Modeling Farmers Use of Market Advisory Services. by Joost M.E. Pennings, Scott H. Irwin, and Darrel L. Good

Transcription

1 Modeling Farmer Ue of Market Adviory Service by Joot M.E. Penning, Scott H. Irwin, and Darrel L. Good Suggeted citation format: Penning, J. M.E., S. H. Irwin, and D. L. Good Modeling Farmer Ue of Market Adviory Service. Proceeding of the NCR-134 Conference on Applied Commodity Price Analyi, Forecating, and Market Rik Management. St. Loui, MO. [

2 Modeling Farmer Ue of Market Adviory Service Joot M.E. Penning, Scott H. Irwin and Darrel L. Good * Paper preented at the NCR-134 Conference on Applied Commodity Price Analyi, Forecating, and Market Rik Management St. Loui, Miouri, April 23-24, 2001 Copyright 2001 by Joot M.E. Penning, Darrel L. Good and Scott H. Irwin. All right reerved. Reader may make verbatim copie of thi document for non-commercial purpoe by any mean, provided that thi copyright notice appear on all uch copie. * Joot M.E. Penning i AST Ditinguihed Profeor in Future Market in the Department of Social Science at Wageningen Univerity, The Netherland. Darrel L. Good and Scott H. Irwin are Profeor in the Department of Agricultural and Conumer Economic at the Univerity of Illinoi at Urbana-Champaign. The co-operation and aitance of the Data Tranmiion Network in the reearch i gratefully acknowledged. Funding for thi reearch wa provided by the Rik Management Agency, US Department of Agriculture and Illinoi Council for Food and Agricultural Reearch.

3 Modeling Farmer Ue of Market Adviory Service Practioner Abtract In an effort to improve marketing of their product, many farmer ue market adviory ervice (MAS). To date, there i only fragmented anecdotal information about how farmer actually ue the recommendation of market adviory ervice in their marketing plan, and how they chooe among thee ervice. Baed on the literature on conulting ervice uage, a conceptual framework i developed in which perceived performance of the MAS regarding realized crop price and rik reduction, and the match between the MAS and the farmer marketing philoophy drive MAS uage. To account for poible heterogeneity among farmer regarding to the ue of MAS, we introduce a mixture-modeling framework that i able to identify unoberved heterogeneity. With thi modeling framework we are able to imultaneouly invetigate the relationhip between market adviory uage and the key component of our conceptual model for each unobervable egment in the population. A large cale interview of US farmer that contained everal experiment revealed that farmer ue of MAS not only depend on the outcome of their ervice (price and rik reduction performance) but alo on the way thee ervice are delivered, i.e., the match of marketing philoophy between farmer and MAS. The influence of the factor in our conceptual model did not influenced farmer MAS uage equally acro the whole ample. Uing the generalized mixture model framework we found 5 egment that differed regarding the influence that thee factor have on farmer MAS uage. The heterogeneity of the farmer appeared to be unoberved, in that it could not be traced back to obervable variable uch a age and region. It i the deciion-making proce itelf, a reflected in our conceptual model, that caued the heterogeneity. Key word : Market adviory ervice, urvey, farmer, election, mixture model, unoberved heterogeneity

4 Modeling Farmer Ue of Market Adviory Service Farmer in the US continue to identify price and income rik a one of their greatet management challenge. Uing a urvey of midwetern grain farmer, Patrick and Ullerich (1996) report that price variability i the highet rated ource of rik by crop farmer. Coble et al. (1999) urvey farmer in Indiana, Miiippi, Nebraka and Texa and find that crop price variability, by a wide margin, i rated a having the mot potential to affect farm income. Norvell and Lattz (1999) urvey a random ample of Illinoi farmer and how that price and income rik management rank econd (following computer education and training) among ten buine categorie in which farmer identify need for additional conulting ervice. Farmer have a variety of price and income rik management tool at their dipoal. Thee include numerou public and private ource of market information; future and option contract; an increaing number of yield and revenue inurance intrument and a new generation of cah indexing contract. While farmer value and ue thee tool, they place an even higher value on market adviory ervice (MAS) a a ource of price rik management information and advice. For example, in a rating of eventeen rik management information ource, Patrick and Ullerich (1996) report MAS are outranked only by farm record. Schroeder et al. (1998) find that a ample of Kana farmer rank MAS a the number one ource of information for developing price expectation. Norvell and Lattz (1999) find that marketing conultant tie for firt (with accountant), in a lit of even, a likely to be mot important to Illinoi farmer in the future. Thee urvey reult mirror the finding by Chava and Pope and Taylor and Chava that external information play an important role in farmer deciion-making. The pricing performance of MAS in corn, oybean and wheat ha been examined in a erie of report from the Agricultural Market Adviory Service (AgMAS) Project (e.g., Martine-Filho, Good, and Irwin 2000; Martine-Filho, Good, and Irwin 2001). A key aumption in thee evaluation i that a repreentative farmer follow the recommendation exactly a provided by the adviory ervice. In reality, there i only fragmented anecdotal information about how farmer actually ue the marketing recommendation provided by adviory ervice. More generally, there i literally no evidence about how they chooe among thee ervice. It i important to better undertand the way farmer ue and elect among adviory ervice in order to improve performance evaluation. Analyi in thi regard will alo provide valuable evidence on the way external information affect farmer deciion-making. Hence, the purpoe of thi tudy i to determine the nature of farmer ue of adviory ervice recommendation and what factor drive farmer election of uch ervice. The firt tep in the analyi i the development of a conceptual framework in which perceived performance of a MAS regarding realized crop price and rik reduction and the match between the adviory ervice and farmer marketing philoophy drive adviory ervice ue. In thi model, the ue of a MAS i driven not only by the main effect of thee three attribute, but alo by the interaction among them. The econd tep in the analyi i to tet the conceptual model and gain empirical evidence regarding actual MAS uage. The data for thi tep wa collected in a large-cale urvey of farmer acro the US in January/February The urvey meaure farmer tated

5 and revealed behavior, along with ome key contruct, hypotheized to be related to MAS uage. The urvey included an experiment in which farmer were expoed to different cenario, decribed in term of three attribute: the MAS pricing performance, the MAS rik reduction performance and the match between farmer and MAS marketing philoophy. Farmer had to indicate on a nine-point emantic cale the extent to which they would ue the MAS in a particular cenario. The experiment allow u to invetigate the influence of the MAS performance (in our experiment reflected by the dimenion of realized crop price and rik reduction) and the match between farmer and MAS market philoophy. Traditionally, the influence of uch attribute would be invetigated by a multiple regreion in which farmer repone to a nine-point cale i the dependent variable and the three attribute are the independent variable (e.g., Géczy, Minton, and Schrand). When the repone variable i binary, Probit and Tobit model typically are ued (e.g. Goodwin and Schroeder). In addition, when uch model are etimated, the data are treated a if they were collected from a ingle population, which i equivalent to auming farmer are homogeneou regarding their repone. Thi aumption of homogeneity might be unrealitic. Penning and Leuthold, in their tudy on farmer future contract uage, howed that heterogeneity at the egment level maked important effect on the aggregate. To tet for heterogeneity Penning and Leuthold ue the traditional cluter analyi method, extenively ued in marketing reearch (Punj and Stewart). There are two drawback of clutering analyi that could trouble our inight into how farmer behave (e.g., farmer MAS uage). Firt, in cluter analyi, egment (cluter) are identified by forming group of farmer that are homogeneou along a et of obervable characteritic, uch a farm ize, age etc. Thi property of cluter analyi exclude heterogeneity caued by the fact that the deciion-making proce of farmer itelf (which can not be directly oberved, and in thi paper i referred to a unoberved) might differ acro egment. Second, the number of egment i a priori, arbitrarily, determined by the reearcher, not by the data. To addre thee two drawback, we ue a mixture regreionmodeling framework, firt introduced by Wedel and DeSarbo, that i able to identify unoberved heterogeneity and at the ame time infer from the data the number of egment. Thi modeling framework allow u to imultaneouly invetigate the relationhip between farmer MAS uage and the explanatory variable of our conceptual model for each unobervable egment in the population. That i, our modeling framework will identify egment of farmer that behave according to the ame regreion equation. So, within a egment, each farmer repone can be adequately reflected by the regreion equation, while thi regreion equation differ for each egment. The propoed mixture regreion modeling framework can be very helpful for agricultural economit when invetigating farmer behavior and when they expect farmer behavior will not be homogeneou, and the heterogeneity might be unoberved, e.g., can not be traced back to obervable variable uch a age and farm ize. The challenge in the third tep of the analyi i to interpret the knowledge we obtain from the unoberved egment in a managerial way and try to characterize thee egment. The latter i a difficult challenge ince the heterogeneity i not baed on obervable variable. 2

6 Our empirical reult how that only eleven percent of farmer cloely follow the recommendation given by MAS. However, a large number of farmer ue adviory recommendation a background information and compare it with other ource of information. Pricing performance (average price and rik) i not the only important factor driving a farmer deciion to ue a particularly MAS. In addition, the extent to which the perceived marketing philoophy of the adviory ervice matche a farmer marketing philoophy i an important factor. Both criteria, adviory ervice pricing performance and the match between marketing philoophie, differ in importance acro egment of farmer. Furthermore, the influence of the interaction between thee component on MAS uage differ acro egment. The remainder of thi paper i tructured a follow. Firt, we introduce a conceptual framework in which the characteritic that might be aociated with MAS uage are dicued and hypothee are formulated. Then we decribe the experimental deign to illutrate our framework. After dicuing the operationalization of the unoberved contruct rik attitude and rik perception, the urvey deign and data gathering procedure are dicued. Next, empirical reult baed on data gathered from 1,399 farmer acro the US are reported. We conclude with an evaluation of the tudy and make uggetion for further reearch. Conceptual Model An important motivation for farmer to adopt MAS i their expectation that uch ervice will directly or indirectly improve the financial performance of their operation. Direct evidence on the relationhip between MAS uage and improved farm financial performance i not available. However, tudie that invetigate the relationhip between the financial performance of mall buinee and the uage of management adviory ervice have found a poitive relationhip (Kent). In thee tudie, management adviory tudie are companie that advie client in any apect of buine management whether the client i engaged in commerce, indutry or government. Prior reearch ha hown the importance of ditinguihing between the reult of the advice (e.g., performance of the management adviory ervice) and the atifaction with the conultant performance in arriving at thee reult (Ginzberg). Zeithaml, Parauraman, and Berry argued that cutomer do not evaluate ervice quality olely on the outcome of a ervice, but alo on the proce of ervice delivery. Therefore, we propoe to make a ditinction between the performance of agricultural MAS and the delivery proce. We aume that the pricing performance of MAS in the context of crop farming ha two dimenion: realized price and realized rik reduction. 1 For a given rik reduction, it i hypotheized that ervice that have hown trong performance regarding the realized crop price have a higher chance to be choen by a farmer than ervice that have hown weak crop price performance. Likewie, for a given realized price, it i hypotheized that ervice that have hown trong rik reduction regarding the realized crop price have a higher chance to be choen by a farmer than ervice that have hown weak rik reduction performance. The proce of delivering the marketing recommendation can be decribed in term of the adviory ervice marketing philoophy. More pecifically, marketing philoophy refer to 3

7 the tool that a ervice recommend to farmer for marketing their crop and to the complexity of the recommended marketing trategie involving thee tool. For example, a ervice which recommend initiating future and option poition and at time recommend elling more of a certain crop in the future market than the farmer actually poee ha what may be conidered an aggreive marketing philoophy. A ervice that tick to elling a crop proportionally in the cah market ha a more conervative marketing philoophy. Farmer too have marketing philoophie that can be decribed in term of the tool they ue to market their crop and the complexity of their marketing trategie. For example, Sartwelle et al. ditinguihed cah marketoriented marketing practice, forward contract-oriented marketing practice and future/optionoriented marketing practice. We hypotheize that there i a poitive relationhip between the extent to which the marketing philoophy of a particular MAS and the farmer match and the farmer deciion to ue a particular adviory ervice. That i, a farmer will not only look at the adviory ervice pricing performance, but will alo take the nature of the recommendation into account. Furthermore, we hypotheize an interactive effect between the match of marketing philoophy and the adviory ervice performance regarding the realized crop price. That i, the effect of the adviory ervice pricing performance on farmer uage will be larger the more the marketing philoophy of the adviory ervice matche the marketing philoophy of the farmer. So, the effect of the adviory ervice performance regarding realized crop price on a farmer ue of a ervice i reinforced when there i a marketing philoophy match between the ervice and farmer. Similar we expect an interaction effect between the match of marketing philoophy and adviory ervice performance regarding rik reduction. Experimental Deign To tet the conceptual model, an experiment wa deigned in which farmer had to indicate the chance of uing a MAS for everal cenario. When deigning the experiment we had to acknowledge that deciion-maker find it difficult to evaluate cenario that contain many attribute. Attribute in thi context refer to trong or weak realized price performance, trong or weak realized rik performance and match of market philoophy. In marketing reearch, in particular conjoint analyi, it ha been hown that too many attribute introduce repone error (often referred to the a level effect ), becaue repondent are unable to proce all the information to which a cenario expoe them (Green and Srinivaan; Wittink, Krihnamurthi, and Nutter). In the cenario, farmer have to make a trade-off between different attribute. Pretet indicated that farmer indeed found it very difficult to evaluate cenario with three or more attribute. Therefore, we formulated cenario that conited of two attribute. We had the following pair of component: MAS matche your marketing philoophy (or not) and trong (weak) performance regarding the realized crop price, and MAS matche your marketing philoophy (or not) and trong (weak) performance regarding rik reduction. A total of eight cenario could be developed on the bai of thi 2*2 + 2*2 deign, a diplayed in Figure 1. Farmer indicated the extent that they will ue the MAS in the context of a particular cenario on a emantic differential nine-point rating cale (1= not ue at all, 9 = certainly ue) (e.g., Churchill 1995). Thi reearch deign allow u to meaure the influence of the main effect of adviory ervice pricing performance and match of marketing philoophy, a well a the interaction between them. Each cenario can be decompoed into it underlying component by mean of introducing dummy variable. Thee dummy variable indicate whether or not a 4

8 pecific characteritic of a MAS i preent in the cenario (imilar to conjoint analyi in which product are decribed in term of a bundle of product attribute). For example, the dummy variable for MAS realized crop price i zero when the ervice ha weak performance regarding realized crop price and one when the ervice ha trong performance regarding realized crop price. Thee dummy variable are the explanatory variable. The Appendix Table A1 provide the dummy variable cheme. Baed on farmer repone to our cenario and the dummy variable cheme, we are able to invetigate the influence of marketing philoophy match, MAS price performance, MAS rik performance and their interaction with the following regreion model: (1) y nk + β 7 = β 0 + β MPPRB 1 nk MP nk + ε nk + β 2 PP nk + β 3 PRG nk + β 4 PRB nk + β 5 MPPP nk + β 6 MPPRG nk where ynk MP nk i the marketing philoophy (match 0 = no match, 1 = match), nk i the rating on the nine-point emantic cale by the n th farmer of the k th cenario, PP i the adviory ervice price performance (0 = weak performance and 1 = trong performance), PRG nk i trong rik reduction performance (1 = ye, no = 0), PRB i weak rik reduction performance (ye = 1, no = 0), MPPP nk i the interaction between market philoophy and price performance (ye 1, no = 0), MPPRG nk i the interaction between market philoophy and good rik reduction performance (ye = 1, no = 0), MPPRBnk i the interaction between market philoophy and bad rik reduction performance (ye = 1 and no = 0) and ε nk i an iid normal error term. 2 Note that thi dummy cheme i orthogonal, imilar to a conjoint experiment in which the attribute have only two level. The intercept capture the ituation in which the adviory ervice ha a weak performance regarding crop price and doe not match the farmer marketing philoophy. The regreion coefficient indicate the change in the farmer repone on the nine-point emantic MAS uage cale when the variable change by one unit, which in our context mean when MAS change from not having a particular feature to having that feature, for example from having a weak price performance to a trong price performance. Finally, marketing reearcher have long known that repondent ue rating cale in different way (Greenleaf). Some tend to chooe extreme anwer, thu uing the entire cale, while other ue only a mall part of the cale. Thi mean that the core of a farmer on the nine-point cale can be thought of a coniting of the true core plu their repone bia. Hence, the reported core ha no abolute meaning. Correcting rating cale for the repone bia by tandardizing repondent core ha proven to be a powerful tool (Churchill 1995). Therefore, the regreion model ue the farmer tandardized core a the dependent variable. The abolute core for a given farmer are tandardized baed on that farmer average core and tandard deviation of core acro the eight cenario. A a reult, the intercept i interpreted a the number of tandard deviation above or below the average core of farmer for the cae of no market philoophy match and poor pricing performance. We expect the ign of the intercept in thi context to be negative. The remaining coefficient then indicate the change in farmer repone due to a particular variable, with the change meaured in number of tandard deviation. nk 5

9 Modeling Unoberved Heterogeneity Since we do not a priori aume farmer to be homogeneou regarding the uage of MAS and the attribute that drive their uage, we propoe a generalized mixture regreion model. In the mixture model it i aumed that our ample of farmer on which the meaurement i taken (farmer repone to the cenario (e.g., Figure 1), the o-called obervation), i compoed of a number of underlying egment of farmer. In order to decribe the proce generating farmer repone, a certain tatitical ditribution i aumed for them. Such a ditribution function decribe the probabilitie that the farmer repone (e.g. obervation) take certain value. Such a tatitical ditribution i characterized by it expectation (for example the expectation of the normal ditribution i equal to the mean of the obervation). Given the ditributional form, the purpoe of the mixture model i to decompoe the farmer population into the underlying egment. Baed on the work of Wedel and DeSarbo, we propoe a mixture regreion methodology that enable the etimation of the relation of the farmer repone (e.g. the obervation) in each underlying egment with the et of explanatory variable. That i our methodology etimate the relation between farmer MAS uage and our explanatory variable a defined in the conceptual model (e.g., Equation (1)) within each of the egment and at the ame time derive the egment. The mixture regreion framework provide the probability that each farmer belong to the derived egment, and the regreion coefficient in each repective egment which relate the expectation of the farmer repone to our explanatory variable. What i particularly powerful about thi method i the fact that the egmentation criterion i the regreion equation (e.g., Equation (1)). Hence, we are able to find egment, uch that member of that egment are behaving according to the beta coefficient a etimated for that particular egment. Formally, we can define our mixture regreion model a follow. Aume the vector of farmer repone, y n (e.g., the obervation), arie from a population that i a mixture of S egment in proportion π 1,...,π, where we do not know in advance the egment from which a particular vector of oberva tion arie. The probabilitie π are poitive and um to one. We aume that the ditribution of y n, given that y n come from egment, f ( y nk θ ), i one of the ditribution in the exponential family or the multivariate exponential family, where θ i the vector of regreion coefficient for each egment. Conditional on egment, the y n are independent. The ditribution f ( y nk θ ) i characterized by parameter. The mean of the ditribution in egment (or expectation) are denoted by µ k. θ k Since, we want to predict the mean of the obervation in each egment by uing our et of explanatory variable (MP, PP.. MPPRB ), we pecify a linear predictor η nk, which i produced by our explanatory variable denoted by X 1,..., X P ( X p = ( X nkp) ; p = 1,..., P ), and parameter vector β = ( βp ) in egment : 6

10 p (2) ηnk = Σ X nkpβp. p = 1 The linear predictor i thu the linear combination of our explanatory variable, and the et of beta that are to be etimated. The beta coefficient can be interpreted a the amount of change in the farmer extent to ue the MAS compared to the bae ituation a captured by the contant (e.g., β 0). A uch, the regreion coefficient do not have an abolute meaning: they hould be interpreted againt the bae ituation. The linear predictor i in turn related to the mean of the ditribution, function g(.) uch that in egment : µ k, through a link (3) η nk = g ( µ nk ). Thu, for each egment, a linear model i formulated with a pecification of the ditribution of the variable (within the exponential family), a linear predictor η nk and a function g(.) that link the linear predictor to the expectation of the ditribution. Since our dependent variable, a nine point cale in which farmer indicate their extent of MAS uage, i normally ditributed, the canonical link i the identity, i.e., η nk = µ k, o that, by combining Equation (2) and (3), the tandard linear regreion model within egment arie. The unconditional probability denity function of an obervation vector expreed in the finite mixture form: y nk, can now be (4) f ( yn φ ) = π f ( yn θ ), = 1 where the parameter vector φ = ( π, θ ), and θ = β. The parameter vector φ i etimated via maximum likelihood uing the EM algorithm. To accomplih thi, the likelihood function i maximized. The likelihood function decribe the probability that the data were generated, given the pecific et of model parameter (e.g., Equation (4)). By maximizing the likelihood, that et of parameter i obtained that mot likely ha given rie to the data at hand. The etimation algorithm i an iterative algorithm (Dempter, Laird, and Rubin) that equentially improve upon ome et of tarting value of the parameter, and permit imultaneou etimation of all model parameter (cf. Wedel and Kamakura). The idea behind the EM algorithm i that the likelihood function contain miing obervation, i.e. the 0/1 memberhip of ubject in the S egment. If thee were known, maximization of the likelihood would be traightforward. The EM algorithm i baed on a multinomial ditribution for the memberhip, the expectation of the likelihood can be formulated over the miing obervation. Thi involve calculating the poterior memberhip probabilitie according to Baye rule and the current parameter etimate of φ and ubtituting thoe into the likelihood. Once thi i accomplihed, the likelihood can be maximized. Given the 7

11 new etimate of φ, new poterior can be calculated in the next E (expectation)-tep, followed by a new M-(maximization) tep to find new φ. The E- and M- tep are thu alternated until convergence (ee Appendix A for the tatitical detail). Etimate of the poterior probability, p, that obervation of farmer n come from egment can be calculated for each obervation n vector y n, a hown in Equation (5): (5) p n = S π = 1 π f ( y f n ( y θ n θ ) ). We will ue Equation (5) to claify farmer in a particular egment. In order to determine the optimal number of egment, Akaike and Bozdogan developed information criteria tool. Thee criteria impoe a penalty on the likelihood that i related to the number of parameter etimated. Studie by Bozdogan indicate that the conitent Akaike information criterion, CAIC, i preferable in general for mixture model. Survey Deign and Data Gathering Procedure Firt, a urvey developed from in-peron interview with 15 farmer wa ent to a different ample of 100 farmer. Second, farmer who did not repond to thi mail urvey were contacted by phone to invetigate the reaon for not reponding. Third, baed on the information from thee non-repondent, the urvey intrument wa revied and ent to 3,990 US farmer. 3 The ample of addree wa drawn from directorie kept by a US firm that deliver agricultural market information and adviory ervice via atellite. The final ample of 3,990 farmer conited of 3,500 farmer that ubcribed ( ubcriber ) to at leat one of the ten mot popular market adviory ervice offered by the atellite network. The remaining 490 farmer erved a a control group, in that they did not ubcribe ( non-ubcriber ) to any of the adviory ervice offered by the network. Becaue we wih to tet that heterogeneity i truly unoberved and wih to characterize egment of farmer, we gathered data that might be aociated with the attribute in our conceptual model. For example, farmer who are in a egment that i characterized by farmer attaching a high value on the rik reduction performance of MAS, a reflected by a high regreion coefficient for adviory ervice rik reduction performance, might be characterized by farmer that are relative more rik avere and perceive more rik than farmer that are in a egment that attach a relatively low value on the rik reduction performance of MAS. We meaured rik attitude and rik perception in our urvey with a et of obervable variable (ocalled indicator). We adhered to the iterative procedure recommended by Churchill (1979) to obtain reliable and valid contruct. We ued imilar item a Penning and Leuthold and Penning and Smidt. Confirmatory factor analyi wa ued to ae the (pychometric) meaurement quality of our contruct (Hair et al.). For a detailed decription of a factor analytical model the reader i referred to Penning and Leuthold. Furthermore, egment may be different regarding other farmer characteritic. In the urvey we meaured a variety of 8

12 demographic variable, uch a age, farm ize, crop grown etc. Thee background variable can poibly be ued to profile the egment. The urvey quetionnaire conited of 31 quetion and could eaily be completed within 10 minute. The quetion were formulated uch that farmer could eaily check the anwer. The cover letter mentioned the importance of thi urvey for farmer, by communicating the benefit for farmer (i.e. gaining inight in the performance of MAS). It wa indicated that completing the urvey would take 10 minute and that it wa part of a univerity reearch program. In addition, it wa indicated that they would be eligible to win one of ten $200 cah prize, if they returned the quetionnaire. In the cover letter, the name and phone number of the reearcher were given, o that farmer with quetion about the mail urvey might contact them. Following Dillman Total Deign Method, farmer who had not reponded were contacted twice by mean of a potcard reminder and an extra copy of the quetionnaire (Dillman). The quetionnaire were ent on January 21, 2000 and the cut-off date for returning quetionnaire wa March 10, A total of 1,399 uable quetionnaire were ent back, reulting in a repone rate of 35%, which i high compared to previou urvey among mall- and medium-ized enterprie (Jobber; Karimabay, and Brunn). It turn out that non-ubcribing farmer among the ample repondent nearly all ue market adviory ervice, jut not the one offered by the atellite network. Conequently, the tatitical reult preented below are baed on the combined ample of ubcriber and non-ubcriber repondent. Sample Statitic and Farmer Opinion of Adviory Service Table 1 provide ome background information on the ample repone. Our ample of farmer can be claified a relatively large commercial farm. A large part of the farmer monitor the cah price of their crop everal time a day, howing their involvement in the crop pricing deciion. Farmer indicated that they generally ue the recommendation of MAS a background information. Only 11 percent of the farmer followed the pecific pricing recommendation of MAS cloely. Table 2 how that farmer are highly involved in pricing the crop. Mot corn, cotton, oybean, and wheat farmer price part of the crop 2 to 5 time during the marketing year. Compared to cotton and wheat farmer, corn farmer market their corp relatively often during the market year. The repone hown in Table 3 indicate that MAS are ued particularly for market information and market analye. Furthermore, Table 3 how that MAS are more often ued in an attempt to receive an above average price than to reduce price fluctuation. Furthermore, it appear that the recommendation of adviory ervice do make a moderate impact on the pricing deciion of farmer. Table 4 how that farmer highly value good quality information provided by MAS. Farmer do not eem to care whether the analyi i baed on the knowledge of one peron or a group, nor do they care about the way the information i preented (text veru chart). The MAS 9

13 method ued to arrive at the recommendation, technical veru fundamental analyi, i an important apect of an adviory ervice for farmer. Frequent update of analyi and conitency of recommendation i alo valued. In um, thee reult upport the hypothei that farmer do not evaluate ervice quality olely on the pricing outcome of the ervice, but alo on the proce of ervice delivery. Modeling Reult Figure 1 how the mean and tandard deviation of farmer repone to the eight cenario. To illutrate the uefulne of our generalized linear mixture modeling framework, we etimated Equation (1) acro the whole ample. Thi reulted in an extremely low R 2 of 0.009, indicating that ignoring unoberved heterogeneity reult in a model that i able to explain only about one percent of the variance of farmer repone to the cenario. However, a dramatic change in reult i found when we account for unoberved heterogeneity, uing our model a expreed in Equation (2) through (4). We etimated our model for everal egment and, a noted earlier, choe the optimal number of egment baed on the CAIC. The CAIC wa minimized for five egment, indicating our ample conited of five unoberved egment. The reult for the five-egment model are hown in Table 5. The R 2 of the 5-egment model, 0.789, i dramatically higher than the R 2 of the one-egment model. Thi indicate that our mixture model can explain 79 percent of the variance of farmer repone to the cenario. To ae the eparation of the egment, an entropy tatitic can be ued to invetigate the degree of eparation in the etimated poterior probabilitie a defined in Equation (6): N pnln p n= 1 = 1 (6) E = 1 Nln S S n The entropy value of indicate that the mixture component are well-eparated, i.e. the poterior (cf. Equation (5)) are cloe to 1 or 0. A hypotheized, Table 5 how that in all egment the bae cae, which i the ituation where the MAS ha a poor price performance and doe not match the farmer market philoophy, ha a trong negative influence on adviory ervice uage. However, thi influence i not equal for all egment. For example, the intercept for egment 2 i 0.889, indicating that farmer in thi egment rate the ituation of poor pricing performance and no match of philoophie about one tandard deviation below the average core for all cenario. By comparion, the intercept for egment 4 i 2.031, indicating that farmer in thi egment rate the bae cenario about two tandard deviation below the average core for all cenario. While the magnitude of the intercept doe vary, the fundamental aymmetry of repone doe hold acro egment. To demontrate thi point, it i helpful to add up the core for the mot beneficial cenario: market philoophy match, good pricing performance, and good rik reduction performance. Thi core i computed by umming the intercept coefficient and the coefficient for MP, PP, PRG, and their interaction MPPP and MPPRG in Table 5 for each egment. The core range from for egment 5 to for egment 1. The clear 10

14 implication i that farmer more heavily penalize the mimatch of market philoophie and weak pricing performance than they reward poitive performance in the ame dimenion. A can be een from Table 5, the influence on MAS uage of the different component differ acro the egment. That i, farmer in different egment attach different value to match of market philoophy, adviory ervice price performance, and rik performance. MAS price performance i an important driver of farmer adviory ervice uage in all egment. However, the influence of adviory ervice price performance on farmer uage i different for each egment. It i more than twice a large in egment 4 a in egment 2. A good price rik reduction performance i important for all egment, except for egment 4. In egment 4 good price rik performance doe not have a direct effect on adviory ervice uage, but an indirect effect by mean of the interaction between match of market philoophy and price rik performance (good a well a bad price rik performance). Market philoophy i indeed an important driver behind adviory ervice uage, a we propoed in our conceptual model. Thi confirm the argument of Ginzberg, and Zeithaml, Parauraman, and Berry that cutomer not only value the outcome of a ervice but alo the proce of ervice delivery. Only 10 percent of the farmer, a repreented by egment 4 and 5, do not take the marketing philoophy match into account. The influence of the match of market philoophy i introduced in thee two egment indirectly by the interaction with MAS price performance (egment 5) or through adviory ervice rik reduction performance (egment 4). All egment how that the influence of price performance i larger than the influence of the match of market philoophy. Thi i alo the cae when we compare the influence of price performance with MAS rik reduction performance. Here too, we find that the price performance i the mot important driver for MAS uage, excepting egment 5. Table 5 how that our hypothei regarding the interaction between marketing philoophy match and adviory ervice performance regarding realized crop price and rik production performance are not confirmed, a only in the relative mall egment 4 and 5 are thee interaction ignificant related to farmer ue of MAS. That i, the main effect of philoophy match and MAS performance (price and rik performance) drive farmer behavior. In our experiment, farmer did had to make a direct trade-off between MAS price performance and MAS rik performance. However, we can indirectly invetigate the weight that farmer attach to rik and return by comparing the beta coefficient for rik and price performance in our regreion reult for each egment. From Table 5, it become clear that a large part of the farmer attach a higher value to the MAS price performance than the rik reduction performance, although the difference are fairly mall for egment 1, 2 and 3. Only farmer in egment 5 put more value on the MAS rik reduction performance than price performance. A quetion that may arie from a managerial perpective i how can thee egment be characterized? Thi quetion i not eay to anwer ince the heterogeneity i not baed on obervable variable. We teted whether farmer in the different egment ignificantly differed on the quetion in the urvey uing ANOVA analyi and Chi-Square tet. Our analyi howed that the farmer in the five egment do not ignificantly differ regarding demographic characteritic, nor do they differ regarding their rik perception, but they do differ in their attitude toward rik (The hypothei that the mean of thee variable of the 5 egment i equal wa rejected at the 5% level uing an ANOVA analyi). The farmer from egment 1 and 11

15 5 are ignificantly more rik-avere than the farmer in egment 2, 3, and 4 (4.02 and 3.82 veru 3.38, 3.72, and 3.62 rik attitude core), a meaured by the indicator of our rik attitude cale (ee Appendix B). Thee finding correpond to the relatively high regreion coefficient for the influence of MAS rik reduction performance in egment 1 and 5, compared to the other three egment. Furthermore we found that farmer in the 5 egment differed regarding the value they attach to ome apect of MAS (ee Table 6). Farmer in egment 1,2 and 3 value conitent recommendation of MAS higher than farmer in egment 4 and 5. Thi i in accordance with the regreion coefficient diplayed in Table 5 for the match of market philoophy which are higher for egment 1,2 and 3 compared to egment 4 and 5. The ame pattern i found for the high quality information apect of MAS. Alo here the farmer in egment with relatively high regreion coefficient for match of marketing philoophy, egment 1, 2 and 3, value high quality information of MAS relatively higher. Farmer in the 5 egment alo ignificantly differ regarding how they value the fact that the recommendation of MAS include future and option. The farmer in the different egment alo howed difference in the MAS they ue(d). Table 7 diplay three well-known ervice. It appear that the egment ignificantly differ regarding their uage, ubtantiating the uefulne of our conceptual model in trying to undertand farmer MAS uage and choice. Concluion Farmer ue of market adviory ervice and conulting ervice in general not only depend on the outcome of thee ervice but alo on the way thee ervice are delivered. The great influence of the match of marketing philoophy in our empirical tudy how that farmer conider the whole ervice delivery proce a well a the final outcome. Had we treated the ample of farmer a homogenou, we would have concluded that our three attribute, marketing philoophy match, adviory ervice price performance, and adviory ervice rik performance, were unable to explain farmer ue of ervice. However, accounting for the heterogeneity of farmer and thereby recognizing the fact that thee attribute may have different influence on farmer adviory ervice uage for each egment revealed that farmer adviory ervice uage can indeed be explained by thee three attribute and their interaction. The heterogeneity of the farmer appeared to be unoberved, in that it could not be traced back to obervable variable uch a age and region. The egment of farmer could not be profiled (characterized) along obervable variable. It i the deciion-making proce itelf that caued the heterogeneity. By introducing a generalized mixture model framework we found 5 egment that differed in their deciion-making proce. Thi framework not only revealed thee egment but alo etimated the deciion-making proce for each egment. Thi confirm that ometime heterogeneity can be unoberved becaue it i caued by the farmer deciion-making proce itelf, or more tatitically aid, it i the regreion equation itelf that differ acro the egment. If we had ued traditional, obervable egment criteria, uch a age and farm ize, we would have concluded that the farmer were homogeneou regarding adviory ervice uage. Etimating a multiple regreion, uing the explanatory variable of the conceptual model, acro the whole ample, would have led u to conclude that the conceptual model i not able to explain farmer adviory ervice uage, ince uch a model ha an extremely low R 2. That i, the aumption of 12

16 homogeneity would have maked important driver of adviory ervice uage. Furthermore, our tatitical method allowed u to tet for heterogeneity on the bai of farmer deciion-making proce a decribed in our conceptual model, in contrat to traditional egmentation method. Methodological reearch i needed that combine the convenient propertie of the generalized mixture model and the propertie of regreion framework that take meaurement error of pychological contruct into account (cf. Penning and Leuthold). To gain more inight into farmer choice regarding adviory ervice, the marketing philoophie of both farmer and adviory ervice need defining and accurate meaurement. Thi paper ha not dientangled the contruct of market philoophy. Doing o might reveal a powerful concept, able to explain farmer choice for a particular ervice. Our finding that the marketing philoophy match i uch an important driver of farmer uage ugget that reearch that invetigate the rik-return profile of the different ervice and relating that to farmer choice for a particular ervice might be valuable, ince uch a reearch deign could tet the hypothei that a farmer choice for a particular adviory ervice i driven by the match between the rik return profile of a particular ervice and the rik-return profile of a farmer. Furthermore, Chevalier and Ellion howed that the performance of mutual fund manager could be traced back to their education level. It would be very intereting to invetigate the factor that drive the performance of adviory ervice and whether thee factor are recognized by farmer. The egment differed regarding the ue of particular market adviory ervice. The challenge i now to characterize thee ervice in term of their performance and market philoophy uch that they can be linked to the farmer in thee egment. We may hypotheize that farmer chooe a particular ervice that matche their market philoophy and that they perceive a performing well. Reearch on thee topic i underway. 13

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20 Endnote 1 Implicitly we aume that the mean and variance of price i ufficient to decribe the performance of a MAS. Thi EV model type of approach i proven to be valid when invetigating the direction of change in relevant variable (cf. Meyer and Rache; Garcia, Adam, and Hauer). 2 If we had been able to contruct cenario that conited of all three attribute (i.e. price performance, rik performance and match of market philoophy imultaneouly), Equation (1) would have been le complex: intead of two variable for rik reduction performance one would have been ufficient and the interpretation of regreion coefficient would have been eaier. A explained above, we choe the two-attribute cenario, ince a three-attribute cenario deign proved to be too complex for the farmer to handle. 3 Detailed information about thi method to improve farmer repone to mail quetionnaire can be found in Penning, Irwin and Good. 17