How Much s E-Commerce Worth to Rural Busnesses? Susan Watson, Assstant Professor O. John Nwoha, Program Assocate Gary Kennedy, Department Head and Assocate Professor Kenneth Rea, Vce Presdent for Academc Affars Lousana Tech Unversty Unversty of Arkansas Lousana Tech Unversty Lousana Tech Unversty Selected Paper prepared for presentaton at the Southern Agrcultural Economcs Assocaton Annual Meetngs Lttle Rock, Arkansas, February 5-9, 2005 Correspondng Author: Susan Watson Department of Agrcultural Scences Lousana Tech Unversty P.O. Box 10198 Ruston, LA 71272 swatson@latech.edu Ths research was funded by the USDA Fund for Rural Amerca Grant awarded to Lousana Tech Unversty. Copyrght 2005 by Susan Watson, O. John Nwoha, Gary Kennedy, and Kenneth Rea. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that hs copyrght notce appears on all such copes. Abstract The probablty of a busness payng for an e-commerce presence ultmately depends on demographc features, experences wth e-commerce, technologcal expertse, and knowledge of e-commerce opportuntes and lmtatons. Results allow for the assgnment of probabltes assocated wth varous busness profles to determne the wllngness to pay for an e-commerce presence. Key Words: e-commerce, nternet, rural busnesses, wllngness to pay JEL Classfcatons: A14, C25, D21, O13, O14, O33, Q16
Increased Internet use has drastcally altered the way busness s conducted. Current Internet use s 186 mllon users n the U.S. and 945 mllon worldwde. In the year 2007, projectons for the U.S. and worldwde are 230 mllon and 1,466 mllon people, respectvely (etforecasts.com). The U.S. once had almost 90% of worldwde users n the md-1980 s, and has contnued to drop through tme wth the latest drop of approxmately 20% n 2004. There are many forces addng to the growth of the Internet n other countres as well as the Unted States ncludng: web cellular phones, pre-pad Internet access cards, broadband Internet connecton, wreless Internet access, e-commerce for moble devces (M-Commerce), Internet cafes n developng countres, declnng Internet servce provder rates, bundled servces, web applances and nteractve web TV, amongst others (etforecasts.com). E-commerce sales are approxmately 1.9% of total retal sales (U.S. Department of Commerce) wth the greatest revenue stemmng from computer hardware, furnture, software, books, musc, vdeos, offce supples, food/beverages, and arlnes tckets (Abate and Moser). Accordng to the Nelsen-Net Ratngs, who conduct analyss and measurement on Internet audences, the average Amercan spends 80 mnutes on-lne at work and 26 mnutes on-lne at home daly, both spendng approxmately one mnute per web page (Nelsen-NetRatngs.com). Ths s not much tme for a busness to convnce a consumer to make a purchase, much less, allow them to conduct the transacton. E-commerce requres a dfferent methodology than the tradtonal brck and mortar busness to make sales. E-commerce provdes an excellent opportunty for many smaller busnesses. Entry costs nto the marketplace are lowered allowng busnesses to compete on an nternatonal forum (Dutta and Evrard; Poone and Swatman; and Webb and Sayer). Many new electronc busnesses have developed because of ths potental (Motwall and Khan). Successful busnesses have 1
several features n common. They respond to webste features that most consumers prefer, such as stock avalablty, prvacy, customer servce, order trackng capabltes, and provdng detaled product nformaton (Post et al.). However, three of every four on-lne busnesses fal n the frst two years, ndcatng a need to develop and admnster a strategc busness model talored to an e-commerce platform (Paper, Pedersen, and Mulbery). In rural economes, ths falure rate s even hgher and t becomes necessary to determne what e-commerce products, educatonal nformaton, and servces are worth to these smaller companes. Recent studes have explored consumers wllngness to pay for Internet servces, but not the sellers wllngness to pay for e-commerce servces (Blefar-Melazz, D Sorte, and Real; Chellappa and Shvendu; Jang; Lee, Park, and Km; Sultan; Sur et al.). There s a lack of nformaton and understandng about what small busnesses, partcularly rural busnesses, would be wllng to pay for ths opportunty to overcome geographc handcaps and compete wth larger companes. An ongong USDA Fund for Rural Amerca, Rural Communty Innovaton project, called the Delta E-Commerce Connecton (DECC), s creatng dversfed economc opportuntes over a four-year perod for small agrcultural and other rural busnesses n the Lower Msssspp Delta by assstng n e-commerce busness development. The project offers semnars, featurng a set of three educatonal tranng modules that relate e-commerce to rural entrepreneurs n a practcal fashon. In addton, techncal support n webste development, developng an Internet marketng strategy, electronc retalng servces, and space on a secure server are provded to selected rural busnesses for a perod of one year. Busnesses retanng webstes after ths tme assume responsblty for mantanng and fundng ther ste. Durng the DECC semnars, the cost structure of e-commerce has always been a topc of great nterest for partcpants. Due to the lack of nformaton and understandng about busnesses wllngness to pay for e-commerce 2
products n rural areas, a survey was developed to gauge the value partcpants placed on the new opportunty. Specfc objectves were to determne what representatve busnesses from Lousana would pay for a semnar offerng nformaton about e-commerce and a bundle of goods ncludng techncal assstance for buldng an e-commerce webste and tutorng n the techncal sklls used to develop and mantan ther own webste. Elastctes and margnal effects assocated wth wllngness to pay are measured as well. Materals and Methods A survey was conducted of busnesses partcpatng n a DECC semnar as those busnesses have been exposed to educatonal materal concernng e-commerce. The survey nstrument examned whether or not partcpants would be wllng to pay for a bundle of packages, ncludng: a workshop to ntroduce e-commerce termnologes, advantages of e- commerce, how to market products, how to desgn a webste as well as learnng how to mantan a webste after t s bult. Sx choces were randomly assgned to partcpants wth few very low and very hgh offers to mnmze the potental for a thck tal n the dstrbuton beyond the hghest offer (Looms). The choces started at $1,000 and ncreased n $1,000 ncrements up to $6,000. The level of technology was assessed wth the followng optons for the partcpant: no access to the Internet or e-mal; access to Internet and e-mal, but no webste; busness has an nformatonal webste; busness has an nteractve webste; or busness has a transactonal webste. Partcpants were asked f they were aware of the potental of e-commerce before attendng a semnar and ther perceved level of dffculty for developng an e-commerce presence. Choces ncluded: mpossble, very dffcult, somewhat dffcult, farly easy, and very easy. Demographc nformaton such as gender, age, place of resdence, and occupaton were 3
gathered. Income, annual sales, and annual proft were also ncluded n the survey to determne f these nfluenced the wllngness of a busness to pay for specfc e-commerce servces. The survey was admnstered through the mal accordng to the Talored Desgn Methods (Dllman). Contact was frst made by mal and e-mal n the form of a pre-notce letter. The prenotce letter was sent a few days pror to the survey ndcatng the mportance of respondng. A cover letter and survey were then sent wth a stamped, self-addressed return envelope, followed one week later by a postcard thankng respondents and urgng non-respondents to fll out and return the survey. A replacement survey was sent to those not respondng to the survey several weeks later. Ths stmulated another surge of survey responses (Dllman). One-hundred and nnety two surveys were sent out, 126 were returned, a 65.6% response rate, whle 93 surveys were flled out completely and deemed usable for the study. Partcpants were allowed to return blank surveys f they dd not want to fll t out or be contacted any further. The dchotomous choce logt contngent valuaton method was used to evaluate wllngness to pay usng maxmum lkelhood estmaton. Looms suggests ths method to be most approprate n mal surveys. Alternatve methods, such as the open-ended approach, allow the partcpant to name ther own prce. Ths s not the way ndvduals operate n a market. Instead, the market prce s gven and the consumer makes a decson to purchase or not based upon the stated prce. Another alternatve, the teratve bddng process, results n socal desrablty bas where the consumer wll overestmate ther wllngness to pay to please the ntervewer. Ths method can also be nfluenced by the prce the ntervewer starts wth (startng pont effects), whch can nfluence the fnal wllngness to pay (Looms). However, the range of prces must be correct as the maxmum wllngness to pay s nferred wth the ad of the logt dchotomous choce model. 4
The logt model s based on the cumulatve logstc probablty functon whch allows all predctons to le on the 0-1 nterval. The model s specfed n equaton (1) as: 1 1 (1) E( Y ) = P = CPF( Z ) = CPF( α + βx ) = = Z ( + ) 1+ α βx e 1+ e Where Y s the lnear probablty model equal to α + βx + ε, P s the probablty of bd acceptance (Prob(Y = 1)), CPF s the cumulatve probablty functon, β s a vector of parameters, and X s a matrx of observatons. The log-lkelhood functon to be maxmzed s specfed n equaton (2) as: n (2) LogL Y Ln( P ) + (1 Y ) Ln(1 P ) = = 1 = 1 n The log-lkelhood functon s maxmzed by dfferentatng the Log L wth respect to α and β, settng them equal to zero and solvng the equatons. The equatons are consstent and effcent asymptotcally (Pndyck and Rubnfled). Elastcty measures also report meanngful nformaton concernng the changes n the probablty of success when an explanatory varable changes. However, when there are many observatons, the average s often used as a summary measure. There s no guarantee that the logt functon wll pass through the summary measure. Therefore, evaluatng every observaton wth predcted probabltes as weghts for the observatons can address ths lmtaton (Shazam on-lne). The elastctes were calculated n equaton (3) accordng to the followng specfcatons for the k th coeffcent: 5
(3) E k Pˆ = X k X Pˆ k Whle the weghted aggregate elastcty s specfed n equaton (4) as: n W (4) Ek = Pˆ Ek / Pˆ = 1 n = 1 Margnal utlty coeffcents allow for the change n an ndependent varable to reflect on the utlty ndex. The logt model margnal effects are specfed n equaton (5) as: (5) Pˆ / X k = ˆ ' β exp( X ˆ) β k ' 2 [ 1+ exp( X ˆ) β ] 2 The ch square test statstc s χ = -2[ln (L unrestrcted - L restrcted ) wth the degrees of freedom equal to the number of slope coeffcents. The null hypothess s that all slope coeffcents are equal to zero, where the sgn of the coeffcent ndcates the drecton of the effect of the varable on the probablty of bd acceptance (Shazam on-lne). Results The estmated equaton s shown n equaton (6) below: 6
PrYes ( 6) Log = -1.0467 -.00068Amount + 0.79803Experence + 0.21380Aware 1 PrYes (-0.40732) (-2.43270) (2.10300) (0.21866) - 0.29750Dffculty + 0.07868Gender + 0.20332Age + 0.05877Place -.09372Job (-0.55594) (0.12220) (0.65475) (0.19841) (-0.13118) 0.08640Income - 0.60582Sales + 0.11737Proft (0.34816) (-2.0774) (0.37873) The logt model coeffcent sgns estmated by maxmum lkelhood gve the drecton of the effect of the change n the explanatory varable on the probablty of bd acceptance. The varable Amount had a negatve sgn ndcatng that the larger the bd amount, the smaller the probablty of bd acceptance. Other negatve coeffcent sgns ncluded the varables: Dffculty, Job, and Sales. Dffculty was a varable that measured the perceved level of dffculty partcpants had for developng an e-commerce presence. Therefore, the more dffcult the busness perceved e-commerce to be, the less lkely the partcpant would accept the bd. A busness that perceves e-commerce as dffcult may fnd t hard to justfy nvestng as the probablty of success decreases the harder the task appears. The Job varable was dvded nto blue and whte-collar occupatons. Whte-collar workers were less lkely to accept the proposed bd. Ths mght be due to the tranng that most whte-collar busness owners ether currently have or the access they possess wth respect to e-commerce and Internet technology. Moreover, most whte collar workers are rsk averse and more reluctant to nvest n a technology wth an uncertan outcome. Therefore, they would not be wllng to pay as hgh of a prce as those wth less access. The Sales varable represented annual sales. Busnesses wth hgher sales had a lower probablty of bd acceptance. Ths could be due to the fact that e-commerce would lkely 7
generate more sales and the busnesses less wllng to pay havng already met that goal. They mght have also been skeptcal that e-commerce would sgnfcantly ncrease ther sales. The remanng varables coeffcent sgns were postve ncludng: Experence, Aware, Gender, Age, Place, Income, and Proft. The Experence varable measured on a contnuum the current level of technology for the busness ncludng the followng categores: No access to the Internet or e-mal (level 0); access to Internet and e-mal, but no webste (level 1); nformatonal webste (level 2); nteractve webste (level 3); and transactonal webste (level 4). The more experence the busness had wth technology, the hgher the probablty of bd acceptance. Ths s probably because busnesses wth the most experence wth technology have more nformaton on prces and values for varous technology-orented tems. Also, the more techncally savvy a busness s currently, the easer t s to contnue to keep up-to-date wth technologes. Therefore, the value to these busnesses s greater as they wll be able to not only set up an e-commerce presence, but mantan and mprove t through tme. A dchotomous queston concernng the busnesses awareness (Aware varable) of the potental of e-commerce ndcated that busnesses more aware of the potental of e-commerce were more lkely to accept the proposed bd. A busness must be able to realze the benefts to match t wth the assocated costs, whch ultmately determne the value to a busness. The Gender varable was also a 0-1 varable wth 1 representng males. The postve sgn ndcates that f the busness was headed by a male, the probablty of bd acceptance would ncrease. Males are usually more techncally-orented than females, on the average, lkely nfluencng ths sgn. The Age varable ndcates that the older the busness owner, the hgher the probablty of bd acceptance. An older busness owner lkely has more savngs and can nvest n e-commerce easer than busness owners stll gettng establshed. 8
Place was a varable to determne the populaton of the town n whch the busness was located. It was found that the larger the populaton the more lkely the bd would be accepted. Larger areas tend to have more resources avalable, and therefore ther populaton has access to and can learn more about new technologes faster than smaller, rural communtes, wth a lack of nfrastructure for the latest technologes. Larger populaton areas are usually early adopters relatve to the rest of the Unted States, due to market access n these areas. The Income varable reflected the annual ncome for the busness owner wth hgher ncome levels beng more lkely to accept the proposed bd. Ths may be reflectve of the lower rsk assocated wth havng more money on nvestng n any one partcular dea. Proft was a varable that measured the annual proft for the busness where the hgher proftng busnesses had a hgher probablty of bd acceptance. The most proftable busnesses have already ether fgured out ther nche or are outpacng ther compettors n some way. They contnue to see the value n ths through e- commerce. The overall sgnfcance of the model was assessed usng the Lkelhood Rato Test. The null hypothess that all slope coeffcents were zero was rejected at the 10% sgnfcance level. The Cragg-Uhler R-square reported was 0.309, whle the McFadden R-square was 0.233. Although, the actual coeffcents on the logt model mean very lttle to economsts, weghted aggregate elastctes can be determned to assess magntudes of change. These measures are reported n Table 1 for all contnuous varables. For example, on average, a one percent ncrease n ncome gves a 0.16 percent ncrease n the probablty of bd acceptance, holdng all else constant. Addtonal weghted aggregate elastctes can be nterpreted n the same way. Margnal effects are also noteworthy and are shown n Table 1. It was found that, on average, a $1,000 ncrease n ncome leads to a 0.005 ncrease n the probablty of bd 9
acceptance, holdng all else constant. Dummy varable can be nterpreted n the same way. For example, f the partcpant was aware of the potental of e-commerce, there was a 0.001437 ncrease n the probablty of bd acceptance. Probabltes can be determned for specfc busness profles by nsertng the varable values nto the logstc probablty functon. The level of technologcal progress was determned to be an mportant varable n determnng wllngness to pay for e-commerce. Holdng all varables at the mean and allowng the Experence varable to change wll demonstrate how these probabltes also change. For example, a whle-collar, male age 45-54 lvng n a town under 10,000 wth annual ncome of $40,001-60,000, annual sales of $10,000-$50,000, annual proft of $5,001-$10,000 that perceves that developng an e-commerce presence s somewhat dffcult would have dfferent probabltes assgned to varous bd amounts dependng on ther level of experence wth technology (Fgure 1). Table 2 demonstrates these fluctuatons. The level of annual sales was also determned to have a large nfluence on wllngness to pay for e-commerce. Holdng all varables constant except sales demonstrates the varablty of the probablty functon. For example, a whle-collar, male age 45-54 lvng n a town under 10,000 wth annual ncome of $40,001-60,000, annual proft of $5,001-$10,000 that perceves that developng an e-commerce presence s somewhat dffcult and has a technology level of 2 (nformatonal webste) would have dfferent probabltes assgned to varous bd amounts dependng on the annual sales of the busness (Fgure 2). Table 3 demonstrates these fluctuatons. 10
Summary and Conclusons The data for ths study are unque n that they represent rural Lousana s percepton of how much e-commerce assstance s worth to the small, rural busness segment of the economy. The analyss shows that certan busness profles are more (or less) wllng to pay for e- commerce. Specfcally, the probablty of a busness payng varous amounts of money for an e- commerce presence depends on demographc features, experences wth e-commerce, level of technologcal expertse, and the fnancal status of the busness. Busnesses that have tradtonally been less compettve n the marketplace, such as small and rural busnesses, have the opportunty to develop a domnant presence n the vrtual marketplace. However, ths comes wth an nvestment n both tme and money. By estmatng functons to assgn probabltes assocated wth the wllngness to pay for an e-commerce presence, one can forecast the lkelhood of certan busness profles payng varous monetary amounts for an e-commerce presence. These estmates wll lkely vary by regon. In Lousana, the wllngness to pay was shown to be largely nfluenced by the current level of technology and annual sales. Postve ndcators of ncreased wllngness to pay ncluded more experence, beng aware of the potental of e-commerce, male busness ownershp, older busness ownershp, larger populaton areas, and hgher annual ncome and proft levels. Negatve ndcators of decreased wllngness to pay for e-commerce ncluded the perceved level of dffculty wth creatng an onlne presence, whte-collar busness owners, and larger annual sales. The forecastng model can assst rural development facltes n determnng whch busnesses value e-commerce the most and can assst n allocatng lmted funds. Busnesses wth the hghest probabltes of payng for e-commerce can be dentfed as prme canddates for assstance servces, thereby maxmzng benefts to socety. 11
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Table 1. Weghted Aggregate Elastctes and Margnal Effect of Logt Model Varables Varable Weghted Aggregate Elastcty Margnal Effect Amount -1.12170-0.00005 Experence 1.57870 0.05367 Aware ----.001437 Dffculty -0.59964-0.02001 Gender ---- 0.00529 Age 0.53240 0.01367 Place 0.11371 0.00395 Job ---- -0.00630 Income 0.16303 0.00581 Sales -1.1868-0.04075 Proft 0.18734 0.00789 16
Table 2. Probablty of Bd Acceptance at Varous Levels of Technology Technology Bd Level $100 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 $7,000 Level 0 0.1380 0.0802 0.0425 0.0221 0.0114 0.0058 0.0030 0.0015 Level 1 0.2623 0.1622 0.0897 0.0478 0.0249 0.0128 0.0066 0.0034 Level 2 0.4412 0.3007 0.1796 0.1002 0.0537 0.0281 0.0145 0.0074 Level 3 0.6369 0.4885 0.3271 0.1984 0.1119 0.0602 0.0316 0.0163 Level 4 0.7957 0.6796 0.5192 0.3547 0.2186 0.1247 0.0676 0.0356 17
Table 3. Probablty of Bd Acceptance at Varous Levels of Sales Level of Sales Bd $100 $1,000 $2000 $3000 $4000 $5000 $6000 $7000 $0-$1,000 0.8294 0.7258 0.5740 0.4068 0.2588 0.1509 0.0829 0.0440 $1,001-0.7262 0.5909 0.4237 0.2723 0.1600 0.0884 0.0470 0.0245 5,000 $5,001-0.5914 0.4407 0.2863 0.1696 0.0941 0.0502 0.0262 0.0135 $10,000 $10,001-0.4412 0.3007 0.1796 0.1002 0.0537 0.0281 0.0145 0.0074 $50,000 $50,001-0.3011 0.1900 0.1067 0.0573 0.0300 0.0155 0.0080 0.0041 $100,000 $100,001-0.1903 0.1135 0.0612 0.0321 0.0166 0.0085 0.0044 0.0022 $500,000 $500,001-0.1137 0.0653 0.0343 0.0178 0.0091 0.0047 0.0024 0.0012 $1,000,000 $1,000,001-0.0654 0.0367 0.0190 0.0098 0.0050 0.0026 0.0013 0.0007 $5,000,000 $5,000,0001+ 0.0368 0.024 0.0105 0.0054 0.0027 0.0014 0.0007 0.0004 18
0.9000 0.8000 0.7000 0.6000 probablty 0.5000 0.4000 0.3000 0.2000 0.1000 0.0000 100 325 550 775 1000 1225 1450 1675 1900 2050 2275 2500 2725 2950 3100 3325 3550 3775 4000 4225 4450 4675 4900 5050 5275 5500 5725 5950 6100 6325 6550 6775 7000 bd ($) Level 0 Level 1 Level 2 Level 3 Level 4 Fgure 1. Probablty of Bd Acceptance by Technology Level 19
0.9000 0.8000 0.7000 0.6000 probablty 0.5000 0.4000 0.3000 0.2000 0.1000 0.0000 100 325 550 775 1000 1225 1450 1675 1900 2050 2275 2500 2725 2950 3100 3325 3550 bd ($) $0-1,000 $1,001-5,000 $5,001-$10,000 $10,001-50,000 $50,001-100,000 $100,001-500,000 $500,0001-$1,000,000 $100,000,001-$5,000,000 $5,000,001+ Fgure 2. Probablty of Bd Acceptance by Level of Annual Sales 3775 4000 4225 4450 4675 4900 5050 5275 5500 5725 5950 6100 6325 6550 6775 7000 20