A Test of the Bass Model for Forecasting Adoption in a Professional Services Market. David Corkindale and Dennis List University of South Australia

Transcription

1 A Test of the Bass Model for Forecasting Adoption in a Professional Services Market David Corkindale and Dennis List University of South Australia Track 17 Continuation of the work of Ehrenberg and Bass Abstract Data was collected from market research offices in Australia in mid-1999 on their adoption of using the Internet to collect primary data. Based upon this, the Bass model was used to forecast the growth in further adoption, up to mid-2002, of their use of and web-based surveys. These forecasts were presented to the market research industry at this time. In mid data was again collected from market research offices to establish the levels of adoption of these two methods. From this the forecasting accuracy of the model could be ascertained. It was found that if the growth in the number of market research offices over the period was allowed for, the accuracy was within 1.6% for web-based survey adoption and 8.6% for surveys. The most commonly cited uses of the Bass model are for the adoption of durable goods; this study adds an application in the professional services domain. The fieldwork in 1999 also established the nature of the influence on adoption - published sources or other users and obtained measures of these. The relationship of these measures and the values of p, the coefficient of innovation and q, the coefficient of imitation, is commented upon and further research proposed. Introduction A census was conducted of market research offices in Australia in mid-1999 and then repeated in mid-2002 to ascertain the extent of the adoption by the market research industry of the use of the Internet for primary data collection. We used the Bass model ( Bass, 1969) to forecast future adoption and particularly by mid-2002 and these were presented to the Industry. From our 2002 study we are able to examine the accuracy of the 1999 forecast and this is reported upon here. In 1999 we also attempted to measure the Bass model coefficients p and q directly from respondents and this is explored and commented upon. The listings of the applications of the Bass model ( e.g. Lilien & Rangaswamy, 2003) indicate that it has been primarily used in studies of the adoption of physical goods. Our study examines its use in the adoption of a service tool by a profession. Definitions For the purposes of our study we examined the adoption of the Internet by the market research industry solely for primary research - that is, data collection from respondents via the Internet. This is subsequently referred to as 'online research' in this paper. We asked when an organisation had 'first used' the Internet for a variety of primary data collection purposes and so that is our interpretation of 'adoption'. We asked other questions about the extent of use of the internet by each organisation and so were in a position to ascertain that 'first use' was not just a temporary 'trial'. Methodology 2492

2 In mid 1999 all organisations, and major offices of these, that could be identified as primarily engaging in market research in Australia were contacted by Fax and asked if they were undertaking any primary research using the Internet. 241 were identified and 181 (75%) replied from which it was ascertained that 60 (33%) had already used the Internet for this purpose, by this time. These were sent a detailed questionnaire which 46 (77%) completed and returned. The exercise was repeated in mid 2002 using the same approach and questionnaire. This time 293 market research offices were found and 267 contacted of which 111 (46%) responded; 52 (46%) had conducted primary market research using the Internet. The detailed questionnaire was sent and 39 (79%) responded. We also asked respondents to state what had influenced them to start using the Internet for online research. We postulated two major influences: published sources and other users. We therefore asked offices which had done online research...who or what influenced the decision most: other users of online research, or published sources? There was an 'other ' option, too. Our survey asked in which year and quarter the office had first used online research. Because of the small numbers of Australian market research offices using methods other than surveys and web-based surveys, only these two sets of data are used here. Findings Use of the Bass model and theory We used the Bass model to generate likely future adoption levels for and web-based market research. For this, we used GBass software, an Excel add-in (Lilien and Rangaswany, 2003). Using the data collected in the 1999 study, applying to the previous 3.5 years quarterly data, we tried to predict usage trends 3 years ahead: to mid We set both the price elasticity and advertising elasticities to 1 (i.e. no effect). In order to establish the necessary value of m, the estimated eventual number of adopters, we looked through the 240 offices in the 1999 MRSA Directory and it was obvious that many of them were very small and far from using all existing technologies. Also, the number of offices using and web surveys had been accelerating, suggesting that the first inflection of the growth curve had not yet been reached. Thus we fitted values of m between 80 and 150 to the data and found that the values of m producing the best-fitting curves were 100 for surveys and 120 for web-based surveys. Forecast accuracy For surveys, the best-fitting values of p and q were and respectively and the model projected that 64 market research offices would have done internet surveys by mid For web-based surveys the best values were for p and 0.32 for q giving a projection that 106 market research offices will have carried out web-based surveys by mid Our forecasts were presented to the Industry at their 1999 Annual Conference. The 2002 survey found that 24 of the 39 respondents had used surveys and 36 had used web-based ones. This means that an estimate of offices using online research would be 84.5 ( i.e. (52/111)x293x(24/39)) and the number doing web-based online research would be 2493

3 126.7 ( i.e. (52/111)x293x(36/39)). As stated earlier, there were 293 market research offices in Australia in mid-2002 compared to 241 in mid-1999, a growth of 21.5%. If we scale up the original,1999 Bass model forecasts of offices having adopted and web use by 21.5% this would bring them to 77.8 and respectively. We therefore see that, if we make an allowance for the known growth in the number of potential adopters ( m ), the Bass model has underestimated survey use by 8.6% and overestimated the web-based survey use by 1.6%. If we do not allow for this then it underestimated survey adoption by 31% and web survey adoption by 24%. A possible further explanation of the underestimate of survey use may be due to the large growth in businesses having access to the Internet. In the period mid-1999 to mid-2002 the percentage of businesses using the Internet increased from 42% to 72%, a 71% increase ( ABS). This will have increased the number of employees that have considerably making surveys of employees by market research organisations more feasible and attractive than when the Internet was first available as a primary data collection tool. A further way of establishing the forecasting accuracy of the model in this case would be to use the method developed in Mahajan and Peterson (1978) that allows market potential, m to change over time. This was clearly the situation in this case. We were aware that by 1999 some firms had been started specifically to try to take advantage of the Internet as a means of collecting primary data for market research purposes. We believe that none of these responded to our request for participation. If they had they should have strictly been removed anyway as our purpose was to examine the adoption of the Internet by existing firms. The influences on the decision to carry out online research In 1999 we asked the offices who had done online research...who or what influenced the decision most: other users of online research or published sources? We found: Main influence % of offices Other users 54% Published sources 27% Both equally 12% Other ( = mostly client requests ) 7% We expected to find that the median date of first carrying out online research, for those who answered published sources to be earlier than for those who answered other users. This was on the common notion that Innovators precede Imitators. For this analysis we included client request among other users. However, we should strictly ask the question: what influenced these clients - their reading about the use of the Internet or other users/clients? For users of surveys, with a sample of 21 cases, we found that those influenced mainly by published sources had first done this type of research by a median date of the second quarter of Those influenced mainly by other users had a median start date of the third quarter of The sample size is small and the result is not significant, but the result does support what we would expect. For users of web-based surveys, the result was not quite what was expected in that, with a sample of 28, the median start date for both groups (ie. those 2494

4 influenced by published sources and by other users) was the same, being the third quarter of A hypothesis to explain this outcome is that we now know that the market was growing ( m actually increased by 21%) ; maybe those who came into the market later were more likely to be innovators than imitators. The rationale for this is that if they are new businesses they are more able to adopt new systems and techniques as they do not have the impediment to adoption of the "installed-base" effect. ( Woodside, 1996.) Estimating values of p and q The above small conflict in what we might expect in times of mean adoption raises the issue of what p and q, the so-called coefficients of innovation and imitation used in the Bass model, actually represent. Also, what is their relationship to estimates of the probability of being influenced 'by the media' ( P) or by 'socially interacting with adopters' ( Q), as can be estimated by surveys of early adopters. For our 1999 study q is of the order of 0.3 for webbased survey adoption while Q is of the order of 0.6, however, p is and P is We see that Q and q are of the same magnitude and possibly measures of the same phenomena whereas P and p appear not to be. Is this due to a scaling effect or indicative of no relationship? Tanny and Derzko (1988) have examined the interpretation of the Bass model 'process', and p and q, and suggested that earlier interpretations were erroneous. They note that the Bass model implicitly assumes that all individuals in the population are homogeneous with respect to their behaviour on the adoption decision, rather than there being two distinct sub-groups of innovators and imitators. In other words, all individuals have intrinsic innovative and imitative tendancies in some common, fixed proportion. They add: "The parameter q reflects the intensity of adoption as a result of the interaction of non-adopters with adopters. This is not the same as the inherent imitative tendancy within each individual.similarly, p represents all other forces leading to adoption. The innovative tendancy within a person is only one factor leading to adoption ( for example, the frequency of contact with a news source would be another). Thus p is not the underlying coefficient of innovation for each individual." Conventionally, p and q are either estimated from early adoption data or by comparison with the values found for the prior adoption of analogous products ( Lilien and Ragaswamy, op cit). However, is there a third possibility, that of deriving values for them by surveying early adopters and asking appropriate questions on appropriate scales? Given Tanny and Derzko's ( op cit) exposition could the range of influencing factors on values of P and Q be measured in adopting populations and the 'formula' for adjusting P and Q derived by examining the estimated values of p and q in future studies. The benefit of this could be that subsequent forecasts using the Bass model could be made sooner than having to wait for the advised twelve periods of actual sales or adoption. Conclusions and Commentary The Bass model was applied to making a forecast of the adoption of the online research by market research offices in Australia. The forecast was based on data collected in mid-1999 from a census of market research offices. A follow-up study was done in mid-2002 from which the accuracy of the original forecasts could be assessed. The forecasts were made on the adoption of primary data collection by and by web-based survey. The forecasts 2495

5 underestimated. However, when an allowance was made for the increase in the number of market research offices ( m) over the period, the forecasts were much closer, overestimating web-based adoption by 1.6% and underestimating survey use by some 8%. A marketspecific explanation was suggested for the discrepancy. This study therefore adds to the literature on the successful application of the Bass model and demonstrates its applicability to the adoption of a professional service. However, the result here does not exclude the possibility that any multinomial with three parameters could also fit the data and produce a reasonable forecast. The superiority, or otherwise, of the bass model over competing and possibly simpler models should be investigated with this data further. Our study in 1999 asked early adopters about the source of influence on their adoption whether it was published sources or the influence of other people on them. This produced a measure of the probability of being influenced by either source, in the population. These values were compared with the Bass model values of p and q. The question was raised about seeking to generate estimates of these by questioning early adopters. This is suggested as an avenue of further research. Acknowledgement The authors would like to acknowledge the constructive criticisms and suggestions of the two reviewers which have materially enhanced this paper. References Australian Bureau of Statistics, Canberra, Australia. Business Use of the Internet - ABS Report Bass, F M. A new product growth model for consumer durables. Management Science, 15, 5, pp , Lilien, G. L. and Rangaswamy, A. Marketing Engineering: Computer-Assisted Marketing Analysis and Planning. 2 nd.edition. Prentice Hall pp259. Mahajan, V. and Peterson, R. Innovation Diffusion in a Dynamic Adopter Population. Management Science, 24, , Tanny, S.M. and Derzko, N.A. Innovators and Imitators in Innovation Diffusion Modelling. Journal of Forecasting, Vol. 7, , Woodside, Arch G. Theory of Rejecting Superior, New Technologies. Journal of Business & Industrial Marketing Vol. 11. No. _ MCB University Press 2496