Formato de Perfil Profesional por Competencias de la

While many agribusiness decision-makers agree that word-of-mouth marketing is important for the marketing of products and services there is a need to evaluate the effectiveness of facilitated word-of-mouth marketing quantitatively. The results of the quantitative analysis will be useful for the company that facilitates word-of-mouth

marketing and the clients that use the word-of-mouth marketing sources. In particular the factors that impact (in a statistically significant manner) the effectiveness of word-of- mouth are identified.

In the remainder of this section, four measures of effectiveness of word-of-mouth marketing are described followed by definitions of the factors that influence

effectiveness. The following section of this chapter contains a discussion of Binary Logit analysis including the justification for using this approach, and a detailed description of the dependent and independent variables. The empirical results are presented in the third and fourth section of this chapter with the insecticide program first followed by the animal health program. The final section of this chapter contains a summary.

The four measures of effectiveness of word-of-mouth marketing used in this analysis are: the participant recalls participating in the program (RECALL), the participant would participate in program again (REPEAT), the participant would recommend participation in program to others (RECOMMEND), and the information level gained from the word-of-mouth programs (INFORMATION). The factors that are

hypothesized to influence effectiveness are: size of business operation, experience, adoption levels, growth plans for the business, and the ideas of information source and information seeker. Two separate groups of results are discussed. The first group of results is from the insecticide program survey, and includes the farmers as respondents. The second group of results deals with the animal health program survey, and includes animal health professionals as respondents. Binary Logit analysis, which will be used to evaluate statistical significance, is described in the following section.

Overview of Binary Logit Analysis

Binary logit analysis is used to evaluate which factors (independent variables) influence the effectiveness of word-of-mouth marketing (dependent variables) in a statistically significant manner. The binary logit analysis is appropriate because the effectiveness measures are coded as binary variables (0 or 1).

Since the same dependent variables are used in both the insecticide and animal health product surveys the following discussion of binary logit analysis applies for both programs. The insecticide program resulted in 67 useable observations, while the animal health product program resulted in 77 useable observations from the telephone survey.

Measures of the Effectiveness of Word-of-Mouth Marketing

The four measures of the effectiveness of word-of-mouth marketing are based on the participants’ memory from the word-of-mouth teleconference and reported during the follow-up telephone survey. The same four measures of effectiveness are used to

dependent variables in this analysis. The first effectiveness measure, RECALL, is measured by whether the participants recalled participating in the specific program. The RECALL variable was assigned a value of 1 if the participant recalled having

participated in the program and 0 otherwise.

If the individual did not recall participating in the program then the survey was complete with the remaining conference related questions being voided. However, if the individual recalled participating in the program then the additional survey questions were asked.

The second effectiveness measure, REPEAT, is measured by whether the

individual would participate in another such facilitated word-of-mouth program again in the future. The question dealing with the program participant’s willingness to participate in another such program was measured with another yes/no response. The REPEAT variable was assigned a 1 if the participant would be willing to participate in another word-of-mouth program and 0 otherwise.

The third effectiveness measure, RECOMMEND, was used to measure the participant’s recommendation of the word-of-mouth program to their colleagues and peers. This variable aided in determining the diffusion networks of the programs themselves and not the product discussed in the teleconference. Also this variable will assist in measuring the effectiveness of word-of-mouth marketing. For recommendation of the program, the question used a yes/no response. The RECOMMEND variable was assigned a 1 if the participant recommended the word-of-mouth program to another colleague and 0 otherwise.

The final effectiveness measure, INFORMATION, is measured by the amount of information gained by the participant from the word-of-mouth program. Participants were asked to recall the amount of information received from the program and rate according to the following choices: no information, a little information, and a great deal of information. As mentioned earlier to continue with the binary logit analysis these three choices were placed into only two categories with none and a little information being combined and a great deal of information being placed into one category. This categorization was necessary due to the small number of observation in the little and no information categories. The INFORMATION variable was assigned a value of 1 if the participants gained a great deal of information or a value of 0 if the participant gained very little, if any, information.

Independent Variables or Factors

Now that the measures of effectiveness have been defined the specific factors (independent variables) that affect the measures of effectiveness will be explored. Different factors (independent variables) are used in the analysis for the insecticide program and the animal health program. The independent variables used with the insecticide program empirical analysis are: corn acres, operation’s growth plans, respondent as information source, respondent as information seeker, adoption rates, and age levels. Table 4.1 gives a brief explanation of these variables.

Table 4.1 Explanations of Independent Variables for Insecticide Survey. Independent Variable Explanation of Variable/Factor

CORNACRES Acres of Corn planted in 2001

GROWPLANC Maintain current farming operation size in the next 5 years GROWPLANE Expand the current farming operation in the next 5 years GROWPLANS Shrink the current farming operation size in the next 5 years

(reference)

ADOPT1 Innovators

ADOPT2 Early adopters

ADOPT3 Early majority

ADOPT4 Late majority and laggards (reference)

LOOKFR Look to other farmers for information frequently

LOOKLI Look to other farmers for information a little, if any (reference) FARMUFR Other farmers look to respondent for information frequently FARMLI Other farmers look to respondent for information a little, if any

(reference)

AGE1 Ages under 45 (reference)

AGE2 Ages 45-54

AGE3 Ages 55-64

AGE4 Ages over 65

The size of the farming operation is measured by the CORNACRES variable, which uses the actual number of corn acres the participants reported in 2001. The CORNACRES variable is a continuous variable.

To measure the experience level of the participants in the word-of-mouth teleconference the age of the respondents was gathered. The data for the age variable was collected by asking respondents to indicate the age range they belonged to. These ranges are noted in the table 4.1. In the logit analysis the age variables entered the analysis as dummy variables. If the participant fell into a given age range the variable was assigned a value of 1 and 0 otherwise. The AGE1 variable (under 45) was the dummy variable left out of the analysis and therefore is the reference group in the logit analysis.

The ADOPT variables measure the actual adoption patterns of the participants in the insecticide word-of-mouth program. The data for the ADOPT variable was collected by asking respondents to indicate what adoption category they belonged to. The series of statements were designed to avoid biasing the respondent’s answer. In the logit analysis the ADOPT variables entered the analysis as dummy variables. If the participant fell into a given adoption level the variable was assigned a 1 and 0 otherwise. The ADOPT4 variable (late majority and laggards) was the dummy variable left out of the analysis and is utilized as the reference group in the logit analysis.

The GROW variables reflect the growth plans of the farming operation for the next five years. The growth plans of the farming operations were collected by asking respondents to indicate the operation’s growth plans over the next five years. In the logit analysis the growth plan variables entered the analysis as dummy variables. If the participant selected a given growth plan the variable was assigned a 1 and 0 otherwise. The variable dealing with shrinking of the operation (GROWS) was the dummy variable left out of the analysis and therefore serves as the reference group for the logit analysis.

The information seeker idea is concerned with where farmers receive information. The information seeker variables (LOOK) measure the number of times the farmer

consults his/her colleagues for information. The LOOK variable data was collected by asking respondents the frequency that they look to other individuals for information. The ranges of none, sometimes, and frequently were used to determine frequency. In the logit analysis the information seeker variables (LOOK) entered the analysis as dummy

variables. If the respondent was in a certain information seeker group (i.e. respondent looks to colleagues frequently) the variable was assigned a 1 and 0 otherwise. The

LOOKLI variable (look to colleagues a little if any) was the dummy variable left out of the analysis and therefore is the reference group in the logit analysis.

The information source idea is concerned with the frequency that other farmers come to the respondent for information and is measured with the FARM variables. Data for the FARM variable was collected by having the respondents indicate the frequency that other farmers come to them for information using the ranges of none, sometimes, and frequently. In the logit analysis the information source variables entered the analysis as dummy variables. For example, if the participant indicated that other farmers come to him/her frequently (FARMFR) the FARMFR variable was assigned a value of 1 and 0 otherwise. The FARMLI variable (colleagues look to respondent a little if any) was the dummy variable left out of the analysis and serves as the reference group.

The independent variables for the animal health product program empirical analysis were: practice size, practice’s growth plans, respondent as information seeker, respondent as information source, adoption rates, and experience levels. Table 4.2 gives a brief explanation of the variables used in the analysis for the animal health professional portion of this study.

Table 4.2 Explanations of Independent Variables for Animal Health Survey. Independent Variable Explanation

PSIZEL Large practice consisting of 3 or more veterinarians

PSIZEM Small & Medium practice consisting of 2 or less veterinarians (reference)

ADOPT1 Innovator

ADOPT2 Early adopter

ADOPT3 Early majority

ADOPT4 Late majority and laggards (reference)

LOTOCOFR Respondent looks to colleagues for information frequently LOTOCOLIN Respondents look to colleagues for information a little if any

(reference)

YEEXP1 1-20 years (reference)

YEEXP2 21-30 years

YEEXP3 31-40 years

YEEXP4 Over 40 years

GROWCO Maintain current practice size for the next 5 years GROWEX Expand current practice size for the next 5 years

GROWS Shrink current practice size in the next 5 years (reference) COLOYOLIN Colleagues look to respondent for information a little if any

(reference)

COLOYOFR Colleagues look to respondent for information frequently

The size of the animal health practice is measured by PSIZE. PSIZE is a categorical variable delineated by the number of veterinarians in the practice. Large practices have three or more veterinarians while small practices have two or fewer. In the logit analysis practice size entered the analysis as a dummy variable. PSIZEL was

assigned a value of one if the practice had three or more veterinarians and zero otherwise. The PSIZEM variable (2 or fewer veterinarians) was the dummy variable left out of the analysis and therefore is the reference group in the logit analysis.

To measure the experience level of the participants in the word-of-mouth teleconference the number of years as an animal health provider was collected. The YEEXP variable was a range for the respondents to complete. In the logit analysis the

years of experience variables entered the analysis as dummy variables. If the participant had a certain experience level the variable was assigned a value of one and zero

otherwise. The YEEXP1 variable (1-20 years) was the dummy variable left out of the analysis and therefore the reference group in the logit analysis.

The ADOPT variables measured the actual adoption patterns of the participants. The adoption levels were collected by presenting respondents with a series of responses to select what best matches their adoption patterns. These responses were designed to determine adoption characteristics without biasing the results. In the logit analysis the adoption levels entered the analysis as dummy variables. If the respondent was

associated with the early majority (ADOPT3) adoption level the variable was assigned a value of 1 and 0 otherwise. The ADOPT4 variable (late majority and laggards) was the dummy variable left out of the analysis and therefore is the reference group in the logit analysis.

The GROW variables reflect the growth plans of the animal health practice for the next five years. The growth plans were collected by asking each respondent for the growth plans of his/her operation over the next five years. In the logit analysis the growth plans entered the analysis as dummy variables. If the respondent held a specific growth plan the variable was assigned a value of 1 and 0 otherwise. The GROWS variable (shrinking of the practice) was left out of the analysis and therefore is the reference group for the logit analysis.

The information seeker was studied through the LOTOCO variables. The data for the information seeker variable was collected by asking respondents the frequency that they look to others for information. The LOTOCO variable was based on the ranges of

none, sometimes, and frequently. In the logit analysis the information seeker variables entered the analysis as dummy variables. LOTOCOFR was assigned a value of one if the participant looked to colleagues frequently for product information and zero otherwise. The LOTOCOLIN variable (look to colleagues a little if any) was the dummy variable left out of the analysis and the reference group for the logit analysis.

The information source idea was measured through the COLOTOYO variables, which measures the frequency that other animal health professionals come to the

respondent for information. The data for information source was based on the ranges of none, sometimes, and frequently. In the logit analysis the information source variable entered the analysis as dummy variables. If the participant was in a specific information source range the variable was assigned a 1 and 0 otherwise. The COLOTOYOLI

variable was the dummy variable left out of the analysis and serves as the reference group in the logit analysis.

Insecticide Program Logit Analysis

Four measures of effectiveness of the insecticide program are evaluated using logit analysis and presented in tables 4.3 to 4.7. As stated earlier these four measures of effectiveness (dependent variables) are: the participant recalls participating in the program (RECALL), the participant would participate in program again (REPEAT), the participant would recommend participation in program to others (RECOMMEND), and the information level gained from the word-of-mouth programs (INFORMATION).

Recall Participating in Insecticide Program

The results from five separate logit models concerning respondents’ ability to recall participating in the program are found in table 4.3. These models deal only with the insecticide program. The five separate logit models correctly predict at least 81% of the total outcomes. The chi-squared values of 10.064, 10.974, 15.443, 19.36, and 9.927 indicate that the set of coefficients, as a group, for each model are statistically significant. These five separate models are comprised of different combinations of independent variables.

Table 4.3 Logit Analyses of Factors Affecting the Recall of Participation in the Insecticide Teleconference Program.

Variables Model 1 Model 2 Model 3 Model 4 Model 5 -2.634* -1.234 -3.308 -3.71 -0.969 CONSTANT [-1.679] [-.715] [-1.497] [-1.523] [-.923] 0.002 0.002 0.002 0.142 - CORNACRES [1.597] [1.099] [0.985] [.885] - 2.501** - 2.812* 2.398 1.55 GROWPLANC [2.186] - [1.864] [1.338] [1.306] 2.357** - 2.547* 2.469 2.063* GROWPLANE [2.26] - [1.78] [1.368] [1.704] - 0.409 0.138 0.298 - LOOKFR - [.538] [0.173] [.322] - - 2.441** 2.641** 2.375** - FARMUFR - [2.189] [2.17] [2.002] - - - - 2.009 2.336* AGE2 - - - [1.572] [1.817] - - - 1.303 1.124 AGE3 - - - [1.175] [1.037] - - - -0.233 0.074 AGE4 - - - [-.167] [.070] % Correctly Predicted 85.00% 81.36% 86.44% 86.21% 86.44% Chi-Squared 10.064*** 10.974** 15.443*** 19.36** 9.927* Degrees of Freedom 3 3 5 8 5

T-Values are reported in brackets

*Represents a statistical significance at α=0.1 to 0.05 **Represents a statistical significance at α=0.05 to 0.01 ***Represents a statistical significance at α=0.01 or below

In Model 1, the variables that were statistically significant are the growth plans of the operation (GROWPLANC and GROWPLANE). Corn acreage (CORNACRES) was not statistically significant in this model. As expected the coefficient for CORNACRES was positive, but due to the lack of statistical significance no concrete conclusions could be drawn concerning CORNACRES effects on participant’s recall of the program. The

positive coefficients on the GROWPLANC and GROWPLANE variables indicate that the farmers that are thriving, or maintaining the operation, recall participating in the program better than those operations that are shrinking or plan to be out of business in five years.

Model 2 incorporates the variables concerning information sources. The positive and statistically significant coefficient on FARMUFR (other farmers come to respondent frequently for information) indicates that farmers who view themselves as information sources are more likely to recall participating in the program. The remaining variables: respondent looks to other farmers for information frequently (LOOKFR) and number of corn acres (CORNACRES) do have positive coefficients, but are not statistically

significant.

Model 3 includes all of the variables included in both Models 1 and 2. Once again the growth plan variables (GROWPLANC, GROWPLANE) and farmer as

information source (FARMUFR) are statistically significant and positive. In this model CORNACRES has a positive coefficient but is not statistically significant.

Model 4 includes age along with the variables included in Model 3. The variables CORNACRES, GROWPLANC, GROWPLANE, LOOKFR, AGE2, and AGE3, are not statistically significant with positive coefficients. Due to the lack of statistical

significance no further conclusions could be drawn concerning the effects of these factors on RECALL.

The variables that are statistically significant in Model 5 are FARMUFR, GROWPLANE, and AGE2. All of the remaining coefficients are positive, but not statistically significant. In summary, the variables that have a direct impact on the

participant’s recall of the program are: the respondent seeing himself/herself as an information source, the age range of 45-54, and the expansion or maintaining the current operation’s size.

Participate in Another Facilitated Word-of-Mouth Marketing Program

The participants’ willingness to participate in another facilitated word-of-mouth teleconference program is the next effectiveness measure considered. The dependent variable in the logit results reported in Table 4.4 equals one if the respondent would be willing to participate in another facilitated word-of-mouth program and zero otherwise. For this analysis three separate logit models are considered. All three of the models predicted the total outcome at least 69% of the time. This group of models does not predict as well as the previous model set. The chi-squared values of 3.616, 8.949, and 4.926 indicate that the explanatory variables, as a group, are not statistically significant for any of the models.

Table 4.4 Logit Analyses of Factors Affecting the Respondent’s Willingness to Participate in a Teleconference Program Again.

Variables Model 1 Model 2 Model 3 1.178 29.81 28.459 CONSTANT [.934] [0.00] [.000]