Cheryl Brown. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes. 1

2 Adopton of E-Marketng by Drect Market Farms n the Northeastern U.S. Computer technologes have drastcally changed socety and busness n recent years. In the 1980s and 90s, mcrocomputers emerged as a new technology that promsed to change every aspect of our lves. As computer technology has advanced, the Internet has become a domnant aspect of computer use by ndvduals and busnesses worldwde. Indvduals use the Internet for several dfferent reasons ncludng socal nteracton, obtanng nformaton, and purchasng goods and servces. Over ffty-four percent of U.S. households now have access to the Internet (Day, Janus, and Davs, 2005). Ths has ncreased from around ffty percent n 2001, forty-one percent n 2000, and twenty-sx percent n 1998 (Day, Janus, and Davs, 2005). Currently, seventy-eght percent of Internet users utlze the Internet to get nformaton on products or servces, and over ffty-four percent purchased products or servces onlne n 2003 (Day, Janus, and Davs, 2005). As the Internet s technologes have advanced, busnesses have adopted t as a sales and marketng medum. The Internet can be used by busnesses to reduce transacton costs assocated wth conductng busness, such as provdng nformaton about products and servces. In addton, companes can use the worldwde web to offer a wder varety of products to a larger audence at lower prces compared to products found n a physcal settng (Couclels, 2004). Specfc to the agrcultural sector, a majorty of farms have reported usng computer technologes, ncludng the Internet, for personal use and as a busness tool. In 2005, ffty-eght percent of farms n the U.S. had access to computers and ffty-one percent had Internet access, an ncrease of fve percent snce 2001 (NASS, 2005). In the northeastern U.S., 55 percent of farms have access to the Internet (NASS, 2005). Many farms use the Internet to obtan weather reports, producton nformaton, and marketng nformaton such as prces and trends. Nne percent of farms natonwde report usng the Internet for gatherng nformaton and marketng ther 2

3 products (NASS, 2005). E-marketng strateges ( the strategc process of creatng, dstrbutng, promotng, and prcng goods and servces to a target market over the Internet or through dgtal tools ) have become an establshed presence n the agrcultural sector (Hooker, Helg and Ernst, 2001, p.4). In the Northeast, eleven percent of farms report usng the Internet to conduct agrcultural marketng actvtes (NASS, 2005), however, very lttle s known about how farms are usng the Internet for drect marketng. Ball and Duval (2001) surveyed farms usng the Internet for drect marketng, focusng on the attrbutes of the farm busness and farmers whch led to Internet marketng beng judged a success. Ther study only examned those farms already usng some form of e-marketng and provdes no detals regardng the characterstcs of these early adopters relatve to those farms whch have yet to nclude e-marketng as part of ther marketng strategy. Understandng the characterstcs of these nnovators provdes mportant nformaton on who s most lkely to adopt ths technology as well as who stands to beneft the most from e-marketng. Ths study s based on a 2005 survey of drect market farms n the northeastern U.S. and uses the presence of a webste for the farm busness as a proxy for e-marketng adopton. Incorporaton of e-marketng nto the marketng mx of drect market farms ndcates that the benefts of a webste (better servng current customers, attractng new customers, ncreasng sales) are greater than the costs (knowledge of computer and Internet applcatons, tme and money spent desgnng and updatng webstes, ganng Internet access). The objectve of ths paper s to determne the farmer, farm, and farm busness characterstcs whch lead to webste use by drect marketng farms n the northeastern U.S. 3

4 Computer, Internet, and E-Commerce Adopton n Agrculture Overall technologcal changes n the last century have drastcally altered agrculture. A large volume of lterature exsts whch examnes farmer adopton of agrcultural nnovatons (Sundng and Zlberman, 2000). Rogers (1995) categorzes nnovatons as hardware and software, where hardware nnovatons provde dfferent levels or forms of materal objects and software nnovatons refer to the nformatonal aspects of the new technology. E-marketng represents a unque form of technology adopton for agrculture. On one hand, e-marketng resembles a software nnovaton by provdng farms wth a new means of delverng nformaton, communcatng wth customers about the farm busness and ts products and enhancng customer relatons. On the other hand, e-marketng could be consdered a hardware nnovaton whch provdes another advertsng and sales medum that expands the scope of the farm operaton. Computer technology adopton studes n agrculture have generally descrbed the demographc and structural characterstcs of adoptng farmers compared to non-adopters, followng a threshold model of adopton. Innovatons such as computer use, Internet use, and e- commerce use have been analyzed usng ths framework. Characterstcs such as farmer age, educaton, and off-farm employment, farm sze, and type of products sold have all been nvestgated n relaton to agrcultural adopton of these technologcal nnovatons over several dfferent populatons n North Amerca. Computer and Internet adopton studes have analyzed certan regons of the U.S. and Canada snce the md-1980s. Sabuhoro and Wunsch (2003) examned the use of computers and the Internet n Canadan farm busnesses wth 2001 Canadan Census of Agrculture data. They found that the type of farm operaton was most mportant n explanng the adopton of computer and Internet use by Canadan farms. Smth et al. (2004) examned the Great Plans regon of the 4

5 U.S. regardng use of computers, general use of the Internet, and use of the Internet for the farm busness, usng data collected n They dscovered that specfc educaton related to computers and employment off the farm had the greatest mpact on computer technology usage. Hoag, Ascough, and Fraser (1999) also examned Great Plans farmers n 1995 n terms of computer adopton. They found that most of the conventonal measures for computer adopton (farm sze, farmer experence, and farm type) were sgnfcant. Batte (2003) and Batte, Jones, and Schntkey (1990) looked at commercal Oho farms usng data from 2003 and 1987, respectvely. Batte (2003) dentfed off-farm full-tme employment and havng more than a hgh school educaton were most mportant n determnng computer adopton. Batte, Jones, and Schntkey (1990) explaned that farmers whch utlzed farm records n managng ther farms and who had hgher educaton levels were more lkely to use computers, whle farms that produced gran crops were less lkely, compared to farms producng lvestock, to use a computer. Gloy and Akrdge (2000) examned large farms n the U.S. for Ther results showed that hgher educaton was the most mportant ndcator n computer adopton, and younger and more educated farmers were more lkely to use the Internet. North Carolna commercal farms were the subject of a study by Amponsah (1995) who used 1991 data and explaned that farm sze and farmer educaton were the most mportant ndcators of computer adopton. Jarvs (1990) found that Texas rce producers had a multtude of characterstcs that affect the adopton of computers such as hgh ncome, farms growng cotton, stable farm sze, and exposure to others that used computers for ther busnesses. Lastly, Putler and Zlberman (1988) examned computer adopton n Tulare County, Calforna usng 1986 data that showed that bachelors and graduate degrees had the greatest mpact on computer adopton. 5

6 E-commerce adopton studes have looked at farmers n the Mdwest, Great Plans, and southern U.S., and most consdered mprovements n supply chan management and purchases of farm-busness nputs. McFarlane, Chembez, and Befecadu (2003) examned agrbusness frms n Alabama wth data collected n They found that the scope of the agrbusness was the largest ndcator of Internet and e-commerce adopton. Usng 2000 data, Ernst and Tucker (2001, 2002) examned frut and vegetable producers n Oho. Ther 2001 study showed that a measure of economc optmsm about IT (nformaton technologes) was hgher for farms that adopted IT. Ther 2002 study also found that atttudnal measures of optmsm n the role of IT were more mportant n determnng e-commerce actvty compared to ther measure of age. Agrbusness manufacturers, dealers, and dstrbutors were the subject of a study by Henderson, Dooley, and Akrdge (2000). They found that sze and scope of the busness were the most mportant determnants of the use of e-commerce strateges for those frms. Modelng Webste Adopton Followng tradtonal technology adopton lterature, ths paper utlzes the threshold model of adopton to dentfy the heterogeneous characterstcs leadng to webste use. The threshold model of adopton focuses on the decson of the ndvdual farmer to adopt a technology, assumng maxmzng or satsfcng behavor (Sundng and Zlberman, 2000). Ths model assumes ndvduals experence dfferent levels of benefts and costs from adopton of a partcular technology (Derden et al., 2004; Sundng and Zlberman, 2000) dependng on the characterstcs of the farmer or the farm busness. Once the net benefts reach some crtcal level or threshold, the farmer wll adopt the technology. The net benefts, B, for farmer are a functon of the farm and farmer characterstcs, X, such that B β1 β2 = + X, where the β 's are parameters of the model. The probablty that the farmer wll adopt a webste ncreases as B 6

7 ncreases, but adopton only occurs after the threshold level of net benefts, * B, s reached, when B B. The threshold level of net benefts s not observable only the fnal choce of whether or * not to adopt. Thus, the adopton decson, Y, can only take two values, and Y = 1 f a webste s adopted, and Y = 0 f a webste s not adopted. Assumng the net benefts are normally dstrbuted, the probablty that * B s less than or equal to B can be gven as: (1) P = P Y = X = P B B = P Z + X = F + X ) * ( 1 ) ( ) ( β1 β2 ) ( β1 β2, where PY ( = 1 X) s the probablty that a webste s adopted gven the values of the explanatory varables, X, Z s the standard normal varable such that Z N σ 2 (0, ), and F s the standard normal cumulatve dstrbuton functon (CDF) (Gujarat, 2003). The CDF n ths case wll be: (2) 1 B z 2 /2 F( B ) = e dz 2π or 1 β1+ β2 X 2 z /2 F( B ) = e dz 2π. Based on an assumpton of a standard normal dstrbuton of the net benefts from webste adopton, ths study uses a bnomal probt model to emprcally examne relatonshps between webste (e-marketng) adopton and farmer, farm, and farm busness characterstcs. The margnal effect on webste adopton of any explanatory varable s found by takng the dervatve of the probt equaton wth respect to that specfc ndependent varable, dp (3) = f( β1+ β2x) β 2, dx where f ( β ) 1+ β X 2 s the standard normal probablty densty functon (Gujarat, 2003). Ths margnal effect wll depend on the value at whch t s calculated, whch usually nvolves settng all explanatory varables equal to ther means, as s done n ths study. Fortunately, what would be a tedous calculaton to determne the margnal mpacts of the explanatory varables, as ndcated n equaton (3), s performed by the LIMDEP software used to estmate the probt 7

8 model n ths study (Greene, 2002). In the case of categorcal (dummy) explanatory varables, the margnal effect s calculated by fndng the dfference between the probablty of adoptng a webste when that category s present and when t s not (Amemya, 1981). Data for Emprcal Analyss Ths study used a survey of drect marketng farms n 12 states n the Northeast 1 to collect data on ther adopton of e-marketng. In early 2005, 5,392 requests for partcpaton were sent to drect marketng farms whose names and addresses were gathered from localharvest.com, state departments of agrculture, and specalty/trade assocaton webstes. Of the total requests maled, 987 farmers agreed to partcpate n the survey, ether through the mal or onlne; 404 surveys were maled and 583 ndvduals were e-maled lnks and access codes for the Internet survey. Of these, 300 mal surveys (74%) were returned, and 346 Internet surveys (59%) were completed. A total of 517 complete observatons were used n the econometrc estmaton of whch 229 farms had webstes, representng 44.3% of the sample. Table 1 presents descrptve statstcs for the varables used n the model. Based on the computer technologes adopton lterature as age ncreases adopton s expected to declne. Survey respondents (both wth and wthout a webste) had a mean and medan age of about 53 years, thus, ths s a slghtly younger group compared to the natonal average farmer age of The 2002 Census of Agrculture also found the average age of farm operators n the Northeast to be slghtly younger than the natonal average n 9 of the 12 states (Allen and Harrs, 2005). Educaton was measured wth an ordnal rankng from one to seven for reported educaton levels whch ranged from less than 9 th grade to a graduate or professonal degree. The average educaton of the sample farms was between some college attendance and a 1 The 12 Northeast states are as defned by the Northeast SARE regon (Connectcut, Delaware, Mane, Maryland, Massachusetts, New Hampshre, New Jersey, New York, Pennsylvana, Rhode Island, Vermont, and West Vrgna). 8

9 bachelor s degree, whch s more educaton than the natonal average of a hgh school educaton for farmers (Mshra, 2006). Educaton s expected to have a postve nfluence on the adopton of a webste by farmers. In general, the technology adopton lterature found evdence of a postve relatonshp between farm sze and adopton. For ths analyss, farm sze s represented by gross farm sales, followng the work of Smth et al. (2004), Sabuhoro and Wunsch (2003), Batte (2003), and Gloy and Akrdge (2000). Accordng to Rogers (1995), adopters of nnovatons have a larger scale of unts, regardless of the actual form of measurement (acres, gross farm sales, or gross farm ncome). Usng Ch-square tests, there was no sgnfcant dfference between adopton categores when acreage was consdered; there was a sgnfcant dfference when usng gross farm sales for Therefore, gross farm sales level s used as a measure of farm sze n the emprcal estmaton. Eleven gross farm sales levels were condensed nto three categores based on the results of the Ch-square tests: (1) Low Sales of less than $5,000, (2) Mdrange Sales between $5,000 and $100,000, and (3) Hgh Sales above $100,000. Land ownershp was found to play a postve role n technology adopton n a study of conservaton practce adopton by U.S. corn producers (Soule, Tegene, and Webe, 2000). Three categores of farmland ownershp are used n ths analyss: ownng all of the land used for farmng, rentng all of the land used for farmng, and other land arrangements. To account for the effect of product type on webste adopton, three broad categores of products are ncluded n the analyss: produce, ornamentals, and meat, dary, and eggs. In addton, several specfc products that were reported n a mscellaneous category are ncorporated n the model: wool or har products, honey, cder, baked goods, wne, and maple syrup. Ch-square tests showed that roadsde stands, farmers markets, and home delvery were 9

10 used more often by farms wthout webstes, and you-pck operatons, on-farm stands or stores, and mal order sales were more lkely to be used by farms wth webstes. Explanatory varables for sales locaton or method ncluded n the econometrc estmaton are roadsde stand or talgate, farmers market, pck-your-own, Communty Supported Agrculture (CSA), on-farm stand or store, flea market, home delvery, and sales to restaurants, grocery stores, and wholesalers or brokers. Ch-square tests found a sgnfcant dfference for farms wth webstes whch reported hgher levels of advertsng expenses and use of more types of advertsng methods compared to farms wthout webstes. An advertsng dversty varable was created whch measures the number of advertsng methods that farms reported usng for ther busness n Its values ranged from a mnmum of 1 to a maxmum of 14 methods. Maleck (2001) stated that broadband (hgh speed) Internet access s an mportant tool n usng the Internet for busness actvtes. A dummy varable equal to one f the farm had a hgh speed Internet connecton and zero f Internet access was by dalup s therefore ncluded n the webste adopton model. Fber optc, wreless, and satellte Internet connectons were combned wth DSL and cable connectons nto one hgh speed Internet access varable (34% of farms). The emprcal model used for econometrc estmaton s specfed as: (4) P = β + β Age + β Educaton + β Gross Sales + β Land Ownershp β Products + β Sales Locatons + β Advertsng Dversty β Hgh Speed Internet Connecton 27 where P, the probablty of webste adopton, s equal to one f the farmer used a webste for the farm busness n 2004, and the other varables and varable categores are as defned above. 10

11 Webste Adopton Model Results Measures of ft for the probt model of webste adopton are presented n Table 2 and generally show that the model performs well. The pseudo R 2 statstc of s consstent wth what the technology adopton lterature has generally reported. In addton, ths model correctly predcted 80.3 percent of the observatons. Table 2 reports the results of the probt model of webste adopton wth sgnfcant coeffcents (p-value of 0.10) n bold. Whle t was expected that age would have a negatve effect on webste adopton, there was no evdence of a sgnfcant mpact of farmer age. Ths s consstent wth the early lterature on computer adopton and more recent studes of Internet and e-commerce adopton (Amponsah, 1995; Jarvs, 1990; Putler and Zlberman, 1988) whch found no relatonshp wth age. The nsgnfcance of age on webste adopton could also be the result of farmers hrng professonals to construct and mantan ther webstes. Nearly seventy-four percent of farms that reported operatng a webste n 2004 had ther webste developed by someone other than themselves, wth 31% hrng a professonal web developer. Farmer educaton level was also not found to have a sgnfcant effect on adopton of a webste, although the coeffcent dd show the expected sgn. Jarvs (1990) also found that educaton was not a sgnfcant determnant of computer adopton. Contrary to these fndngs, Batte (2003), Gloy and Akrdge (2000), Amponsah (1995), Batte, Jones, and Schntkey (1990), and Putler and Zlberman (1988) all found that educaton played a postve role n computer adopton. The nsgnfcance of educaton n ths study could ndcate that educaton does not play a role n determnng webste adopton because farmers do not desgn ther own webstes, as was noted n the dscusson regardng farmer age. In addton, t may be that specfc sklls related to web development and mantenance are not related to overall educaton level. Ths 11

12 result could also be attrbuted to the dfferent role that webstes play for a farm compared to general farm computer use. Computer adopton studes examned the role of computers n record-keepng and nformaton processng, whle webstes have played a role n advertsng the farm busness or marketng ts products, whch may requre less computer use by the farmer. The low gross farm sales category was not statstcally sgnfcant for webste adopton when compared to farms wth mdrange sales, the base category. On the other hand, the hgh sales category was postve and sgnfcant. Margnal effects presented n Table 3 show that farms wth 2004 sales greater than $100,000 were 18.9% more lkely to adopt a webste than farms wth mdrange sales. Ths renforces the belef that larger farms see more of a beneft from havng a webste than smaller farms. It could also ndcate that the expense of developng and mantanng a webste s a smaller proporton of the budgets of larger farms compared to smaller farms. Ths result s consstent wth the relatonshp that the computer technology adopton lterature has found between sze of an operaton measured by gross farm sales and adopton of that technology (Gloy and Akrdge, 2000; Hoag, Ascough, and Fraser, 1999; Putler and Zlberman, 1988). Henderson, Dooley, and Akrdge (2000) also establshed a postve relatonshp between e-commerce actvtes of agrbusnesses and gross sales. Nether of the land tenure categores (rentng or other) has a sgnfcant mpact on webste adopton, when compared to the base category of ownng all of the land beng farmed. Sundng and Zlberman (2000) provde a possble explanaton for ths result. They stated that some technology characterstcs may lead to dfferences n property arrangements, whle other technologes may have no relatonshp to land ownershp. Webstes make up a smaller nvestment compared to producton equpment, and could be altered relatvely easly to account for changes n the locaton of a farm dependng on new rental contracts. The structure of 12

13 webstes and level of expense of development and mantenance may allow the farmer to consder abandonng webstes wthout representng a large loss to the farm. It would be expected that farmers sellng products wth dfferent characterstcs would make dfferent choces on usng a webste for ther busness. Products that are branded and/or processed were expected to have a postve nfluence on the adopton of a webste snce these products may possess characterstcs that are easly dfferentated by vsual nspecton, such as a pcture on a webste. Product choces were not lmted to a sngle selecton; so many operatons reported sellng products from several categores. The model found a negatve relatonshp between farms growng produce and webste adopton, wth the probablty of adoptng a webste 14.8% lower for farms sellng produce. The negatve relatonshp of produce producton wth webste adopton could be due to an nablty of these farms to dfferentate ther products over an extended perod of tme due to the seasonal nature of produce producton and the pershablty of these products. Ths negatve relatonshp could also ndcate that farmers are spendng so much tme growng and sellng ther products that they are too busy to desgn and update a webste. Only one dfferentated product, cder, was slghtly sgnfcant, showng a postve relatonshp wth webste adopton. Cder sales may have a postve mpact on webste adopton due to the characterstcs of cder, as t s a branded, processed product. Farms that sold cder have a 22.3% hgher probablty of webste adopton. The nsgnfcance of meat, dary, or eggs regardng webste adopton was smlar to results found n the lterature, even though those studes dd not consder drect market sales of these products. In ther studes of computer adopton by farms, Gloy and Akrdge (2000) and Putler and Zlberman (1988) found cattle and dary operatons had an nsgnfcant mpact on adopton. Gloy and Akrdge (2000) also dscovered a smlar result n ther analyss of Internet 13

14 use. The nsgnfcant mpact of anmal product sales on webste adopton could be due to farmer perceptons regardng the role that webstes can play f they are only consderng webstes as a tool for onlne sales. The ornamentals coeffcent has the expected postve relatonshp wth webste adopton, although the varable was nsgnfcant. The seasonal nature of sellng ornamental goods, whch ncluded beddng plants and nursery products, Chrstmas trees, and cut flowers, may have lmted the use of a webste snce some of these busnesses may be operated for only a porton of the year and mght not want the addtonal expense of a webste. Other farms sellng ornamental products may stay open all year leadng to ther use of webstes n 2004, however ths may be only a small proporton of farms. The Natonal Chrstmas Tree Assocaton (2005) reported that the top 5% of farms sold 61% of the trees produced n the year Ths ndcates that a few farms each sell a very large amount of Chrstmas trees, whle many other farms sell a small amount of trees, whch provdes evdence to the dfferental role that Chrstmas tree sales, and perhaps other ornamental products, can play n a farm busness. In addton, wool or har products, wne, and maple syrup were not sgnfcant predctors of webste adopton. The lack of sgnfcance of specalty goods could be due to the small proporton of observatons representng these products n the sample, rangng from 2.1% for wne to 13.9% for honey. Another explanaton for the nsgnfcance of specalty goods could be that small farms wthout webstes see the benefts of advertsng or sellng these goods over the Internet, but are already sellng all of the products they make; consequently, they mght not beleve they could handle the addtonal demand for ther products that a webste mght create. Sellng at a roadsde stand or talgate and sellng at an on-farm stand or store both had a negatve and sgnfcant relatonshp wth webste adopton. When a roadsde stand or talgate s used by a farm busness, there s a 21.4% lower probablty of webste adopton. Ths could be 14

15 due to the nformal locaton of roadsde stands, whch are generally stuated at locatons that are conducve to spontaneous purchases by consumers who are drvng by. Contrary to pror expectatons, sellng va an on-farm stand or store, results n an 11.8% lower probablty of webste adopton. Ths could be true f the majorty of farmers operatng on-farm stands are small operatons whch lack specfc hours of operaton and also cater to mostly drve-by consumers. A dstncton n the data could not be made between on-farm stands and stores, whch as bgger, more professonal operatons were expected to have a webste. None of the other sales locatons/methods (farmers market, pck-your-own, CSA, flea market, home delvery, and sales to restaurants, grocery stores, and wholesalers or brokers) had a sgnfcant relatonshp wth webste adopton. The lack of sgnfcance of these other sales locaton varables could be attrbuted to the number of observatons used n the analyss snce many of the sales locatons had low levels reported (rangng from 1.7% sellng at flea markets to 32% at farmers markets). The advertsng dversty varable has a postve and sgnfcant relatonshp wth webste adopton. The results ndcate that use of an addtonal advertsng method leads to an ncrease of 16.7% n the probablty of adoptng a webste; at the mean value of advertsng dversty (approxmately 5 forms of advertsng). Ths ndcates that farmers use webstes as an addtonal type of advertsng whch augments ther other advertsng formats, and those farmers who regularly advertsed n 2004 also recognze the value of an addtonal advertsng method, whereas farmers wthout webstes lkely depend manly on word of mouth for advertsng ther farm busnesses. As expected the relatonshp between hgh speed Internet access and webste adopton was postve and sgnfcant. Hgh speed Internet access ncreases the probablty of adoptng a 15

16 webste by 14.7%. Ths s supported by Maleck (2001) who stated that broadband access s becomng an essental aspect of Internet access n relaton to onlne busness actvtes. Lmtatons and Future Research Ths research was lmted to the Northeast regon of the Unted States for the year The farms used n ths study were drect marketng farms, whch may exhbt dfferent characterstcs from farm operatons that do not drectly market ther products to consumers. Whle the locaton of the farms was avalable by zp code, tests for spatal dependence or heterogenety were not conducted, so spatal varablty was assumed to have no mpact n the model. Omtted varables could be a problem, due to the mpossblty of ncludng all relevant characterstcs when usng lmted data gathered from a survey. A larger number of observatons mght make t possble to show more relatonshps between some types of specalty goods that are sold by farms, ether confrmng that only a small proporton of farmers sell these goods or that there are real dfferences between farms that own webstes and those that do not n terms of sellng specalty goods. Several avenues could be followed to expand ths research. Identfyng the mpact of havng a webste on the structure of a drect marketng farm, such as gross farm sales and percentage of household ncome from the farm busness, s planned for future research. Addtonal research could dentfy the characterstcs of farms that sell products onlne, compared to farms that use webstes only as a form of advertsng, to determne f there are mportant dfferences between these two groups. Spatal analyss methods could be used to dentfy any locatonal patterns of dffuson of webste use. Lastly, studyng the demographcs of consumers usng farm webstes would provde essental nformaton to farms desgnng and usng a webste as part of ther marketng plan. 16

17 Summary and Conclusons Followng the advances n computer technologes n the last few decades and ncreased use of drect sales to mprove farm ncome, there has been a need to research the characterstcs of drect marketng farms n the Northeast that use the Internet for marketng. Ths study attempted to classfy farmers accordng to the characterstcs of ther farms and products, as well as ther nvestment n human captal. The study determned whch characterstcs sgnfcantly nfluence webste adopton by drect marketng farms n the Northeast for the year 2004, usng a probt model for econometrc estmaton. Margnal mpacts of the sgnfcant varables were also examned. The analyzed farmer and farm characterstcs ncluded varables prevously dentfed n computer, Internet, and e-commerce adopton studes and varables specfcally related to drect marketng farms. The sgnfcance of hgh gross farm sales shows that the sze of a farm busness has an effect on webste adopton, ndcatng that the scale of the costs of a webste may cause larger farms to use webstes more often than smaller farms. Processed and branded goods appear to have a slghtly postve nfluence on webste adopton, compared to a negatve mpact for produce. The negatve mpact of sgnfcant sales locaton varables (roadsde stand or talgate and on-farm stand or store) shows that farmers who sold at less organzed and tme constraned locatons were less lkely to adopt a webste. The results also ndcate that farmers who advertse n more ways are more lkely to adopt webstes. The data does not suggest that educaton, land ownershp status, or age are mportant n determnng webste adopton. 17

18 References Allen, R., and G. Harrs What We Know about the Demographcs of U.S. Farm Operators. Agrcultural Outlook Forum USDA, Natonal Agrcultural Statstcal Servce. Avalable at Amemya, T Qualtatve Response Models: A Survey. Journal of Economc Lterature 19: Amponsah, W.A Computer Adopton and Use of Informaton Servces by North Carolna Commercal Farmers. Journal of Agrcultural and Appled Economcs 27(2): Ball, T., and Y. Duval Internet Drect Marketng Success of Farm Busnesses n the U.S.: A Web-based Survey. USDA, Agrcultural Marketng Servce. Avalable at Batte, M.T Computers on Oho Farms: How Used and How Useful. The Oho State Unversty, Dvson of Agrcultural, Envronmental, and Development Economcs, Report Seres, AEDE-RP Batte, M.T., E. Jones, and G.D. Schntkey Computer Use by Oho Commercal Farmers. Amercan Journal of Agrcultural Economcs 72(4): Couclels, H Pzza over the Internet: E-commerce, the Fragmentaton of Actvty and the Tyranny of the Regon. Entrepreneurshp & Regonal Development 1(16): Day, J.C., A. Janus, and J. Davs Computer and Internet Use n the Unted States: U.S. Census Bureau and U.S. Department of Commerce, Economcs and Statstcs Admnstraton. 18

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22 Table 1. Descrptve Statstcs of Varables Varable Mean Standard Devaton Mnmum Maxmum Webste Age Educaton Low Sales Mdrange Sales Hgh Sales Own All Land Farmed Rent All Land Farmed Other Land Arrangement Produce Ornamentals Meat, Dary, or Eggs Wool or Har Products Honey Cder Baked Goods Wne Maple Syrup & Products Roadsde Stand or Talgate Farmers' Market Pck-your-own CSA Farm Stand or Store Flea Market Home Delvery Restaurants Grocery Stores Wholesaler or Broker Advertsng Dversty Hgh Speed Internet Connecton

23 Table 2. Webste Adopton Probt Results Varable Coeffcent Asymptotc t-statstc p-value Mean Constant Age Educaton Low Sales Hgh Sales Rent All Land Farmed Other Land Arrangement Produce Ornamentals Meat, Dary, or Eggs Wool or Har Products Honey Cder Baked Goods Wne Maple Syrup & Products Roadsde Stand or Talgate Farmers' Market Pck-your-own CSA Farm Stand or Store Flea Market Home Delvery Restaurants Grocery Stores Wholesaler or Broker Advertsng Dversty Hgh Speed Internet Connecton Measures of ft Log lkelhood rato p-value Pseudo R Percentage of correct predctons 80.3 Bold ndcates sgnfcance for varable at p-value 10%. Table 3. Margnal Effects of Sgnfcant Varables Varable Margnal Effect p-value Hgh Sales Produce Cder Roadsde Stand or Talgate Farm Stand or Store Advertsng Dversty Hgh Speed Internet Connecton