Computer Adoption and Use by New Mexico Nonfarm Agribusinesses

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1 Computer Adoption and Use by New Mexico Nonfarm Agribusinesses Gregory A. Baker This research explores characteristics that influence computer adoption and successful use in nonfarm New Mexico agribusinesses. Results indicate that computer adoption is related to firm characteristics such as size and type of business, but unrelated to manager characteristics, including age and education. Manager involvement in the computer purchase decision is the most important characteristic affecting computer effectiveness as measured by user satisfaction and number of applications. Key words: agribusiness management, computer adoption, multinomial logit, technological change. Advances in information technology, consistent declines in computer hardware prices, and development of application software have greatly expanded information management options available to businesses. Previous studies suggest that computer use in agriculture has lagged behind computer development (Stegelin and Novak; Batte, Jones, and Schnitkey). However, most research focuses on computer adoption by a specific type of agribusiness-farming operations. The present paper explores factors influencing adoption and successful application of computers in nonfarm agribusinesses. Factors Affecting Computer Adoption My conceptual model of computer adoption in nonfarm agribusinesses relies heavily on findings concerning farmers. Previous studies hypothesized that two types of variables influence computer use decisions: farmer and farm characteristics (Putler and Zilberman; Batte, Jones, and Schnitkey). These studies indicate that better educated and younger farmers are most likely to adopt computers. Both age and education variables are included in my nonfarm agribusiness computer adoption model. However, because decision making is typically more decentralized in nonfarm agribusinesses than on farms, age and education variables may be less important than they are on farms. Previous studies examined many farm characteristics as predictors of computer adoption, although few characteristics have proved significant. Both Batte, Jones, and Schnitkey, and Putler and Zilberman found strong relationships between farm size and computer adoption. I hypothesize that business size is also an important predictor of computer adoption by nonfarm agribusinesses. The rationale is that large farms and large agribusinesses have many similar characteristics, such as a high degree of organizational complexity, and the need to keep extensive, detailed records. Previous studies reported conflicting results concerning effect of firm type on computer adoption. Batte, Jones, and Schnitkey found some types of farms are more likely than others to adopt computers. Although Putler and Zilberman did not find type of enterprise to be important, they did find that the farm's nonfarm business activities were a predictor of computer adoption. It seems reasonable that type of business activity should be an important predictor of computer adoption in nonfarm agribusinesses. These businesses are involved in a much wider range of business activities than are farms, and some activities may benefit relatively more than others from computerization. The author is an assistant professor, Institute of Agribusiness. Santa Clara University. The author acknowledges the helpful comments and suggestions of two anonymous journal reviewers. Review coordinated by Steven Buccola. Factors Affecting Computer System Effectiveness Researchers typically employ two types of measures to evaluate computer effectiveness. Sand- Copyright 1992 American Agricultural Economics Association

2 738 August 1992 ers and Courtney, Singleton, McLean, and Altman, and Zmud rely upon user satisfaction to evaluate computer system performance. The alternative measure, system usage, is less subjective. Lucas defines system usage as amount of time spent on the system or utilization level of specific functions. For example, Robey measured system usage by salespeople as the number of customer records maintained per account. Vanlommel and De Brabander assess system usage by calculating an index measuring number of applications, such as job orders or sales statistics. Those favoring system usage as a measure of computer effectiveness argue that user satisfaction measures are too subjective. A specific criticism is that a manager's perception of computer effectiveness may be biased, particularly if the manager was instrumental in adopting the computer. In practice, several studies have found a high degree of correlation between user satisfaction and system usage measures of computer effectiveness (Lucas; Robey). The present models employ both measures. I evaluated system performance by soliciting managers' opinions as to whether computer use increased firm profitability. In measuring system usage, I followed Vanlommel and De Brabander's method of determining the number of computer applications. Studies of computer system effectiveness do not typically examine the relationship between managerial characteristics and system success. They focus instead on business characteristics and management practices. However, Batte, Jones, and Schnitkey studied the effect of both farmer and farm characteristics on system effectiveness. They found farmer's age and farm type are important in predicting computer usefulness. Farmer's age and level of education were also important in predicting number of applications. Stegelin and Novak study computer satisfaction in agribusinesses but do not attempt to relate computer satisfaction to manager or business characteristics. All explanatory variables in the present model of computer adoption (manager's age and education, and firm size and type) are included also in the models of computer effecti veness. Management information system literature suggests that successful computer performance depends upon several factors. There is widespread agreement that support of a firm's chief decision maker is critical to computer system success (Alavi; Benson; Couger and Wergin; DeLone 1988; Garrity; Raymond; Raysman). Several studies also link system performance with Amer. J. Agr. Econ. computer expertise available in the firm (Bourke; Cerullo; Couger and Wergin; Mykytyn). Computer-related training is important to computer system effectiveness (Fuerst and Cheney; Kasper and Cerveny). Finally, several studies examined the relationship between system performance and the organization's experience with computers (DeLone 1988; Kasper and Cerveny; Raymond; Sanders and Courtney). Variables in my own models of computer effectiveness include manager involvement in computer purchasing decisions, presence of a computer specialist, and years of computer use. Survey Design and Methodology Sources including the New Mexico Department of Agriculture, the Cooperative Extension Service, and trade associations assisted in identifying a total of 808 New Mexico nonfarm agribusinesses. I randomly chose 300 firms to participate in the study. A telephone survey was conducted in late 1987 and early 1988, using a pretested questionnaire. The response rate was 62%. Respondents provided the following information: whether the firm used computers, years of computer use, manager's age and education, firm size (gross sales), presence of a computer specialist, manager's involvement in the computer purchase decision, whether the manager thought computer use increased profits, number and type of computer applications, and primary business activity. Because many businesses are reluctant to provide financial information, the survey asked respondents to indicate a range corresponding to their firms' sales. Choices were $0 to $19,999, $20,000 to $49,999, $50,000 to $99,999, $100,000 to $499,999, and $500,000 and over. Firms were classified as belonging to one of four categories: providers of services, such as pesticide applicators, farm management firms, or veterinarians; processors, including meat packers, dairies, and fruit and vegetable processors; farm supply and retail businesses, including sellers of agricultural chemicals, seed, and feed, and retail nurseries; and firms whose primary business was sales or distribution, including brokers and fruit or vegetable packers. Because the dependent variables (computer adoption and satisfaction) are dichotomous, a logit model was used to estimate the effect of independent variables on dependent variables. In the case of computer adoption, the model predicted the probability (Pi) that the ith agri-

3 Baker business will adopt computers. The logit model (Judge et al.; Pindyck and Rubinfeld) is given by (1) Pi = 1/[1 + exp(-xil3)]' where (2) xj3 = f30 + f3i AGEi + f32 UGi + f33gri + f34sizei + f3sproc j + f3ffteti + f37disti; subscript i is the ith observation; Pi is the probability the ith individual will fall into a certain dependent variable class given Xi; AGE i is age in years of the ith manager; UG indicates whether the highest level of education is an undergraduate degree (1 if degree, 0 if otherwise); GR indicates a graduate degree (1 if graduate degree, o if otherwise); SIZE is total firm sales (1 if $100,000 or greater, 0 if $0 to $99,999); PROC indicates a processing firm (1 if yes, 0 if no); RET indicates a farm supply or retail business (1 if yes, 0 if no); DIST indicates a broker, packer, or shipper (1 if yes, 0 if no); and f3 is the vector of estimated coefficients. The following multinomial logit model was specified to predict the number-of-applications group for each firm: (3) Pij/Pik = 1/[1 + exp(-xif3jk)), where xj3jk is given by (4) xj3jk = f30jk + f31jkagei + f3jkugi + f33jkgri + f34ik SIZEi + f3sjkproci + f36jk RETi + {37jk D1STi' + {38jk MANG i, + f39jkyearsh + {3lOjkSPECi; subscript i is the ith observation, j the jth dependent variable class, k the kth dependent variable class, and Pij/P ikthe probability that the ith agribusiness will fall into the jth number-of-applications category relative to the kth numberof-applications category. MANG indicates the manager's involvement in the decision to computerize (l if yes, 0 if no), YEARS indicates years of computer use, and SPEC indicates presence of a computer specialist (1 if yes, 0 if no). The logit and multinomial logit models were estimated with the maximum likelihood method. Computer Adoption Of the agribusinesses surveyed, 44% use computers. Managers of firms with computers are slightly younger than managers of nonadopting Computer Adoption and Use 739 firms: 41 versus 44 years, respectively (table 1). Managers of adopting firms are also better educated on average. In adopting firms, 77% of the managers have at least an undergraduate degree, compared to only 60% of managers in nonadopting firms. We see the most striking differences when firms are classified by size and principal business activity. Eighty-five percent of adopting firms have at least $100,000 in gross sales, compared to 48% of nonadopting firms. Service and processing firms are more likely to be nonadopters than adopters of computer technology, while farm supply and retail firms, and sales and distribution firms are more likely than not to have adopted computers. The several goodness-of-fit measures indicate that the logit model performed well (table 2). The likelihood ratio test indicates the model was significant at the 0.01 level and the McFadden R 2 is acceptably high at Percentage of correct predictions is also acceptably high for both groups, with 81% of nonadopters and 73% of adopters being correctly predicted for an overall success rate of 77%. Analysis indicates that likelihood of computer adoption is positively related to firm size. Also, farm supply/retail businesses and distribution firms are more likely to use computers than are service firms (the base group for the analysis, because no indicator variable was present for this variable). A correlation analysis was run to determine whether business size and type interactions influenced results. The Pearson correlation coefficients are low, the strongest correlation being a negative one between service firms and business size (-0.22). My finding of a strong relationship between firm size and computer adoption is consistent with results of several studies of both farm and nonfarm businesses (DeLone 1981; Batte, Jones, and Schnitkey; Putler and Zilberman). This is not surprising, because many of the same factors affect both types of firms. For example, smaller operations may have tighter financial constraints and lack the expertise (or resources necessary to acquire expertise) for developing and operating a computerized information system (Markland). Moreover, larger firms may benefit more from computerization as the result of factors such as increased organizational complexity and increased need for coordination. An important difference in computer adoption by nonfarm agribusinesses concerns the influence of manager characteristics. Contrary to previous farm studies (Batte, Jones, and Schnitkey; Putler and Zilberman), the present one shows

4 740 August 1992 Amer. J. Agr. Econ. Table 1. Characteristics of Nonfarm Agribusiness Computer Adopters and Nonadopters Agribusinesses that had Agribusinesses that had not All agribusinesses adopted computers adopted computers Manager age (years) Highest level of education (%) High school Undergraduate degree Graduate degree II 17 7 Primary business activity (%) Service Processing Farm supply/retail Sales / distribution Size (gross sales, %) $0-$99, $100,000 and over Table 2. Variable Constant AGE UG GR SIZE PROC RET DIST Logit Model of Computer Adoption by Nonfarm Agribusinesses McFadden R Log likelihood Log likelihood, restricted Model chi-square 48.42*** Correct Predictions (%) Total: Adopters: Nonadopters: Asymptotic Probability Coefficient t-ratio effect" ***b *** ** a The probability effect is calculated with all variables at their mean values. For continuous variables it is the derivative of the function with respect to that variable; for discrete variables it is the change in probability resulting from that variable. b Double asterisk indicates significant at the 5% level of probability, triple asterisk indicates significant at the I% level of probability. neither age nor education variables to be significant at the 0.10 level. However, presence of a graduate degree was significant at the O.12 level, suggesting an increased probability of computer adoption. Batte, Jones, and Schnitkey argue that education influences the rate of computer adoption because greater education increases demand for information. They also argue that older farmers have a shorter time to recapture learning costs associated with computerization and are therefore less likely to adopt computers. Because farm operators are often the owners of their farms and possibly the only management personnel, they are likely to have a very large impact on the firm through the decisions they make. Because decision making in many nonfarm agribusinesses is much more decentralized than on farms, manager characteristics probably have less impact on management decisions about computer utilization. For example, managers often feel uncomfortable with or do not know how to operate a computer, but their firms use computers because they have employees with the necessary expertise. Coefficients on the DIST and RET variables were both significant, indicating that agribusinesses involved in sales and distribution, and farm supply and retail businesses, are more likely to

5 Baker Computer Adoption and Use 741 adopt computers than are agribusinesses that primarily provide services. Agribusinesses in the DIST and RET categories typically process a large number of transactions and therefore may more readily benefit from computerization. The PROC coefficient is not significant. Computer Usefulness Managers of adopting firms also evaluated a computer's usefulness. Specifically, they were asked if computer use increased profits, and 74% of computer adopters indicated increased profits. Results indicate the computer usefulness model performed acceptably well (table 3). Model chi-square was significant at the 0.10 level, McFadden R 2 was 0.23, and the percentage of correct predictions was over 80. The only variable significant at the 0.10 level was MANG. Its coefficient is positive, indicating that managers who participated in decisions to computerize operations are more likely to believe that computer use increases profits than are those who did not participate in such decisions. A common rationale for this finding is that without involvement of the chief decision maker, the computer is likely to be used primarily for such tasks as transaction processing, record keeping, and word processing, as opposed to supporting managerial decision making. In other words, the computer is perceived as not having a substantial impact on firm performance. Table 3. Variable No other variables were statistically significant at the 0.10 level, although the YEARS variable was significant at the 0.11 level. This suggests that longer computer use results in lower effectiveness. Raymond also found an inverse relationship between organizational experience with computers and user satisfaction. He hypothesized that firms that had recently computerized more readily benefited from "state-of-theart" equipment and applications and therefore found their computer systems to be more effective. The lack of statistical significance of variables influencing computer adoption is consistent with previous findings that factors predicting computer adoption do not influence computer success (Batte, Jones, & Schnitkey). It is particularly significant that manager and firm characteristics which influence computer adoption do not affect successful use of computer systems. Computer Applications Managers of adopting firms specified types of applications used, including accounting records, billing and invoicing, inventory control and purchasing, production decision making, financial decisions, marketing decisions, and word processing. Correlation between application types was low. The majority (64.3%) of the Pearson correlation coefficients were below 0.30 in absolute value and only one coefficient was above Logit Model of Computer Usefulness by Nonfarm Agribusinesses Coefficient Constant AGE UG GR SIZE PROC RET DIST MANG YEARS SPEC McFadden R Log likelihood Log likelihood, restricted Model chi-square 16.94* Correct prediction (%) Total: Useful: Not useful: Asymptotic t-ratio *b Probability effect" a The probability effect is calculated with all variables at their mean values. For continuous variables it is the derivative of the function with respect to that variable; for discrete variables it is the change in probability resulting from that variable. b Single asterisk indicates significant at the 10% level of probability.

6 Table 4. Multinomial Logit Model of Number of Computer Applications by Nonfarm Agribusinesses Variable Coefficient In(Pl/PO)" Asymptotic t-ratio In(P2/Pl) In(P2/PO) Asymptotic Asymptotic Coefficient r-ratio Coefficient z-ratio Constant AGE UG GR SIZE PROC RET DIST MANG YEARS SPEC * * *b ** McFadden R 2 Log likelihood Log likelihood, restricted Model chi-square *** Probability effects' AGE UG GR SIZE PROC RET DIST MANG YEARS SPEC a Number of application groups are: 0 = 0 to 3 applications, I = 4 or 5 applications, and 2 = 6 to 8 applications. b Single asterisk indicates significant at the 10% level of probability, double asterisk indicates significant at the 5% level of probability, triple asterisk indicates significant at the I% level of probability. C The probability effect is calculated with all variables at their mean values. For continuous variables it is the derivative of the function with respect to that variable; for discrete variables it is the change in probability resulting from that variable. -..J.j::. N -= ::::;. -= \0 -- ;l:. :" ;l:. C>o:l :" t'r, a

7 Baker The highest Pearson correlation coefficient was 0.41, namely for the production and financial decision making application types. Correlation between number of computer applications and computer system satisfaction variables was This compares with a correlation coefficient of 0.36 obtained between Robey's measures of system performance and system nsaze. The system usage model compares the logarithm of the probabilities of using different numbers of applications. It performed reasonably well, with a model chi-square significant at the 0.0 I level and a McFadden R 2 of 0.21 (table 4). The first column of table 4 is used to determine the probability that the agribusiness will use 4 or 5 applications versus 0 to 3 applications. The second set of coefficients compares the probability of using 6 to 8 applications with that of using 0 to 3 applications, and the third set of coefficients compares the probability of using 6 to 8 applications with that of using 4 or 5. Agribusinesses in which the manager is involved in the decision to adopt computers tend to use a greater number of applications. Specifically, when the manager is involved in the purchase decision, the firm is more likely to use either 4 or 5 applications than 0 to 3 applications, although it is not more likely to use 6 to 8 applications than either 0 to 3, or 4 or 5 applications. This result is consistent with the finding that managerial involvement in computer system development is important to computer system satisfaction. The YEARS variable was also significant in two of three cases, although results are contradictory. Firms employing computers for a greater number of years are more likely to use 4 or 5 applications than 0 to 3 applications. However, they are less likely to use 6 to 8 applications than 4 or 5 applications. Surprisingly, greater computer experience may lead to reduced computer system effectiveness. Conclusions This study indicates important differences between farm and nonfarm agribusinesses in factors affecting computer adoption. Manager characteristics are less important in explaining computer adoption in nonfarm agribusinesses than on farms. Previous studies found strong relationships between the farmer's age and educa- Computer Adoption and Use 743 tion and rate of computer adoption. In nonfarm agribusinesses, age is unrelated to computer adoption and only a weak relationship exists between manager's education and computer adoption rate. Larger agribusinesses, and farm supply /retail and distribution firms, are most likely to utilize computers. Manager involvement in computer purchase is the only factor positively influencing both measures of computer system effectiveness, that is, satisfaction and usage. Firms in which the manager participates in computer technology choice are more likely to use computers for a greater number of applications. Finally, situational variables are not important determinants of computer system effectiveness. Success is determined primarily by factors under management's control, the chief of which is the manager's involvement itself. References Alavi, M. "An Assessment of the Concept of Decision Support Systems as Viewed by Senior-Level Executives." MIS Quart. 6(1982): 1-9. Batte, M., E. Jones, and G. D. Schnitkey. "Computer Use by Ohio Commercial Farmers." Amer. J. Agr. Econ. 72( 1990): Benson, D. H. "A Field Study of End User Computing: Findings and Issues." MIS Quart. 7(1983): Bourke, R. "Manufacturing Systems Scene Improving." Small Systems World 7(1979):20-24, 54. Cerullo, M. J. "Information Systems Success Factors." J. Systems Manag. 31(1980): Couger, J. D., and L. M. Wergin. "Systems Management: Small Company MIS." Infosystems 21(1974): DeLone, W. H. "Firm Size and the Characteristics of Computer Use." MIS Quart. 5(1981): Determinantsof Success for Computer Usage in Small Business." MIS Quart. 12(1988): Fuerst, W. L., and P. H. Cheney. "Factors Affecting the Perceived Utilization of Computer-Based Decision Support Systems in the Oil Industry." Decision Sciences 13(1982): Garrity, J. T. "Top Management and Computer Profits." Harv. Bus. Rev. 41(1963):6-12, 174. Judge, G. G., W. E. Griffiths, R. C. Hill, H. Lutkepohl, and T. Lee. The Theory and Practice ofeconometrics, 2nd ed. New York: John Wiley & Sons, Kasper, G. M., and R. P. Cerveny. "A Laboratory Study of User Characteristics and Decision-Making Performance in End-User Computing." Information and Management 9(1985): Lucas, H. c., Jr. "Systems Quality, User Reactions and the Use of Information Systems." Management Informatics 3(1974):

8 744 August 1992 Markland, R. E. "The Role of the Computer in Small Business Management." 1. Small Bus. Manag. 12(1974): Mykytyn, P. P. "An Empirical Investigation of DSS Usage and the User's Perception of DSS Training." Information and Management 14(1988):9-17. Pindyck, R. S., and Daniel L. Rubinfe1d. Econometric Methods and Economic Forecasts, 2nd ed. New York: McGraw-Hill Book Co Putler, D. S., and D. Zi1berman. "Computer Use in Agriculture: Evidence from Tulare County, California." Amer. J. Agr. Econ. 70(1988) Raymond, L. "Organizational Characteristics and MIS Success in the Context of Small Business." MIS Quart. 9(1985): Raysman, R. "Manager Involvement Needed in Computer Selection." Harv. Bus. Rev. 59(1981): Robey, D. "User Attitudes and Management Information Amer. 1. Agr. Econ. System Use." Academy of Management J. 22(1979): Sanders, G. L., and J. F. Courtney. "A Field Study of Organizational Factors Influencing DSS Success." MIS Quart. 9(1985): Singleton, J. P., E. R. McLean, and E. N. Altman. Measuring Information Systems" Performance: Experience with the Management by Results System at Security Pacific Bank. MIS Quart. 12(1988): Stegelin, F. E., and J. L. Novak. "Attitudes of Agribusiness toward Microcomputers. Agribusiness: An International Journal. 2(1986): Van1ommel, E., and B. De Brabander. "The Organization of Electronic Data Processing (EDP) Activities and Computer Use." J. Business 48(1975): Zmud, R. W. "Individual Differences and MIS Success: A Review of the Empirical Literature. " Management Science 25( 1979):